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image fusion</title> <meta name="description" content="Search results for: multi-focus image fusion"> <meta name="keywords" content="multi-focus image fusion"> <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="multi-focus image fusion"> <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> 3186</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: multi-focus image fusion</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3186</span> Performance of Hybrid Image Fusion: Implementation of Dual-Tree Complex Wavelet Transform Technique </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Manoj%20Gupta">Manoj Gupta</a>, <a href="https://publications.waset.org/abstracts/search?q=Nirmendra%20Singh%20Bhadauria"> Nirmendra Singh Bhadauria</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Most of the applications in image processing require high spatial and high spectral resolution in a single image. For example satellite image system, the traffic monitoring system, and long range sensor fusion system all use image processing. However, most of the available equipment is not capable of providing this type of data. The sensor in the surveillance system can only cover the view of a small area for a particular focus, yet the demanding application of this system requires a view with a high coverage of the field. Image fusion provides the possibility of combining different sources of information. In this paper, we have decomposed the image using DTCWT and then fused using average and hybrid of (maxima and average) pixel level techniques and then compared quality of both the images using PSNR. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=image%20fusion" title="image fusion">image fusion</a>, <a href="https://publications.waset.org/abstracts/search?q=DWT" title=" DWT"> DWT</a>, <a href="https://publications.waset.org/abstracts/search?q=DT-CWT" title=" DT-CWT"> DT-CWT</a>, <a href="https://publications.waset.org/abstracts/search?q=PSNR" title=" PSNR"> PSNR</a>, <a href="https://publications.waset.org/abstracts/search?q=average%20image%20fusion" title=" average image fusion"> average image fusion</a>, <a href="https://publications.waset.org/abstracts/search?q=hybrid%20image%20fusion" title=" hybrid image fusion"> hybrid image fusion</a> </p> <a href="https://publications.waset.org/abstracts/19207/performance-of-hybrid-image-fusion-implementation-of-dual-tree-complex-wavelet-transform-technique" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/19207.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">606</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">3185</span> Efficient Feature Fusion for Noise Iris in Unconstrained Environment</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yao-Hong%20Tsai">Yao-Hong Tsai</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents an efficient fusion algorithm for iris images to generate stable feature for recognition in unconstrained environment. Recently, iris recognition systems are focused on real scenarios in our daily life without the subject’s cooperation. Under large variation in the environment, the objective of this paper is to combine information from multiple images of the same iris. The result of image fusion is a new image which is more stable for further iris recognition than each original noise iris image. A wavelet-based approach for multi-resolution image fusion is applied in the fusion process. The detection of the iris image is based on Adaboost algorithm and then local binary pattern (LBP) histogram is then applied to texture classification with the weighting scheme. Experiment showed that the generated features from the proposed fusion algorithm can improve the performance for verification system through iris recognition. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=image%20fusion" title="image fusion">image fusion</a>, <a href="https://publications.waset.org/abstracts/search?q=iris%20recognition" title=" iris recognition"> iris recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=local%20binary%20pattern" title=" local binary pattern"> local binary pattern</a>, <a href="https://publications.waset.org/abstracts/search?q=wavelet" title=" wavelet"> wavelet</a> </p> <a href="https://publications.waset.org/abstracts/17027/efficient-feature-fusion-for-noise-iris-in-unconstrained-environment" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/17027.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">367</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">3184</span> Adaptive Dehazing Using Fusion Strategy </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=M.%20Ramesh%20Kanthan">M. Ramesh Kanthan</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20Naga%20Nandini%20Sujatha"> S. Naga Nandini Sujatha</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The goal of haze removal algorithms is to enhance and recover details of scene from foggy image. In enhancement the proposed method focus into two main categories: (i) image enhancement based on Adaptive contrast Histogram equalization, and (ii) image edge strengthened Gradient model. Many circumstances accurate haze removal algorithms are needed. The de-fog feature works through a complex algorithm which first determines the fog destiny of the scene, then analyses the obscured image before applying contrast and sharpness adjustments to the video in real-time to produce image the fusion strategy is driven by the intrinsic properties of the original image and is highly dependent on the choice of the inputs and the weights. Then the output haze free image has reconstructed using fusion methodology. In order to increase the accuracy, interpolation method has used in the output reconstruction. A promising retrieval performance is achieved especially in particular examples. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=single%20image" title="single image">single image</a>, <a href="https://publications.waset.org/abstracts/search?q=fusion" title=" fusion"> fusion</a>, <a href="https://publications.waset.org/abstracts/search?q=dehazing" title=" dehazing"> dehazing</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-scale%20fusion" title=" multi-scale fusion"> multi-scale fusion</a>, <a href="https://publications.waset.org/abstracts/search?q=per-pixel" title=" per-pixel"> per-pixel</a>, <a href="https://publications.waset.org/abstracts/search?q=weight%20map" title=" weight map"> weight map</a> </p> <a href="https://publications.waset.org/abstracts/32544/adaptive-dehazing-using-fusion-strategy" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/32544.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">464</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">3183</span> Sampling Two-Channel Nonseparable Wavelets and Its Applications in Multispectral Image Fusion</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bin%20Liu">Bin Liu</a>, <a href="https://publications.waset.org/abstracts/search?q=Weijie%20Liu"> Weijie Liu</a>, <a href="https://publications.waset.org/abstracts/search?q=Bin%20Sun"> Bin Sun</a>, <a href="https://publications.waset.org/abstracts/search?q=Yihui%20Luo"> Yihui Luo </a> </p> <p class="card-text"><strong>Abstract:</strong></p> In order to solve the problem of lower spatial resolution and block effect in the fusion method based on separable wavelet transform in the resulting fusion image, a new sampling mode based on multi-resolution analysis of two-channel non separable wavelet transform, whose dilation matrix is [1,1;1,-1], is presented and a multispectral image fusion method based on this kind of sampling mode is proposed. Filter banks related to this kind of wavelet are constructed, and multiresolution decomposition of the intensity of the MS and panchromatic image are performed in the sampled mode using the constructed filter bank. The low- and high-frequency coefficients are fused by different fusion rules. The experiment results show that this method has good visual effect. The fusion performance has been noted to outperform the IHS fusion method, as well as, the fusion methods based on DWT, IHS-DWT, IHS-Contourlet transform, and IHS-Curvelet transform in preserving both spectral quality and high spatial resolution information. Furthermore, when compared with the fusion method based on nonsubsampled two-channel non separable wavelet, the proposed method has been observed to have higher spatial resolution and good global spectral information. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=image%20fusion" title="image fusion">image fusion</a>, <a href="https://publications.waset.org/abstracts/search?q=two-channel%20sampled%20nonseparable%20wavelets" title=" two-channel sampled nonseparable wavelets"> two-channel sampled nonseparable wavelets</a>, <a href="https://publications.waset.org/abstracts/search?q=multispectral%20image" title=" multispectral image"> multispectral image</a>, <a href="https://publications.waset.org/abstracts/search?q=panchromatic%20image" title=" panchromatic image"> panchromatic image</a> </p> <a href="https://publications.waset.org/abstracts/15357/sampling-two-channel-nonseparable-wavelets-and-its-applications-in-multispectral-image-fusion" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/15357.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">440</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">3182</span> A Multi Sensor Monochrome Video Fusion Using Image Quality Assessment</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=M.%20Prema%20Kumar">M. Prema Kumar</a>, <a href="https://publications.waset.org/abstracts/search?q=P.%20Rajesh%20Kumar"> P. Rajesh Kumar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The increasing interest in image fusion (combining images of two or more modalities such as infrared and visible light radiation) has led to a need for accurate and reliable image assessment methods. This paper gives a novel approach of merging the information content from several videos taken from the same scene in order to rack up a combined video that contains the finest information coming from different source videos. This process is known as video fusion which helps in providing superior quality (The term quality, connote measurement on the particular application.) image than the source images. In this technique different sensors (whose redundant information can be reduced) are used for various cameras that are imperative for capturing the required images and also help in reducing. In this paper Image fusion technique based on multi-resolution singular value decomposition (MSVD) has been used. The image fusion by MSVD is almost similar to that of wavelets. The idea behind MSVD is to replace the FIR filters in wavelet transform with singular value decomposition (SVD). It is computationally very simple and is well suited for real time applications like in remote sensing and in astronomy. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=multi%20sensor%20image%20fusion" title="multi sensor image fusion">multi sensor image fusion</a>, <a href="https://publications.waset.org/abstracts/search?q=MSVD" title=" MSVD"> MSVD</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=monochrome%20video" title=" monochrome video"> monochrome video</a> </p> <a href="https://publications.waset.org/abstracts/14866/a-multi-sensor-monochrome-video-fusion-using-image-quality-assessment" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/14866.