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classification</title> <meta name="description" content="Search results for: image classification"> <meta name="keywords" content="image classification"> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> <nav class="navbar navbar-expand-lg navbar-light"> <a class="navbar-brand" href="https://waset.org"> <img src="https://cdn.waset.org/static/images/wasetc.png" alt="Open Science Research Excellence" title="Open Science Research Excellence" /> </a> <button class="d-block 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action="https://publications.waset.org/abstracts/search"> <div id="custom-search-input"> <div class="input-group"> <i class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value="image classification"> <input type="submit" class="btn_search" value="Search"> </div> </div> </form> </div> </div> <div class="row mt-3"> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Commenced</strong> in January 2007</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Frequency:</strong> Monthly</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Edition:</strong> International</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Paper Count:</strong> 4557</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: image classification</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4437</span> Mapping Forest Biodiversity Using Remote Sensing and Field Data in the National Park of Tlemcen (Algeria)</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bencherif%20Kada">Bencherif Kada</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In forest management practice, landscape and Mediterranean forest are never posed as linked objects. But sustainable forestry requires the valorization of the forest landscape and this aim involves assessing the spatial distribution of biodiversity by mapping forest landscaped units and subunits and by monitoring the environmental trends. This contribution aims to highlight, through object-oriented classifications, the landscaped biodiversity of the National Park of Tlemcen (Algeria). The methodology used is based on ground data and on the basic processing units of object-oriented classification that are segments, so-called image-objects, representing a relatively homogenous units on the ground. The classification of Landsat Enhanced Thematic Mapper plus (ETM+) imagery is performed on image objects, and not on pixels. Advantages of object-oriented classification are to make full use of meaningful statistic and texture calculation, uncorrelated shape information (e.g., length-to-width ratio, direction and area of an object, etc.) and topological features (neighbor, super-object, etc.), and the close relation between real-world objects and image objects. The results show that per object classification using the k-nearest neighbor’s method is more efficient than per pixel one. It permits to simplify the content of the image while preserving spectrally and spatially homogeneous types of land covers such as Aleppo pine stands, cork oak groves, mixed groves of cork oak, holm oak and zen oak, mixed groves of holm oak and thuja, water plan, dense and open shrub-lands of oaks, vegetable crops or orchard, herbaceous plants and bare soils. Texture attributes seem to provide no useful information while spatial attributes of shape, compactness seem to be performant for all the dominant features, such as pure stands of Aleppo pine and/or cork oak and bare soils. Landscaped sub-units are individualized while conserving the spatial information. Continuously dominant dense stands over a large area were formed into a single class, such as dense, fragmented stands with clear stands. Low shrublands formations and high wooded shrublands are well individualized but with some confusion with enclaves for the former. Overall, a visual evaluation of the classification shows that the classification reflects the actual spatial state of the study area at the landscape level. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=forest" title="forest">forest</a>, <a href="https://publications.waset.org/abstracts/search?q=oaks" title=" oaks"> oaks</a>, <a href="https://publications.waset.org/abstracts/search?q=remote%20sensing" title=" remote sensing"> remote sensing</a>, <a href="https://publications.waset.org/abstracts/search?q=biodiversity" title=" biodiversity"> biodiversity</a>, <a href="https://publications.waset.org/abstracts/search?q=shrublands" title=" shrublands"> shrublands</a> </p> <a href="https://publications.waset.org/abstracts/187570/mapping-forest-biodiversity-using-remote-sensing-and-field-data-in-the-national-park-of-tlemcen-algeria" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/187570.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">30</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4436</span> Local Directional Encoded Derivative Binary Pattern Based Coral Image Classification Using Weighted Distance Gray Wolf Optimization Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Annalakshmi%20G.">Annalakshmi G.</a>, <a href="https://publications.waset.org/abstracts/search?q=Sakthivel%20Murugan%20S."> Sakthivel Murugan S.</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents a local directional encoded derivative binary pattern (LDEDBP) feature extraction method that can be applied for the classification of submarine coral reef images. The classification of coral reef images using texture features is difficult due to the dissimilarities in class samples. In coral reef image classification, texture features are extracted using the proposed method called local directional encoded derivative binary pattern (LDEDBP). The proposed approach extracts the complete structural arrangement of the local region using local binary batten (LBP) and also extracts the edge information using local directional pattern (LDP) from the edge response available in a particular region, thereby achieving extra discriminative feature value. Typically the LDP extracts the edge details in all eight directions. The process of integrating edge responses along with the local binary pattern achieves a more robust texture descriptor than the other descriptors used in texture feature extraction methods. Finally, the proposed technique is applied to an extreme learning machine (ELM) method with a meta-heuristic algorithm known as weighted distance grey wolf optimizer (GWO) to optimize the input weight and biases of single-hidden-layer feed-forward neural networks (SLFN). In the empirical results, ELM-WDGWO demonstrated their better performance in terms of accuracy on all coral datasets, namely RSMAS, EILAT, EILAT2, and MLC, compared with other state-of-the-art algorithms. The proposed method achieves the highest overall classification accuracy of 94% compared to the other state of art methods. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=feature%20extraction" title="feature extraction">feature extraction</a>, <a href="https://publications.waset.org/abstracts/search?q=local%20directional%20pattern" title=" local directional pattern"> local directional pattern</a>, <a href="https://publications.waset.org/abstracts/search?q=ELM%20classifier" title=" ELM classifier"> ELM classifier</a>, <a href="https://publications.waset.org/abstracts/search?q=GWO%20optimization" title=" GWO optimization"> GWO optimization</a> </p> <a href="https://publications.waset.org/abstracts/142439/local-directional-encoded-derivative-binary-pattern-based-coral-image-classification-using-weighted-distance-gray-wolf-optimization-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/142439.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">163</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4435</span> Research on Ultrafine Particles Classification Using Hydrocyclone with Annular Rinse Water</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Tao%20Youjun">Tao Youjun</a>, <a href="https://publications.waset.org/abstracts/search?q=Zhao%20Younan"> Zhao Younan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The separation effect of fine coal can be improved by the process of pre-desliming. It was significantly enhanced when the fine coal was processed using Falcon concentrator with the removal of -45um coal slime. Ultrafine classification tests using Krebs classification cyclone with annular rinse water showed that increasing feeding pressure can effectively avoid the phenomena of heavy particles passing into overflow and light particles slipping into underflow. The increase of rinse water pressure could reduce the content of fine-grained particles while increasing the classification size. The increase in feeding concentration had a negative effect on the efficiency of classification, meanwhile increased the classification size due to the enhanced hindered settling caused by high underflow concentration. As a result of optimization experiments with response indicator of classification efficiency which based on orthogonal design using Design-Expert software indicated that the optimal classification efficiency reached 91.32% with the feeding pressure of 0.03MPa, the rinse water pressure of 0.02MPa and the feeding concentration of 12.5%. Meanwhile, the classification size was 49.99 μm which had a good agreement with the predicted value. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=hydrocyclone" title="hydrocyclone">hydrocyclone</a>, <a href="https://publications.waset.org/abstracts/search?q=ultrafine%20classification" title=" ultrafine classification"> ultrafine classification</a>, <a href="https://publications.waset.org/abstracts/search?q=slime" title=" slime"> slime</a>, <a href="https://publications.waset.org/abstracts/search?q=classification%20efficiency" title=" classification efficiency"> classification efficiency</a>, <a href="https://publications.waset.org/abstracts/search?q=classification%20size" title=" classification size "> classification size </a> </p> <a href="https://publications.waset.org/abstracts/99752/research-on-ultrafine-particles-classification-using-hydrocyclone-with-annular-rinse-water" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/99752.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">167</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4434</span> Automatic Multi-Label Image Annotation System Guided by Firefly Algorithm and Bayesian Method</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Saad%20M.%20Darwish">Saad M. Darwish</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohamed%20A.%20El-Iskandarani"> Mohamed A. El-Iskandarani</a>, <a href="https://publications.waset.org/abstracts/search?q=Guitar%20M.%20Shawkat"> Guitar M. Shawkat</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Nowadays, the amount of available multimedia data is continuously on the rise. The need to find a required image for an ordinary user is a challenging task. Content based image retrieval (CBIR) computes relevance based on the visual similarity of low-level image features such as color, textures, etc. However, there is a gap between low-level visual features and semantic meanings required by applications. The typical method of bridging the semantic gap is through the automatic image annotation (AIA) that extracts semantic features using machine learning techniques. In this paper, a multi-label image annotation system guided by Firefly and Bayesian method is proposed. Firstly, images are segmented using the maximum variance intra cluster and Firefly algorithm, which is a swarm-based approach with high convergence speed, less computation rate and search for the optimal multiple threshold. Feature extraction techniques based on color features and region properties are applied to obtain the representative features. After that, the images are annotated using translation model based on the Net Bayes system, which is efficient for multi-label learning with high precision and less complexity. Experiments are performed using Corel Database. The results show that the proposed system is better than traditional ones for automatic image annotation and retrieval. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=feature%20extraction" title="feature extraction">feature extraction</a>, <a href="https://publications.waset.org/abstracts/search?q=feature%20selection" title=" feature selection"> feature selection</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20annotation" title=" image annotation"> image annotation</a>, <a href="https://publications.waset.org/abstracts/search?q=classification" title=" classification"> classification</a> </p> <a href="https://publications.waset.org/abstracts/18552/automatic-multi-label-image-annotation-system-guided-by-firefly-algorithm-and-bayesian-method" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/18552.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">586</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4433</span> A Robust Spatial Feature Extraction Method for Facial Expression Recognition</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=H.%20G.%20C.%20P.%20Dinesh">H. G. C. P. Dinesh</a>, <a href="https://publications.waset.org/abstracts/search?q=G.%20Tharshini"> G. Tharshini</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20P.%20B.%20Ekanayake"> M. P. B. Ekanayake</a>, <a href="https://publications.waset.org/abstracts/search?q=G.%20M.%20R.%20I.%20Godaliyadda"> G. M. R. I. Godaliyadda</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents a new spatial feature extraction method based on principle component analysis (PCA) and Fisher Discernment Analysis (FDA) for facial expression recognition. It not only extracts reliable features for classification, but also reduces the feature space dimensions of pattern samples. In this method, first each gray scale image is considered in its entirety as the measurement matrix. Then, principle components (PCs) of row vectors of this matrix and variance of these row vectors along PCs are estimated. Therefore, this method would ensure the preservation of spatial information of the facial image. Afterwards, by incorporating the spectral information of the eigen-filters derived from the PCs, a feature vector was constructed, for a given image. Finally, FDA was used to define a set of basis in a reduced dimension subspace such that the optimal clustering is achieved. The method of FDA defines an inter-class scatter matrix and intra-class scatter matrix to enhance the compactness of each cluster while maximizing the distance between cluster marginal points. In order to matching the test image with the training set, a cosine similarity based Bayesian classification was used. The proposed method was tested on the Cohn-Kanade database and JAFFE database. It was observed that the proposed method which incorporates spatial information to construct an optimal feature space outperforms the standard PCA and FDA based methods. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=facial%20expression%20recognition" title="facial expression recognition">facial expression recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=principle%20component%20analysis%20%28PCA%29" title=" principle component analysis (PCA)"> principle component analysis (PCA)</a>, <a href="https://publications.waset.org/abstracts/search?q=fisher%20discernment%20analysis%20%28FDA%29" title=" fisher discernment analysis (FDA)"> fisher discernment analysis (FDA)</a>, <a href="https://publications.waset.org/abstracts/search?q=eigen-filter" title=" eigen-filter"> eigen-filter</a>, <a href="https://publications.waset.org/abstracts/search?q=cosine%20similarity" title=" cosine similarity"> cosine similarity</a>, <a href="https://publications.waset.org/abstracts/search?q=bayesian%20classifier" title=" bayesian classifier"> bayesian classifier</a>, <a href="https://publications.waset.org/abstracts/search?q=f-measure" title=" f-measure"> f-measure</a> </p> <a href="https://publications.waset.org/abstracts/36459/a-robust-spatial-feature-extraction-method-for-facial-expression-recognition" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/36459.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">425</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4432</span> Radical Web Text Classification Using a Composite-Based Approach</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kolade%20Olawande%20Owoeye">Kolade Olawande Owoeye</a>, <a href="https://publications.waset.org/abstracts/search?q=George%20R.%20S.%20Weir"> George R. S. Weir</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The widespread of terrorism and extremism activities on the internet has become a major threat to the government and national securities due to their potential dangers which have necessitated the need for intelligence gathering via web and real-time monitoring of potential websites for extremist activities. However, the manual classification for such contents is practically difficult or time-consuming. In response to this challenge, an automated classification system called composite technique was developed. This is a computational framework that explores the combination of both semantics and syntactic features of textual contents of a web. We implemented the framework on a set of extremist webpages dataset that has been subjected to the manual classification process. Therein, we developed a classification model on the data using J48 decision algorithm, this is to generate a measure of how well each page can be classified into their appropriate classes. The classification result obtained from our method when compared with other states of arts, indicated a 96% success rate in classifying overall webpages when matched against the manual classification. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=extremist" title="extremist">extremist</a>, <a href="https://publications.waset.org/abstracts/search?q=web%20pages" title=" web pages"> web pages</a>, <a href="https://publications.waset.org/abstracts/search?q=classification" title=" classification"> classification</a>, <a href="https://publications.waset.org/abstracts/search?q=semantics" title=" semantics"> semantics</a>, <a href="https://publications.waset.org/abstracts/search?q=posit" title=" posit"> posit</a> </p> <a href="https://publications.waset.org/abstracts/98432/radical-web-text-classification-using-a-composite-based-approach" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/98432.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">145</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4431</span> Transfer Learning for Protein Structure Classification at Low Resolution</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Alexander%20Hudson">Alexander Hudson</a>, <a href="https://publications.waset.org/abstracts/search?q=Shaogang%20Gong"> Shaogang Gong</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Structure determination is key to understanding protein function at a molecular level. Whilst significant advances have been made in predicting structure and function from amino acid sequence, researchers must still rely on expensive, time-consuming analytical methods to visualise detailed protein conformation. In this study, we demonstrate that it is possible to make accurate (≥80%) predictions of protein class and architecture from structures determined at low (>3A) resolution, using a deep convolutional neural network trained on high-resolution (≤3A) structures represented as 2D matrices. Thus, we provide proof of concept for high-speed, low-cost protein structure classification at low resolution, and a basis for extension to prediction of function. We investigate the impact of the input representation on classification performance, showing that side-chain information may not be necessary for fine-grained structure predictions. Finally, we confirm that high resolution, low-resolution and NMR-determined structures inhabit a common feature space, and thus provide a theoretical foundation for boosting with single-image super-resolution. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=transfer%20learning" title="transfer learning">transfer learning</a>, <a href="https://publications.waset.org/abstracts/search?q=protein%20distance%20maps" title=" protein distance maps"> protein distance maps</a>, <a href="https://publications.waset.org/abstracts/search?q=protein%20structure%20classification" title=" protein structure classification"> protein structure classification</a>, <a href="https://publications.waset.org/abstracts/search?q=neural%20networks" title=" neural networks"> neural networks</a> </p> <a href="https://publications.waset.org/abstracts/129704/transfer-learning-for-protein-structure-classification-at-low-resolution" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/129704.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">136</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4430</span> Non-intrusive Hand Control of Drone Using an Inexpensive and Streamlined Convolutional Neural Network Approach</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Evan%20Lowhorn">Evan Lowhorn</a>, <a href="https://publications.waset.org/abstracts/search?q=Rocio%20Alba-Flores"> Rocio Alba-Flores</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The purpose of this work is to develop a method for classifying hand signals and using the output in a drone control algorithm. To achieve this, methods based on Convolutional Neural Networks (CNN) were applied. CNN's are a subset of deep learning, which allows grid-like inputs to be processed and passed through a neural network to be trained for classification. This type of neural network allows for classification via imaging, which is less intrusive than previous methods using biosensors, such as EMG sensors. Classification CNN's operate purely from the pixel values in an image; therefore they can be used without additional exteroceptive sensors. A development bench was constructed using a desktop computer connected to a high-definition webcam mounted on a scissor arm. This allowed the camera to be pointed downwards at the desk to provide a constant solid background for the dataset and a clear detection area for the user. A MATLAB script was created to automate dataset image capture at the development bench and save the images to the desktop. This allowed the user to create their own dataset of 12,000 images within three hours. These images were evenly distributed among seven classes. The defined classes include forward, backward, left, right, idle, and land. The drone has a popular flip function which was also included as an additional class. To simplify control, the corresponding hand signals chosen were the numerical hand signs for one through five for movements, a fist for land, and the universal “ok” sign for the flip command. Transfer learning with PyTorch (Python) was performed using a pre-trained 18-layer residual learning network (ResNet-18) to retrain the network for custom classification. An algorithm was created to interpret the classification and send encoded messages to a Ryze Tello drone over its 2.4 GHz Wi-Fi connection. The drone’s movements were performed in half-meter distance increments at a constant speed. When combined with the drone control algorithm, the classification performed as desired with negligible latency when compared to the delay in the drone’s movement commands. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=classification" title="classification">classification</a>, <a href="https://publications.waset.org/abstracts/search?q=computer%20vision" title=" computer vision"> computer vision</a>, <a href="https://publications.waset.org/abstracts/search?q=convolutional%20neural%20networks" title=" convolutional neural networks"> convolutional neural networks</a>, <a href="https://publications.waset.org/abstracts/search?q=drone%20control" title=" drone control"> drone control</a> </p> <a href="https://publications.waset.org/abstracts/139743/non-intrusive-hand-control-of-drone-using-an-inexpensive-and-streamlined-convolutional-neural-network-approach" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/139743.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">210</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4429</span> Texture Analysis of Grayscale Co-Occurrence Matrix on Mammographic Indexed Image</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=S.%20Sushma">S. Sushma</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20Balasubramanian"> S. Balasubramanian</a>, <a href="https://publications.waset.org/abstracts/search?q=K.%20C.%20Latha"> K. C. Latha</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The mammographic image of breast cancer compressed and synthesized to get co-efficient values which will be converted (5x5) matrix to get ROI image where we get the highest value of effected region and with the same ideology the technique has been extended to differentiate between Calcification and normal cell image using mean value derived from 5x5 matrix values <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=texture%20analysis" title="texture analysis">texture analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=mammographic%20image" title=" mammographic image"> mammographic image</a>, <a href="https://publications.waset.org/abstracts/search?q=partitioned%20gray%20scale%20co-oocurance%20matrix" title=" partitioned gray scale co-oocurance matrix"> partitioned gray scale co-oocurance matrix</a>, <a href="https://publications.waset.org/abstracts/search?q=co-efficient" title=" co-efficient "> co-efficient </a> </p> <a href="https://publications.waset.org/abstracts/17516/texture-analysis-of-grayscale-co-occurrence-matrix-on-mammographic-indexed-image" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/17516.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">533</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4428</span> Deep Learning Application for Object Image Recognition and Robot Automatic Grasping</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Shiuh-Jer%20Huang">Shiuh-Jer Huang</a>, <a href="https://publications.waset.org/abstracts/search?q=Chen-Zon%20Yan"> Chen-Zon Yan</a>, <a href="https://publications.waset.org/abstracts/search?q=C.%20K.%20Huang"> C. K. Huang</a>, <a href="https://publications.waset.org/abstracts/search?q=Chun-Chien%20Ting"> Chun-Chien Ting</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Since the vision system application in industrial environment for autonomous purposes is required intensely, the image recognition technique becomes an important research topic. Here, deep learning algorithm is employed in image system to recognize the industrial object and integrate with a 7A6 Series Manipulator for object automatic gripping task. PC and Graphic Processing Unit (GPU) are chosen to construct the 3D Vision Recognition System. Depth Camera (Intel RealSense SR300) is employed to extract the image for object recognition and coordinate derivation. The YOLOv2 scheme is adopted in Convolution neural network (CNN) structure for object classification and center point prediction. Additionally, image processing strategy is used to find the object contour for calculating the object orientation angle. Then, the specified object location and orientation information are sent to robotic controller. Finally, a six-axis manipulator can grasp the specific object in a random environment based on the user command and the extracted image information. The experimental results show that YOLOv2 has been successfully employed to detect the object location and category with confidence near 0.9 and 3D position error less than 0.4 mm. It is useful for future intelligent robotic application in industrial 4.0 environment. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title="deep learning">deep learning</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20processing" title=" image processing"> image processing</a>, <a href="https://publications.waset.org/abstracts/search?q=convolution%20neural%20network" title=" convolution neural network"> convolution neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=YOLOv2" title=" YOLOv2"> YOLOv2</a>, <a href="https://publications.waset.org/abstracts/search?q=7A6%20series%20manipulator" title=" 7A6 series manipulator"> 7A6 series manipulator</a> </p> <a href="https://publications.waset.org/abstracts/110468/deep-learning-application-for-object-image-recognition-and-robot-automatic-grasping" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/110468.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">250</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4427</span> Pose Normalization Network for Object Classification</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bingquan%20Shen">Bingquan Shen</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Convolutional Neural Networks (CNN) have demonstrated their effectiveness in synthesizing 3D views of object instances at various viewpoints. Given the problem where one have limited viewpoints of a particular object for classification, we present a pose normalization architecture to transform the object to existing viewpoints in the training dataset before classification to yield better classification performance. We have demonstrated that this Pose Normalization Network (PNN) can capture the style of the target object and is able to re-render it to a desired viewpoint. Moreover, we have shown that the PNN improves the classification result for the 3D chairs dataset and ShapeNet airplanes dataset when given only images at limited viewpoint, as compared to a CNN baseline. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=convolutional%20neural%20networks" title="convolutional neural networks">convolutional neural networks</a>, <a href="https://publications.waset.org/abstracts/search?q=object%20classification" title=" object classification"> object classification</a>, <a href="https://publications.waset.org/abstracts/search?q=pose%20normalization" title=" pose normalization"> pose normalization</a>, <a href="https://publications.waset.org/abstracts/search?q=viewpoint%20invariant" title=" viewpoint invariant"> viewpoint invariant</a> </p> <a href="https://publications.waset.org/abstracts/56852/pose-normalization-network-for-object-classification" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/56852.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">352</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4426</span> A Robust System for Foot Arch Type Classification from Static Foot Pressure Distribution Data Using Linear Discriminant Analysis</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=R.%20Periyasamy">R. Periyasamy</a>, <a href="https://publications.waset.org/abstracts/search?q=Deepak%20Joshi"> Deepak Joshi</a>, <a href="https://publications.waset.org/abstracts/search?q=Sneh%20Anand"> Sneh Anand </a> </p> <p class="card-text"><strong>Abstract:</strong></p> Foot posture assessment is important to evaluate foot type, causing gait and postural defects in all age groups. Although different methods are used for classification of foot arch type in clinical/research examination, there is no clear approach for selecting the most appropriate measurement system. Therefore, the aim of this study was to develop a system for evaluation of foot type as clinical decision-making aids for diagnosis of flat and normal arch based on the Arch Index (AI) and foot pressure distribution parameter - Power Ratio (PR) data. The accuracy of the system was evaluated for 27 subjects with age ranging from 24 to 65 years. Foot area measurements (hind foot, mid foot, and forefoot) were acquired simultaneously from foot pressure intensity image using portable PedoPowerGraph system and analysis of the image in frequency domain to obtain foot pressure distribution parameter - PR data. From our results, we obtain 100% classification accuracy of normal and flat foot by using the linear discriminant analysis method. We observe there is no misclassification of foot types because of incorporating foot pressure distribution data instead of only arch index (AI). We found that the mid-foot pressure distribution ratio data and arch index (AI) value are well correlated to foot arch type based on visual analysis. Therefore, this paper suggests that the proposed system is accurate and easy to determine foot arch type from arch index (AI), as well as incorporating mid-foot pressure distribution ratio data instead of physical area of contact. Hence, such computational tool based system can help the clinicians for assessment of foot structure and cross-check their diagnosis of flat foot from mid-foot pressure distribution. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=arch%20index" title="arch index">arch index</a>, <a href="https://publications.waset.org/abstracts/search?q=computational%20tool" title=" computational tool"> computational tool</a>, <a href="https://publications.waset.org/abstracts/search?q=static%20foot%20pressure%20intensity%20image" title=" static foot pressure intensity image"> static foot pressure intensity image</a>, <a href="https://publications.waset.org/abstracts/search?q=foot%20pressure%20distribution" title=" foot pressure distribution"> foot pressure distribution</a>, <a href="https://publications.waset.org/abstracts/search?q=linear%20discriminant%20analysis" title=" linear discriminant analysis"> linear discriminant analysis</a> </p> <a href="https://publications.waset.org/abstracts/13085/a-robust-system-for-foot-arch-type-classification-from-static-foot-pressure-distribution-data-using-linear-discriminant-analysis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/13085.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">499</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4425</span> Open-Source YOLO CV For Detection of Dust on Solar PV Surface</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jeewan%20Rai">Jeewan Rai</a>, <a href="https://publications.waset.org/abstracts/search?q=Kinzang"> Kinzang</a>, <a href="https://publications.waset.org/abstracts/search?q=Yeshi%20Jigme%20Choden"> Yeshi Jigme Choden</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Accumulation of dust on solar panels impacts the overall efficiency and the amount of energy they produce. While various techniques exist for detecting dust to schedule cleaning, many of these methods use MATLAB image processing tools and other licensed software, which can be financially burdensome. This study will investigate the efficiency of a free open-source computer vision library using the YOLO algorithm. The proposed approach has been tested on images of solar panels with varying dust levels through an experiment setup. The experimental findings illustrated the effectiveness of using the YOLO-based image classification method and the overall dust detection approach with an accuracy of 90% in distinguishing between clean and dusty panels. This open-source solution provides a cost effective and accessible alternative to commercial image processing tools, offering solutions for optimizing solar panel maintenance and enhancing energy production. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=YOLO" title="YOLO">YOLO</a>, <a href="https://publications.waset.org/abstracts/search?q=openCV" title=" openCV"> openCV</a>, <a href="https://publications.waset.org/abstracts/search?q=dust%20detection" title=" dust detection"> dust detection</a>, <a href="https://publications.waset.org/abstracts/search?q=solar%20panels" title=" solar panels"> solar panels</a>, <a href="https://publications.waset.org/abstracts/search?q=computer%20vision" title=" computer vision"> computer vision</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20processing" title=" image processing"> image processing</a> </p> <a href="https://publications.waset.org/abstracts/189289/open-source-yolo-cv-for-detection-of-dust-on-solar-pv-surface" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/189289.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">32</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4424</span> Lean Models Classification: Towards a Holistic View</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Y.%20Tiamaz">Y. Tiamaz</a>, <a href="https://publications.waset.org/abstracts/search?q=N.%20Souissi"> N. Souissi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The purpose of this paper is to present a classification of Lean models which aims to capture all the concepts related to this approach and thus facilitate its implementation. This classification allows the identification of the most relevant models according to several dimensions. From this perspective, we present a review and an analysis of Lean models literature and we propose dimensions for the classification of the current proposals while respecting among others the axes of the Lean approach, the maturity of the models as well as their application domains. This classification allowed us to conclude that researchers essentially consider the Lean approach as a toolbox also they design their models to solve problems related to a specific environment. Since Lean approach is no longer intended only for the automotive sector where it was invented, but to all fields (IT, Hospital, ...), we consider that this approach requires a generic model that is capable of being implemented in all areas. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=lean%20approach" title="lean approach">lean approach</a>, <a href="https://publications.waset.org/abstracts/search?q=lean%20models" title=" lean models"> lean models</a>, <a href="https://publications.waset.org/abstracts/search?q=classification" title=" classification"> classification</a>, <a href="https://publications.waset.org/abstracts/search?q=dimensions" title=" dimensions"> dimensions</a>, <a href="https://publications.waset.org/abstracts/search?q=holistic%20view" title=" holistic view"> holistic view</a> </p> <a href="https://publications.waset.org/abstracts/65716/lean-models-classification-towards-a-holistic-view" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/65716.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">435</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4423</span> Size Reduction of Images Using Constraint Optimization Approach for Machine Communications</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Chee%20Sun%20Won">Chee Sun Won</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents the size reduction of images for machine-to-machine communications. Here, the salient image regions to be preserved include the image patches of the key-points such as corners and blobs. Based on a saliency image map from the key-points and their image patches, an axis-aligned grid-size optimization is proposed for the reduction of image size. To increase the size-reduction efficiency the aspect ratio constraint is relaxed in the constraint optimization framework. The proposed method yields higher matching accuracy after the size reduction than the conventional content-aware image size-reduction methods. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=image%20compression" title="image compression">image compression</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20matching" title=" image matching"> image matching</a>, <a href="https://publications.waset.org/abstracts/search?q=key-point%20detection%20and%20description" title=" key-point detection and description"> key-point detection and description</a>, <a href="https://publications.waset.org/abstracts/search?q=machine-to-machine%20communication" title=" machine-to-machine communication"> machine-to-machine communication</a> </p> <a href="https://publications.waset.org/abstracts/67605/size-reduction-of-images-using-constraint-optimization-approach-for-machine-communications" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/67605.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">418</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4422</span> Comparison of Various Classification Techniques Using WEKA for Colon Cancer Detection</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Beema%20Akbar">Beema Akbar</a>, <a href="https://publications.waset.org/abstracts/search?q=Varun%20P.%20Gopi"> Varun P. Gopi</a>, <a href="https://publications.waset.org/abstracts/search?q=V.%20Suresh%20Babu"> V. Suresh Babu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Colon cancer causes the deaths of about half a million people every year. The common method of its detection is histopathological tissue analysis, it leads to tiredness and workload to the pathologist. A novel method is proposed that combines both structural and statistical pattern recognition used for the detection of colon cancer. This paper presents a comparison among the different classifiers such as Multilayer Perception (MLP), Sequential Minimal Optimization (SMO), Bayesian Logistic Regression (BLR) and k-star by using classification accuracy and error rate based on the percentage split method. The result shows that the best algorithm in WEKA is MLP classifier with an accuracy of 83.333% and kappa statistics is 0.625. The MLP classifier which has a lower error rate, will be preferred as more powerful classification capability. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=colon%20cancer" title="colon cancer">colon cancer</a>, <a href="https://publications.waset.org/abstracts/search?q=histopathological%20image" title=" histopathological image"> histopathological image</a>, <a href="https://publications.waset.org/abstracts/search?q=structural%20and%20statistical%20pattern%20recognition" title=" structural and statistical pattern recognition"> structural and statistical pattern recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=multilayer%20perception" title=" multilayer perception"> multilayer perception</a> </p> <a href="https://publications.waset.org/abstracts/17345/comparison-of-various-classification-techniques-using-weka-for-colon-cancer-detection" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/17345.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">575</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4421</span> Detection and Classification of Rubber Tree Leaf Diseases Using Machine Learning</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kavyadevi%20N.">Kavyadevi N.</a>, <a href="https://publications.waset.org/abstracts/search?q=Kaviya%20G."> Kaviya G.</a>, <a href="https://publications.waset.org/abstracts/search?q=Gowsalya%20P."> Gowsalya P.</a>, <a href="https://publications.waset.org/abstracts/search?q=Janani%20M."> Janani M.</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohanraj%20S."> Mohanraj S.</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Hevea brasiliensis, also known as the rubber tree, is one of the foremost assets of crops in the world. One of the most significant advantages of the Rubber Plant in terms of air oxygenation is its capacity to reduce the likelihood of an individual developing respiratory allergies like asthma. To construct such a system that can properly identify crop diseases and pests and then create a database of insecticides for each pest and disease, we must first give treatment for the illness that has been detected. We shall primarily examine three major leaf diseases since they are economically deficient in this article, which is Bird's eye spot, algal spot and powdery mildew. And the recommended work focuses on disease identification on rubber tree leaves. It will be accomplished by employing one of the superior algorithms. Input, Preprocessing, Image Segmentation, Extraction Feature, and Classification will be followed by the processing technique. We will use time-consuming procedures that they use to detect the sickness. As a consequence, the main ailments, underlying causes, and signs and symptoms of diseases that harm the rubber tree are covered in this study. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=image%20processing" title="image processing">image processing</a>, <a href="https://publications.waset.org/abstracts/search?q=python" title=" python"> python</a>, <a href="https://publications.waset.org/abstracts/search?q=convolution%20neural%20network%20%28CNN%29" title=" convolution neural network (CNN)"> convolution neural network (CNN)</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a> </p> <a href="https://publications.waset.org/abstracts/163559/detection-and-classification-of-rubber-tree-leaf-diseases-using-machine-learning" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/163559.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">76</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4420</span> Non-Targeted Adversarial Object Detection Attack: Fast Gradient Sign Method</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bandar%20Alahmadi">Bandar Alahmadi</a>, <a href="https://publications.waset.org/abstracts/search?q=Manohar%20Mareboyana"> Manohar Mareboyana</a>, <a href="https://publications.waset.org/abstracts/search?q=Lethia%20Jackson"> Lethia Jackson</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Today, there are many applications that are using computer vision models, such as face recognition, image classification, and object detection. The accuracy of these models is very important for the performance of these applications. One challenge that facing the computer vision models is the adversarial examples attack. In computer vision, the adversarial example is an image that is intentionally designed to cause the machine learning model to misclassify it. One of very well-known method that is used to attack the Convolution Neural Network (CNN) is Fast Gradient Sign Method (FGSM). The goal of this method is to find the perturbation that can fool the CNN using the gradient of the cost function of CNN. In this paper, we introduce a novel model that can attack Regional-Convolution Neural Network (R-CNN) that use FGSM. We first extract the regions that are detected by R-CNN, and then we resize these regions into the size of regular images. Then, we find the best perturbation of the regions that can fool CNN using FGSM. Next, we add the resulted perturbation to the attacked region to get a new region image that looks similar to the original image to human eyes. Finally, we placed the regions back to the original image and test the R-CNN with the attacked images. Our model could drop the accuracy of the R-CNN when we tested with Pascal VOC 2012 dataset. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=adversarial%20examples" title="adversarial examples">adversarial examples</a>, <a href="https://publications.waset.org/abstracts/search?q=attack" title=" attack"> attack</a>, <a href="https://publications.waset.org/abstracts/search?q=computer%20vision" title=" computer vision"> computer vision</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20processing" title=" image processing"> image processing</a> </p> <a href="https://publications.waset.org/abstracts/103308/non-targeted-adversarial-object-detection-attack-fast-gradient-sign-method" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/103308.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">193</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4419</span> The Change of Urban Land Use/Cover Using Object Based Approach for Southern Bali</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=I.%20Gusti%20A.%20A.%20Rai%20Asmiwyati">I. Gusti A. A. Rai Asmiwyati</a>, <a href="https://publications.waset.org/abstracts/search?q=Robert%20J.%20Corner"> Robert J. Corner</a>, <a href="https://publications.waset.org/abstracts/search?q=Ashraf%20M.%20Dewan"> Ashraf M. Dewan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Change on land use/cover (LULC) dominantly affects spatial structure and function. It can have such impacts by disrupting social culture practice and disturbing physical elements. Thus, it has become essential to understand of the dynamics in time and space of LULC as it can be used as a critical input for developing sustainable LULC. This study was an attempt to map and monitor the LULC change in Bali Indonesia from 2003 to 2013. Using object based classification to improve the accuracy, and change detection, multi temporal land use/cover data were extracted from a set of ASTER satellite image. The overall accuracies of the classification maps of 2003 and 2013 were 86.99% and 80.36%, respectively. Built up area and paddy field were the dominant type of land use/cover in both years. Patch increase dominantly in 2003 illustrated the rapid paddy field fragmentation and the huge occurring transformation. This approach is new for the case of diverse urban features of Bali that has been growing fast and increased the classification accuracy than the manual pixel based classification. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=land%20use%2Fcover" title="land use/cover">land use/cover</a>, <a href="https://publications.waset.org/abstracts/search?q=urban" title=" urban"> urban</a>, <a href="https://publications.waset.org/abstracts/search?q=Bali" title=" Bali"> Bali</a>, <a href="https://publications.waset.org/abstracts/search?q=ASTER" title=" ASTER"> ASTER</a> </p> <a href="https://publications.waset.org/abstracts/20635/the-change-of-urban-land-usecover-using-object-based-approach-for-southern-bali" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/20635.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">541</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4418</span> Characterization and Monitoring of the Yarn Faults Using Diametric Fault System</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=S.%20M.%20Ishtiaque">S. M. Ishtiaque</a>, <a href="https://publications.waset.org/abstracts/search?q=V.%20K.%20Yadav"> V. K. Yadav</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20D.%20Joshi"> S. D. Joshi</a>, <a href="https://publications.waset.org/abstracts/search?q=J.%20K.%20Chatterjee"> J. K. Chatterjee</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The DIAMETRIC FAULTS system has been developed that captures a bi-directional image of yarn continuously in sequentially manner and provides the detailed classification of faults. A novel mathematical framework developed on the acquired bi-directional images forms the basis of fault classification in four broad categories, namely, Thick1, Thick2, Thin and Normal Yarn. A discretised version of Radon transformation has been used to convert the bi-directional images into one-dimensional signals. Images were divided into training and test sample sets. Karhunen–Loève Transformation (KLT) basis is computed for the signals from the images in training set for each fault class taking top six highest energy eigen vectors. The fault class of the test image is identified by taking the Euclidean distance of its signal from its projection on the KLT basis for each sample realization and fault class in the training set. Euclidean distance applied using various techniques is used for classifying an unknown fault class. An accuracy of about 90% is achieved in detecting the correct fault class using the various techniques. The four broad fault classes were further sub classified in four sub groups based on the user set boundary limits for fault length and fault volume. The fault cross-sectional area and the fault length defines the total volume of fault. A distinct distribution of faults is found in terms of their volume and physical dimensions which can be used for monitoring the yarn faults. It has been shown from the configurational based characterization and classification that the spun yarn faults arising out of mass variation, exhibit distinct characteristics in terms of their contours, sizes and shapes apart from their frequency of occurrences. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Euclidean%20distance" title="Euclidean distance">Euclidean distance</a>, <a href="https://publications.waset.org/abstracts/search?q=fault%20classification" title=" fault classification"> fault classification</a>, <a href="https://publications.waset.org/abstracts/search?q=KLT" title=" KLT"> KLT</a>, <a href="https://publications.waset.org/abstracts/search?q=Radon%20Transform" title=" Radon Transform"> Radon Transform</a> </p> <a href="https://publications.waset.org/abstracts/65167/characterization-and-monitoring-of-the-yarn-faults-using-diametric-fault-system" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/65167.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">265</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4417</span> Breast Cancer Metastasis Detection and Localization through Transfer-Learning Convolutional Neural Network Classification Based on Convolutional Denoising Autoencoder Stack</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Varun%20Agarwal">Varun Agarwal</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Introduction: With the advent of personalized medicine, histopathological review of whole slide images (WSIs) for cancer diagnosis presents an exceedingly time-consuming, complex task. Specifically, detecting metastatic regions in WSIs of sentinel lymph node biopsies necessitates a full-scanned, holistic evaluation of the image. Thus, digital pathology, low-level image manipulation algorithms, and machine learning provide significant advancements in improving the efficiency and accuracy of WSI analysis. Using Camelyon16 data, this paper proposes a deep learning pipeline to automate and ameliorate breast cancer metastasis localization and WSI classification. Methodology: The model broadly follows five stages -region of interest detection, WSI partitioning into image tiles, convolutional neural network (CNN) image-segment classifications, probabilistic mapping of tumor localizations, and further processing for whole WSI classification. Transfer learning is applied to the task, with the implementation of Inception-ResNetV2 - an effective CNN classifier that uses residual connections to enhance feature representation, adding convolved outputs in the inception unit to the proceeding input data. Moreover, in order to augment the performance of the transfer learning CNN, a stack of convolutional denoising autoencoders (CDAE) is applied to produce embeddings that enrich image representation. Through a saliency-detection algorithm, visual training segments are generated, which are then processed through a denoising autoencoder -primarily consisting of convolutional, leaky rectified linear unit, and batch normalization layers- and subsequently a contrast-normalization function. A spatial pyramid pooling algorithm extracts the key features from the processed image, creating a viable feature map for the CNN that minimizes spatial resolution and noise. Results and Conclusion: The simplified and effective architecture of the fine-tuned transfer learning Inception-ResNetV2 network enhanced with the CDAE stack yields state of the art performance in WSI classification and tumor localization, achieving AUC scores of 0.947 and 0.753, respectively. The convolutional feature retention and compilation with the residual connections to inception units synergized with the input denoising algorithm enable the pipeline to serve as an effective, efficient tool in the histopathological review of WSIs. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=breast%20cancer" title="breast cancer">breast cancer</a>, <a href="https://publications.waset.org/abstracts/search?q=convolutional%20neural%20networks" title=" convolutional neural networks"> convolutional neural networks</a>, <a href="https://publications.waset.org/abstracts/search?q=metastasis%20mapping" title=" metastasis mapping"> metastasis mapping</a>, <a href="https://publications.waset.org/abstracts/search?q=whole%20slide%20images" title=" whole slide images"> whole slide images</a> </p> <a href="https://publications.waset.org/abstracts/133783/breast-cancer-metastasis-detection-and-localization-through-transfer-learning-convolutional-neural-network-classification-based-on-convolutional-denoising-autoencoder-stack" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/133783.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">130</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4416</span> Design and Implementation of a Counting and Differentiation System for Vehicles through Video Processing</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Derlis%20Gregor">Derlis Gregor</a>, <a href="https://publications.waset.org/abstracts/search?q=Kevin%20Cikel"> Kevin Cikel</a>, <a href="https://publications.waset.org/abstracts/search?q=Mario%20Arzamendia"> Mario Arzamendia</a>, <a href="https://publications.waset.org/abstracts/search?q=Ra%C3%BAl%20Gregor"> Raúl Gregor</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents a self-sustaining mobile system for counting and classification of vehicles through processing video. It proposes a counting and classification algorithm divided in four steps that can be executed multiple times in parallel in a SBC (Single Board Computer), like the Raspberry Pi 2, in such a way that it can be implemented in real time. The first step of the proposed algorithm limits the zone of the image that it will be processed. The second step performs the detection of the mobile objects using a BGS (Background Subtraction) algorithm based on the GMM (Gaussian Mixture Model), as well as a shadow removal algorithm using physical-based features, followed by morphological operations. In the first step the vehicle detection will be performed by using edge detection algorithms and the vehicle following through Kalman filters. The last step of the proposed algorithm registers the vehicle passing and performs their classification according to their areas. An auto-sustainable system is proposed, powered by batteries and photovoltaic solar panels, and the data transmission is done through GPRS (General Packet Radio Service)eliminating the need of using external cable, which will facilitate it deployment and translation to any location where it could operate. The self-sustaining trailer will allow the counting and classification of vehicles in specific zones with difficult access. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=intelligent%20transportation%20system" title="intelligent transportation system">intelligent transportation system</a>, <a href="https://publications.waset.org/abstracts/search?q=object%20detection" title=" object detection"> object detection</a>, <a href="https://publications.waset.org/abstracts/search?q=vehicle%20couting" title=" vehicle couting"> vehicle couting</a>, <a href="https://publications.waset.org/abstracts/search?q=vehicle%20classification" title=" vehicle classification"> vehicle classification</a>, <a href="https://publications.waset.org/abstracts/search?q=video%20processing" title=" video processing"> video processing</a> </p> <a href="https://publications.waset.org/abstracts/43870/design-and-implementation-of-a-counting-and-differentiation-system-for-vehicles-through-video-processing" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/43870.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">322</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4415</span> A Review on Artificial Neural Networks in Image Processing</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=B.%20Afsharipoor">B. Afsharipoor</a>, <a href="https://publications.waset.org/abstracts/search?q=E.%20Nazemi"> E. Nazemi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Artificial neural networks (ANNs) are powerful tool for prediction which can be trained based on a set of examples and thus, it would be useful for nonlinear image processing. The present paper reviews several paper regarding applications of ANN in image processing to shed the light on advantage and disadvantage of ANNs in this field. Different steps in the image processing chain including pre-processing, enhancement, segmentation, object recognition, image understanding and optimization by using ANN are summarized. Furthermore, results on using multi artificial neural networks are presented. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=neural%20networks" title="neural networks">neural networks</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20processing" title=" image processing"> image processing</a>, <a href="https://publications.waset.org/abstracts/search?q=segmentation" title=" segmentation"> segmentation</a>, <a href="https://publications.waset.org/abstracts/search?q=object%20recognition" title=" object recognition"> object recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20understanding" title=" image understanding"> image understanding</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization" title=" optimization"> optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=MANN" title=" MANN"> MANN</a> </p> <a href="https://publications.waset.org/abstracts/36843/a-review-on-artificial-neural-networks-in-image-processing" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/36843.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">407</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4414</span> Hyperspectral Data Classification Algorithm Based on the Deep Belief and Self-Organizing Neural Network</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Li%20Qingjian">Li Qingjian</a>, <a href="https://publications.waset.org/abstracts/search?q=Li%20Ke"> Li Ke</a>, <a href="https://publications.waset.org/abstracts/search?q=He%20Chun"> He Chun</a>, <a href="https://publications.waset.org/abstracts/search?q=Huang%20Yong"> Huang Yong</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, the method of combining the Pohl Seidman's deep belief network with the self-organizing neural network is proposed to classify the target. This method is mainly aimed at the high nonlinearity of the hyperspectral image, the high sample dimension and the difficulty in designing the classifier. The main feature of original data is extracted by deep belief network. In the process of extracting features, adding known labels samples to fine tune the network, enriching the main characteristics. Then, the extracted feature vectors are classified into the self-organizing neural network. This method can effectively reduce the dimensions of data in the spectrum dimension in the preservation of large amounts of raw data information, to solve the traditional clustering and the long training time when labeled samples less deep learning algorithm for training problems, improve the classification accuracy and robustness. Through the data simulation, the results show that the proposed network structure can get a higher classification precision in the case of a small number of known label samples. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=DBN" title="DBN">DBN</a>, <a href="https://publications.waset.org/abstracts/search?q=SOM" title=" SOM"> SOM</a>, <a href="https://publications.waset.org/abstracts/search?q=pattern%20classification" title=" pattern classification"> pattern classification</a>, <a href="https://publications.waset.org/abstracts/search?q=hyperspectral" title=" hyperspectral"> hyperspectral</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20compression" title=" data compression"> data compression</a> </p> <a href="https://publications.waset.org/abstracts/89759/hyperspectral-data-classification-algorithm-based-on-the-deep-belief-and-self-organizing-neural-network" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/89759.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">341</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4413</span> An Advanced Automated Brain Tumor Diagnostics Approach</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Berkan%20Ural">Berkan Ural</a>, <a href="https://publications.waset.org/abstracts/search?q=Arif%20Eser"> Arif Eser</a>, <a href="https://publications.waset.org/abstracts/search?q=Sinan%20Apaydin"> Sinan Apaydin</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Medical image processing is generally become a challenging task nowadays. Indeed, processing of brain MRI images is one of the difficult parts of this area. This study proposes a hybrid well-defined approach which is consisted from tumor detection, extraction and analyzing steps. This approach is mainly consisted from a computer aided diagnostics system for identifying and detecting the tumor formation in any region of the brain and this system is commonly used for early prediction of brain tumor using advanced image processing and probabilistic neural network methods, respectively. For this approach, generally, some advanced noise removal functions, image processing methods such as automatic segmentation and morphological operations are used to detect the brain tumor boundaries and to obtain the important feature parameters of the tumor region. All stages of the approach are done specifically with using MATLAB software. Generally, for this approach, firstly tumor is successfully detected and the tumor area is contoured with a specific colored circle by the computer aided diagnostics program. Then, the tumor is segmented and some morphological processes are achieved to increase the visibility of the tumor area. Moreover, while this process continues, the tumor area and important shape based features are also calculated. Finally, with using the probabilistic neural network method and with using some advanced classification steps, tumor area and the type of the tumor are clearly obtained. Also, the future aim of this study is to detect the severity of lesions through classes of brain tumor which is achieved through advanced multi classification and neural network stages and creating a user friendly environment using GUI in MATLAB. In the experimental part of the study, generally, 100 images are used to train the diagnostics system and 100 out of sample images are also used to test and to check the whole results. The preliminary results demonstrate the high classification accuracy for the neural network structure. Finally, according to the results, this situation also motivates us to extend this framework to detect and localize the tumors in the other organs. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=image%20processing%20algorithms" title="image processing algorithms">image processing algorithms</a>, <a href="https://publications.waset.org/abstracts/search?q=magnetic%20resonance%20imaging" title=" magnetic resonance imaging"> magnetic resonance imaging</a>, <a href="https://publications.waset.org/abstracts/search?q=neural%20network" title=" neural network"> neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=pattern%20recognition" title=" pattern recognition"> pattern recognition</a> </p> <a href="https://publications.waset.org/abstracts/69471/an-advanced-automated-brain-tumor-diagnostics-approach" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/69471.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">418</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4412</span> Integrating Time-Series and High-Spatial Remote Sensing Data Based on Multilevel Decision Fusion</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Xudong%20Guan">Xudong Guan</a>, <a href="https://publications.waset.org/abstracts/search?q=Ainong%20Li"> Ainong Li</a>, <a href="https://publications.waset.org/abstracts/search?q=Gaohuan%20Liu"> Gaohuan Liu</a>, <a href="https://publications.waset.org/abstracts/search?q=Chong%20Huang"> Chong Huang</a>, <a href="https://publications.waset.org/abstracts/search?q=Wei%20Zhao"> Wei Zhao</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Due to the low spatial resolution of MODIS data, the accuracy of small-area plaque extraction with a high degree of landscape fragmentation is greatly limited. To this end, the study combines Landsat data with higher spatial resolution and MODIS data with higher temporal resolution for decision-level fusion. Considering the importance of the land heterogeneity factor in the fusion process, it is superimposed with the weighting factor, which is to linearly weight the Landsat classification result and the MOIDS classification result. Three levels were used to complete the process of data fusion, that is the pixel of MODIS data, the pixel of Landsat data, and objects level that connect between these two levels. The multilevel decision fusion scheme was tested in two sites of the lower Mekong basin. We put forth a comparison test, and it was proved that the classification accuracy was improved compared with the single data source classification results in terms of the overall accuracy. The method was also compared with the two-level combination results and a weighted sum decision rule-based approach. The decision fusion scheme is extensible to other multi-resolution data decision fusion applications. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=image%20classification" title="image classification">image classification</a>, <a href="https://publications.waset.org/abstracts/search?q=decision%20fusion" title=" decision fusion"> decision fusion</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-temporal" title=" multi-temporal"> multi-temporal</a>, <a href="https://publications.waset.org/abstracts/search?q=remote%20sensing" title=" remote sensing"> remote sensing</a> </p> <a href="https://publications.waset.org/abstracts/112195/integrating-time-series-and-high-spatial-remote-sensing-data-based-on-multilevel-decision-fusion" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/112195.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">124</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4411</span> Bitplanes Gray-Level Image Encryption Approach Using Arnold Transform</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ali%20Abdrhman%20M.%20Ukasha">Ali Abdrhman M. Ukasha</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Data security needed in data transmission, storage, and communication to ensure the security. The single step parallel contour extraction (SSPCE) method is used to create the edge map as a key image from the different Gray level/Binary image. Performing the X-OR operation between the key image and each bit plane of the original image for image pixel values change purpose. The Arnold transform used to changes the locations of image pixels as image scrambling process. Experiments have demonstrated that proposed algorithm can fully encrypt 2D Gary level image and completely reconstructed without any distortion. Also shown that the analyzed algorithm have extremely large security against some attacks like salt & pepper and JPEG compression. Its proof that the Gray level image can be protected with a higher security level. The presented method has easy hardware implementation and suitable for multimedia protection in real time applications such as wireless networks and mobile phone services. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=SSPCE%20method" title="SSPCE method">SSPCE method</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20compression-salt-%20peppers%20attacks" title=" image compression-salt- peppers attacks"> image compression-salt- peppers attacks</a>, <a href="https://publications.waset.org/abstracts/search?q=bitplanes%20decomposition" title=" bitplanes decomposition"> bitplanes decomposition</a>, <a href="https://publications.waset.org/abstracts/search?q=Arnold%20transform" title=" Arnold transform"> Arnold transform</a>, <a href="https://publications.waset.org/abstracts/search?q=lossless%20image%20encryption" title=" lossless image encryption"> lossless image encryption</a> </p> <a href="https://publications.waset.org/abstracts/14573/bitplanes-gray-level-image-encryption-approach-using-arnold-transform" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/14573.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">436</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4410</span> Integral Image-Based Differential Filters</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kohei%20Inoue">Kohei Inoue</a>, <a href="https://publications.waset.org/abstracts/search?q=Kenji%20Hara"> Kenji Hara</a>, <a href="https://publications.waset.org/abstracts/search?q=Kiichi%20Urahama"> Kiichi Urahama</a> </p> <p class="card-text"><strong>Abstract:</strong></p> We describe a relationship between integral images and differential images. First, we derive a simple difference filter from conventional integral image. In the derivation, we show that an integral image and the corresponding differential image are related to each other by simultaneous linear equations, where the numbers of unknowns and equations are the same, and therefore, we can execute the integration and differentiation by solving the simultaneous equations. We applied the relationship to an image fusion problem, and experimentally verified the effectiveness of the proposed method. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=integral%20images" title="integral images">integral images</a>, <a href="https://publications.waset.org/abstracts/search?q=differential%20images" title=" differential images"> differential images</a>, <a href="https://publications.waset.org/abstracts/search?q=differential%20filters" title=" differential filters"> differential filters</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20fusion" title=" image fusion"> image fusion</a> </p> <a href="https://publications.waset.org/abstracts/8531/integral-image-based-differential-filters" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/8531.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">506</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4409</span> A Similarity Measure for Classification and Clustering in Image Based Medical and Text Based Banking Applications</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=K.%20P.%20Sandesh">K. P. Sandesh</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20H.%20Suman"> M. H. Suman</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Text processing plays an important role in information retrieval, data-mining, and web search. Measuring the similarity between the documents is an important operation in the text processing field. In this project, a new similarity measure is proposed. To compute the similarity between two documents with respect to a feature the proposed measure takes the following three cases into account: (1) The feature appears in both documents; (2) The feature appears in only one document and; (3) The feature appears in none of the documents. The proposed measure is extended to gauge the similarity between two sets of documents. The effectiveness of our measure is evaluated on several real-world data sets for text classification and clustering problems, especially in banking and health sectors. The results show that the performance obtained by the proposed measure is better than that achieved by the other measures. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=document%20classification" title="document classification">document classification</a>, <a href="https://publications.waset.org/abstracts/search?q=document%20clustering" title=" document clustering"> document clustering</a>, <a href="https://publications.waset.org/abstracts/search?q=entropy" title=" entropy"> entropy</a>, <a href="https://publications.waset.org/abstracts/search?q=accuracy" title=" accuracy"> accuracy</a>, <a href="https://publications.waset.org/abstracts/search?q=classifiers" title=" classifiers"> classifiers</a>, <a href="https://publications.waset.org/abstracts/search?q=clustering%20algorithms" title=" clustering algorithms"> clustering algorithms</a> </p> <a href="https://publications.waset.org/abstracts/22708/a-similarity-measure-for-classification-and-clustering-in-image-based-medical-and-text-based-banking-applications" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/22708.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">518</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4408</span> Bitplanes Image Encryption/Decryption Using Edge Map (SSPCE Method) and Arnold Transform</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ali%20A.%20Ukasha">Ali A. Ukasha</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Data security needed in data transmission, storage, and communication to ensure the security. The single step parallel contour extraction (SSPCE) method is used to create the edge map as a key image from the different Gray level/Binary image. Performing the X-OR operation between the key image and each bit plane of the original image for image pixel values change purpose. The Arnold transform used to changes the locations of image pixels as image scrambling process. Experiments have demonstrated that proposed algorithm can fully encrypt 2D Gary level image and completely reconstructed without any distortion. Also shown that the analyzed algorithm have extremely large security against some attacks like salt & pepper and JPEG compression. Its proof that the Gray level image can be protected with a higher security level. The presented method has easy hardware implementation and suitable for multimedia protection in real time applications such as wireless networks and mobile phone services. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=SSPCE%20method" title="SSPCE method">SSPCE method</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20compression" title=" image compression"> image compression</a>, <a href="https://publications.waset.org/abstracts/search?q=salt%20and%0D%0Apeppers%20attacks" title=" salt and peppers attacks"> salt and peppers attacks</a>, <a href="https://publications.waset.org/abstracts/search?q=bitplanes%20decomposition" title=" bitplanes decomposition"> bitplanes decomposition</a>, <a href="https://publications.waset.org/abstracts/search?q=Arnold%20transform" title=" Arnold transform"> Arnold transform</a>, <a href="https://publications.waset.org/abstracts/search?q=lossless%20image%20encryption" title=" lossless image encryption"> lossless image encryption</a> </p> <a href="https://publications.waset.org/abstracts/14570/bitplanes-image-encryptiondecryption-using-edge-map-sspce-method-and-arnold-transform" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/14570.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">497</span> </span> </div> </div> <ul class="pagination"> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=image%20classification&page=4" rel="prev">&lsaquo;</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=image%20classification&page=1">1</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=image%20classification&page=2">2</a></li> <li class="page-item"><a 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