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">572</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">3181</span> Multi-Focus Image Fusion Using SFM and Wavelet Packet</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Somkait%20Udomhunsakul">Somkait Udomhunsakul</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, a multi-focus image fusion method using Spatial Frequency Measurements (SFM) and Wavelet Packet was proposed. The proposed fusion approach, firstly, the two fused images were transformed and decomposed into sixteen subbands using Wavelet packet. Next, each subband was partitioned into sub-blocks and each block was identified the clearer regions by using the Spatial Frequency Measurement (SFM). Finally, the recovered fused image was reconstructed by performing the Inverse Wavelet Transform. From the experimental results, it was found that the proposed method outperformed the traditional SFM based methods in terms of objective and subjective assessments. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=multi-focus%20image%20fusion" title="multi-focus image fusion">multi-focus image fusion</a>, <a href="https://publications.waset.org/abstracts/search?q=wavelet%20packet" title=" wavelet packet"> wavelet packet</a>, <a href="https://publications.waset.org/abstracts/search?q=spatial%20frequency%20measurement" title=" spatial frequency measurement"> spatial frequency measurement</a> </p> <a href="https://publications.waset.org/abstracts/4886/multi-focus-image-fusion-using-sfm-and-wavelet-packet" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/4886.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">474</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">3180</span> Implementation and Comparative Analysis of PET and CT Image Fusion Algorithms</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=S.%20Guruprasad">S. Guruprasad</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Z.%20Kurian"> M. Z. Kurian</a>, <a href="https://publications.waset.org/abstracts/search?q=H.%20N.%20Suma"> H. N. Suma</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Medical imaging modalities are becoming life saving components. These modalities are very much essential to doctors for proper diagnosis, treatment planning and follow up. Some modalities provide anatomical information such as Computed Tomography (CT), Magnetic Resonance Imaging (MRI), X-rays and some provides only functional information such as Positron Emission Tomography (PET). Therefore, single modality image does not give complete information. This paper presents the fusion of structural information in CT and functional information present in PET image. This fused image is very much essential in detecting the stages and location of abnormalities and in particular very much needed in oncology for improved diagnosis and treatment. We have implemented and compared image fusion techniques like pyramid, wavelet, and principal components fusion methods along with hybrid method of DWT and PCA. The performances of the algorithms are evaluated quantitatively and qualitatively. The system is implemented and tested by using MATLAB software. Based on the MSE, PSNR and ENTROPY analysis, PCA and DWT-PCA methods showed best results over all experiments. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=image%20fusion" title="image fusion">image fusion</a>, <a href="https://publications.waset.org/abstracts/search?q=pyramid" title=" pyramid"> pyramid</a>, <a href="https://publications.waset.org/abstracts/search?q=wavelets" title=" wavelets"> wavelets</a>, <a href="https://publications.waset.org/abstracts/search?q=principal%20component%20analysis" title=" principal component analysis"> principal component analysis</a> </p> <a href="https://publications.waset.org/abstracts/60736/implementation-and-comparative-analysis-of-pet-and-ct-image-fusion-algorithms" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/60736.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">283</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">3179</span> Multi-Sensor Image Fusion for Visible and Infrared Thermal Images</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Amit%20Kumar%20Happy">Amit Kumar Happy</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper is motivated by the importance of multi-sensor image fusion with a specific focus on infrared (IR) and visual image (VI) fusion for various applications, including military reconnaissance. Image fusion can be defined as the process of combining two or more source images into a single composite image with extended information content that improves visual perception or feature extraction. These images can be from different modalities like visible camera & IR thermal imager. While visible images are captured by reflected radiations in the visible spectrum, the thermal images are formed from thermal radiation (infrared) that may be reflected or self-emitted. A digital color camera captures the visible source image, and a thermal infrared camera acquires the thermal source image. In this paper, some image fusion algorithms based upon multi-scale transform (MST) and region-based selection rule with consistency verification have been proposed and presented. This research includes the implementation of the proposed image fusion algorithm in MATLAB along with a comparative analysis to decide the optimum number of levels for MST and the coefficient fusion rule. The results are presented, and several commonly used evaluation metrics are used to assess the suggested method's validity. Experiments show that the proposed approach is capable of producing good fusion results. While deploying our image fusion algorithm approaches, we observe several challenges from the popular image fusion methods. While high computational cost and complex processing steps of image fusion algorithms provide accurate fused results, they also make it hard to become deployed in systems and applications that require a real-time operation, high flexibility, and low computation ability. So, the methods presented in this paper offer good results with minimum time complexity. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=image%20fusion" title="image fusion">image fusion</a>, <a href="https://publications.waset.org/abstracts/search?q=IR%20thermal%20imager" title=" IR thermal imager"> IR thermal imager</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-sensor" title=" multi-sensor"> multi-sensor</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-scale%20transform" title=" multi-scale transform"> multi-scale transform</a> </p> <a href="https://publications.waset.org/abstracts/138086/multi-sensor-image-fusion-for-visible-and-infrared-thermal-images" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/138086.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">115</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">3178</span> Medical Imaging Fusion: A Teaching-Learning Simulation Environment</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Cristina%20Maria%20Ribeiro%20Martins%20Pereira%20Caridade">Cristina Maria Ribeiro Martins Pereira Caridade</a>, <a href="https://publications.waset.org/abstracts/search?q=Ana%20Rita%20Ferreira%20Morais"> Ana Rita Ferreira Morais</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The use of computational tools has become essential in the context of interactive learning, especially in engineering education. In the medical industry, teaching medical image processing techniques is a crucial part of training biomedical engineers, as it has integrated applications with healthcare facilities and hospitals. The aim of this article is to present a teaching-learning simulation tool developed in MATLAB using a graphical user interface for medical image fusion that explores different image fusion methodologies and processes in combination with image pre-processing techniques. The application uses different algorithms and medical fusion techniques in real time, allowing you to view original images and fusion images, compare processed and original images, adjust parameters, and save images. The tool proposed in an innovative teaching and learning environment consists of a dynamic and motivating teaching simulation for biomedical engineering students to acquire knowledge about medical image fusion techniques and necessary skills for the training of biomedical engineers. In conclusion, the developed simulation tool provides real-time visualization of the original and fusion images and the possibility to test, evaluate and progress the student’s knowledge about the fusion of medical images. It also facilitates the exploration of medical imaging applications, specifically image fusion, which is critical in the medical industry. Teachers and students can make adjustments and/or create new functions, making the simulation environment adaptable to new techniques and methodologies. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=image%20fusion" title="image fusion">image fusion</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=teaching-learning%20simulation%20tool" title=" teaching-learning simulation tool"> teaching-learning simulation tool</a>, <a href="https://publications.waset.org/abstracts/search?q=biomedical%20engineering%20education" title=" biomedical engineering education"> biomedical engineering education</a> </p> <a href="https://publications.waset.org/abstracts/164987/medical-imaging-fusion-a-teaching-learning-simulation-environment" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/164987.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">131</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">3177</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">3176</span> Mage Fusion Based Eye Tumor Detection</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ahmed%20Ashit">Ahmed Ashit</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Image fusion is a significant and efficient image processing method used for detecting different types of tumors. This method has been used as an effective combination technique for obtaining high quality images that combine anatomy and physiology of an organ. It is the main key in the huge biomedical machines for diagnosing cancer such as PET-CT machine. This thesis aims to develop an image analysis system for the detection of the eye tumor. Different image processing methods are used to extract the tumor and then mark it on the original image. The images are first smoothed using median filtering. The background of the image is subtracted, to be then added to the original, results in a brighter area of interest or tumor area. The images are adjusted in order to increase the intensity of their pixels which lead to clearer and brighter images. once the images are enhanced, the edges of the images are detected using canny operators results in a segmented image comprises only of the pupil and the tumor for the abnormal images, and the pupil only for the normal images that have no tumor. The images of normal and abnormal images are collected from two sources: “Miles Research” and “Eye Cancer”. The computerized experimental results show that the developed image fusion based eye tumor detection system is capable of detecting the eye tumor and segment it to be superimposed on the original image. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=image%20fusion" title="image fusion">image fusion</a>, <a href="https://publications.waset.org/abstracts/search?q=eye%20tumor" title=" eye tumor"> eye tumor</a>, <a href="https://publications.waset.org/abstracts/search?q=canny%20operators" title=" canny operators"> canny operators</a>, <a href="https://publications.waset.org/abstracts/search?q=superimposed" title=" superimposed"> superimposed</a> </p> <a href="https://publications.waset.org/abstracts/30750/mage-fusion-based-eye-tumor-detection" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/30750.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">363</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">3175</span> Image Retrieval Based on Multi-Feature Fusion for Heterogeneous Image Databases</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=N.%20W.%20U.%20D.%20Chathurani">N. W. U. D. Chathurani</a>, <a href="https://publications.waset.org/abstracts/search?q=Shlomo%20Geva"> Shlomo Geva</a>, <a href="https://publications.waset.org/abstracts/search?q=Vinod%20Chandran"> Vinod Chandran</a>, <a href="https://publications.waset.org/abstracts/search?q=Proboda%20Rajapaksha"> Proboda Rajapaksha </a> </p> <p class="card-text"><strong>Abstract:</strong></p> Selecting an appropriate image representation is the most important factor in implementing an effective Content-Based Image Retrieval (CBIR) system. This paper presents a multi-feature fusion approach for efficient CBIR, based on the distance distribution of features and relative feature weights at the time of query processing. It is a simple yet effective approach, which is free from the effect of features' dimensions, ranges, internal feature normalization and the distance measure. This approach can easily be adopted in any feature combination to improve retrieval quality. The proposed approach is empirically evaluated using two benchmark datasets for image classification (a subset of the Corel dataset and Oliva and Torralba) and compared with existing approaches. The performance of the proposed approach is confirmed with the significantly improved performance in comparison with the independently evaluated baseline of the previously proposed feature fusion approaches. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=feature%20fusion" title="feature fusion">feature fusion</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20retrieval" title=" image retrieval"> image retrieval</a>, <a href="https://publications.waset.org/abstracts/search?q=membership%20function" title=" membership function"> membership function</a>, <a href="https://publications.waset.org/abstracts/search?q=normalization" title=" normalization"> normalization</a> </p> <a href="https://publications.waset.org/abstracts/52968/image-retrieval-based-on-multi-feature-fusion-for-heterogeneous-image-databases" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/52968.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">345</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">3174</span> A Hybrid Image Fusion Model for Generating High Spatial-Temporal-Spectral Resolution Data Using OLI-MODIS-Hyperion Satellite Imagery</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yongquan%20Zhao">Yongquan Zhao</a>, <a href="https://publications.waset.org/abstracts/search?q=Bo%20Huang"> Bo Huang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Spatial, Temporal, and Spectral Resolution (STSR) are three key characteristics of Earth observation satellite sensors; however, any single satellite sensor cannot provide Earth observations with high STSR simultaneously because of the hardware technology limitations of satellite sensors. On the other hand, a conflicting circumstance is that the demand for high STSR has been growing with the remote sensing application development. Although image fusion technology provides a feasible means to overcome the limitations of the current Earth observation data, the current fusion technologies cannot enhance all STSR simultaneously and provide high enough resolution improvement level. This study proposes a Hybrid Spatial-Temporal-Spectral image Fusion Model (HSTSFM) to generate synthetic satellite data with high STSR simultaneously, which blends the high spatial resolution from the panchromatic image of Landsat-8 Operational Land Imager (OLI), the high temporal resolution from the multi-spectral image of Moderate Resolution Imaging Spectroradiometer (MODIS), and the high spectral resolution from the hyper-spectral image of Hyperion to produce high STSR images. The proposed HSTSFM contains three fusion modules: (1) spatial-spectral image fusion; (2) spatial-temporal image fusion; (3) temporal-spectral image fusion. A set of test data with both phenological and land cover type changes in Beijing suburb area, China is adopted to demonstrate the performance of the proposed method. The experimental results indicate that HSTSFM can produce fused image that has good spatial and spectral fidelity to the reference image, which means it has the potential to generate synthetic data to support the studies that require high STSR satellite imagery. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=hybrid%20spatial-temporal-spectral%20fusion" title="hybrid spatial-temporal-spectral fusion">hybrid spatial-temporal-spectral fusion</a>, <a href="https://publications.waset.org/abstracts/search?q=high%20resolution%20synthetic%20imagery" title=" high resolution synthetic imagery"> high resolution synthetic imagery</a>, <a href="https://publications.waset.org/abstracts/search?q=least%20square%20regression" title=" least square regression"> least square regression</a>, <a href="https://publications.waset.org/abstracts/search?q=sparse%20representation" title=" sparse representation"> sparse representation</a>, <a href="https://publications.waset.org/abstracts/search?q=spectral%20transformation" title=" spectral transformation"> spectral transformation</a> </p> <a href="https://publications.waset.org/abstracts/74667/a-hybrid-image-fusion-model-for-generating-high-spatial-temporal-spectral-resolution-data-using-oli-modis-hyperion-satellite-imagery" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/74667.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">235</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">3173</span> Biimodal Biometrics System Using Fusion of Iris and Fingerprint</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Attallah%20Bilal">Attallah Bilal</a>, <a href="https://publications.waset.org/abstracts/search?q=Hendel%20Fatiha"> Hendel Fatiha</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper proposes the bimodal biometrics system for identity verification iris and fingerprint, at matching score level architecture using weighted sum of score technique. The features are extracted from the pre processed images of iris and fingerprint. These features of a query image are compared with those of a database image to obtain matching scores. The individual scores generated after matching are passed to the fusion module. This module consists of three major steps i.e., normalization, generation of similarity score and fusion of weighted scores. The final score is then used to declare the person as genuine or an impostor. The system is tested on CASIA database and gives an overall accuracy of 91.04% with FAR of 2.58% and FRR of 8.34%. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=iris" title="iris">iris</a>, <a href="https://publications.waset.org/abstracts/search?q=fingerprint" title=" fingerprint"> fingerprint</a>, <a href="https://publications.waset.org/abstracts/search?q=sum%20rule" title=" sum rule"> sum rule</a>, <a href="https://publications.waset.org/abstracts/search?q=fusion" title=" fusion"> fusion</a> </p> <a href="https://publications.waset.org/abstracts/18556/biimodal-biometrics-system-using-fusion-of-iris-and-fingerprint" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/18556.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">368</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">3172</span> Sparse Representation Based Spatiotemporal Fusion Employing Additional Image Pairs to Improve Dictionary Training</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Dacheng%20Li">Dacheng Li</a>, <a href="https://publications.waset.org/abstracts/search?q=Bo%20Huang"> Bo Huang</a>, <a href="https://publications.waset.org/abstracts/search?q=Qinjin%20Han"> Qinjin Han</a>, <a href="https://publications.waset.org/abstracts/search?q=Ming%20Li"> Ming Li</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Remotely sensed imagery with the high spatial and temporal characteristics, which it is hard to acquire under the current land observation satellites, has been considered as a key factor for monitoring environmental changes over both global and local scales. On a basis of the limited high spatial-resolution observations, challenged studies called spatiotemporal fusion have been developed for generating high spatiotemporal images through employing other auxiliary low spatial-resolution data while with high-frequency observations. However, a majority of spatiotemporal fusion approaches yield to satisfactory assumption, empirical but unstable parameters, low accuracy or inefficient performance. Although the spatiotemporal fusion methodology via sparse representation theory has advantage in capturing reflectance changes, stability and execution efficiency (even more efficient when overcomplete dictionaries have been pre-trained), the retrieval of high-accuracy dictionary and its response to fusion results are still pending issues. In this paper, we employ additional image pairs (here each image-pair includes a Landsat Operational Land Imager and a Moderate Resolution Imaging Spectroradiometer acquisitions covering the partial area of Baotou, China) only into the coupled dictionary training process based on K-SVD (K-means Singular Value Decomposition) algorithm, and attempt to improve the fusion results of two existing sparse representation based fusion models (respectively utilizing one and two available image-pair). The results show that more eligible image pairs are probably related to a more accurate overcomplete dictionary, which generally indicates a better image representation, and is then contribute to an effective fusion performance in case that the added image-pair has similar seasonal aspects and image spatial structure features to the original image-pair. It is, therefore, reasonable to construct multi-dictionary training pattern for generating a series of high spatial resolution images based on limited acquisitions. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=spatiotemporal%20fusion" title="spatiotemporal fusion">spatiotemporal fusion</a>, <a href="https://publications.waset.org/abstracts/search?q=sparse%20representation" title=" sparse representation"> sparse representation</a>, <a href="https://publications.waset.org/abstracts/search?q=K-SVD%20algorithm" title=" K-SVD algorithm"> K-SVD algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=dictionary%20learning" title=" dictionary learning"> dictionary learning</a> </p> <a href="https://publications.waset.org/abstracts/74785/sparse-representation-based-spatiotemporal-fusion-employing-additional-image-pairs-to-improve-dictionary-training" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/74785.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">260</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">3171</span> Color Image Enhancement Using Multiscale Retinex and Image Fusion Techniques</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Chang-Hsing%20Lee">Chang-Hsing Lee</a>, <a href="https://publications.waset.org/abstracts/search?q=Cheng-Chang%20Lien"> Cheng-Chang Lien</a>, <a href="https://publications.waset.org/abstracts/search?q=Chin-Chuan%20Han"> Chin-Chuan Han</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, an edge-strength guided multiscale retinex (EGMSR) approach will be proposed for color image contrast enhancement. In EGMSR, the pixel-dependent weight associated with each pixel in the single scale retinex output image is computed according to the edge strength around this pixel in order to prevent from over-enhancing the noises contained in the smooth dark/bright regions. Further, by fusing together the enhanced results of EGMSR and adaptive multiscale retinex (AMSR), we can get a natural fused image having high contrast and proper tonal rendition. Experimental results on several low-contrast images have shown that our proposed approach can produce natural and appealing enhanced images. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=image%20enhancement" title="image enhancement">image enhancement</a>, <a href="https://publications.waset.org/abstracts/search?q=multiscale%20retinex" title=" multiscale retinex"> multiscale retinex</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20fusion" title=" image fusion"> image fusion</a>, <a href="https://publications.waset.org/abstracts/search?q=EGMSR" title=" EGMSR"> EGMSR</a> </p> <a href="https://publications.waset.org/abstracts/15139/color-image-enhancement-using-multiscale-retinex-and-image-fusion-techniques" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/15139.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">458</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">3170</span> High-Capacity Image Steganography using Wavelet-based Fusion on Deep Convolutional Neural Networks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Amal%20Khalifa">Amal Khalifa</a>, <a href="https://publications.waset.org/abstracts/search?q=Nicolas%20Vana%20Santos"> Nicolas Vana Santos</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Steganography has been known for centuries as an efficient approach for covert communication. Due to its popularity and ease of access, image steganography has attracted researchers to find secure techniques for hiding information within an innocent looking cover image. In this research, we propose a novel deep-learning approach to digital image steganography. The proposed method, DeepWaveletFusion, uses convolutional neural networks (CNN) to hide a secret image into a cover image of the same size. Two CNNs are trained back-to-back to merge the Discrete Wavelet Transform (DWT) of both colored images and eventually be able to blindly extract the hidden image. Based on two different image similarity metrics, a weighted gain function is used to guide the learning process and maximize the quality of the retrieved secret image and yet maintaining acceptable imperceptibility. Experimental results verified the high recoverability of DeepWaveletFusion which outperformed similar deep-learning-based methods. <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=steganography" title=" steganography"> steganography</a>, <a href="https://publications.waset.org/abstracts/search?q=image" title=" image"> image</a>, <a href="https://publications.waset.org/abstracts/search?q=discrete%20wavelet%20transform" title=" discrete wavelet transform"> discrete wavelet transform</a>, <a href="https://publications.waset.org/abstracts/search?q=fusion" title=" fusion"> fusion</a> </p> <a href="https://publications.waset.org/abstracts/170293/high-capacity-image-steganography-using-wavelet-based-fusion-on-deep-convolutional-neural-networks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/170293.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">90</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">3169</span> Characterization of Inertial Confinement Fusion Targets Based on Transmission Holographic Mach-Zehnder Interferometer</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=B.%20Zare-Farsani">B. Zare-Farsani</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Valieghbal"> M. Valieghbal</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Tarkashvand"> M. Tarkashvand</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20H.%20Farahbod"> A. H. Farahbod</a> </p> <p class="card-text"><strong>Abstract:</strong></p> To provide the conditions for nuclear fusion by high energy and powerful laser beams, it is required to have a high degree of symmetry and surface uniformity of the spherical capsules to reduce the Rayleigh-Taylor hydrodynamic instabilities. In this paper, we have used the digital microscopic holography based on Mach-Zehnder interferometer to study the quality of targets for inertial fusion. The interferometric pattern of the target has been registered by a CCD camera and analyzed by Holovision software. The uniformity of the surface and shell thickness are investigated and measured in reconstructed image. We measured shell thickness in different zone where obtained non uniformity 22.82 percent.   <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=inertial%20confinement%20fusion" title="inertial confinement fusion">inertial confinement fusion</a>, <a href="https://publications.waset.org/abstracts/search?q=mach-zehnder%20interferometer" title=" mach-zehnder interferometer"> mach-zehnder interferometer</a>, <a href="https://publications.waset.org/abstracts/search?q=digital%20holographic%20microscopy" title=" digital holographic microscopy"> digital holographic microscopy</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20reconstruction" title=" image reconstruction"> image reconstruction</a>, <a href="https://publications.waset.org/abstracts/search?q=holovision" title=" holovision"> holovision</a> </p> <a href="https://publications.waset.org/abstracts/45440/characterization-of-inertial-confinement-fusion-targets-based-on-transmission-holographic-mach-zehnder-interferometer" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/45440.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">304</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">3168</span> Multiple Fusion Based Single Image Dehazing</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Joe%20Amalraj">Joe Amalraj</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Arunkumar"> M. Arunkumar </a> </p> <p class="card-text"><strong>Abstract:</strong></p> Haze is an atmospheric phenomenon that signicantly degrades the visibility of outdoor scenes. This is mainly due to the atmosphere particles that absorb and scatter the light. This paper introduces a novel single image approach that enhances the visibility of such degraded images. In this method is a fusion-based strategy that derives from two original hazy image inputs by applying a white balance and a contrast enhancing procedure. To blend effectively the information of the derived inputs to preserve the regions with good visibility, we filter their important features by computing three measures (weight maps): luminance, chromaticity, and saliency. To minimize artifacts introduced by the weight maps, our approach is designed in a multiscale fashion, using a Laplacian pyramid representation. This paper demonstrates the utility and effectiveness of a fusion-based technique for de-hazing based on a single degraded image. The method performs in a per-pixel fashion, which is straightforward to implement. The experimental results demonstrate that the method yields results comparative to and even better than the more complex state-of-the-art techniques, having the advantage of being appropriate for real-time applications. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=single%20image%20de-hazing" title="single image de-hazing">single image de-hazing</a>, <a href="https://publications.waset.org/abstracts/search?q=outdoor%20images" title=" outdoor images"> outdoor images</a>, <a href="https://publications.waset.org/abstracts/search?q=enhancing" title=" enhancing"> enhancing</a>, <a href="https://publications.waset.org/abstracts/search?q=DSP" title=" DSP"> DSP</a> </p> <a href="https://publications.waset.org/abstracts/38475/multiple-fusion-based-single-image-dehazing" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/38475.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">410</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">3167</span> Evaluation of Fusion Sonar and Stereo Camera System for 3D Reconstruction of Underwater Archaeological Object</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yadpiroon%20Onmek">Yadpiroon Onmek</a>, <a href="https://publications.waset.org/abstracts/search?q=Jean%20Triboulet"> Jean Triboulet</a>, <a href="https://publications.waset.org/abstracts/search?q=Sebastien%20Druon"> Sebastien Druon</a>, <a href="https://publications.waset.org/abstracts/search?q=Bruno%20Jouvencel"> Bruno Jouvencel</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The objective of this paper is to develop the 3D underwater reconstruction of archaeology object, which is based on the fusion between a sonar system and stereo camera system. The underwater images are obtained from a calibrated camera system. The multiples image pairs are input, and we first solve the problem of image processing by applying the well-known filter, therefore to improve the quality of underwater images. The features of interest between image pairs are selected by well-known methods: a FAST detector and FLANN descriptor. Subsequently, the RANSAC method is applied to reject outlier points. The putative inliers are matched by triangulation to produce the local sparse point clouds in 3D space, using a pinhole camera model and Euclidean distance estimation. The SFM technique is used to carry out the global sparse point clouds. Finally, the ICP method is used to fusion the sonar information with the stereo model. The final 3D models have a précised by measurement comparing with the real object. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=3D%20reconstruction" title="3D reconstruction">3D reconstruction</a>, <a href="https://publications.waset.org/abstracts/search?q=archaeology" title=" archaeology"> archaeology</a>, <a href="https://publications.waset.org/abstracts/search?q=fusion" title=" fusion"> fusion</a>, <a href="https://publications.waset.org/abstracts/search?q=stereo%20system" title=" stereo system"> stereo system</a>, <a href="https://publications.waset.org/abstracts/search?q=sonar%20system" title=" sonar system"> sonar system</a>, <a href="https://publications.waset.org/abstracts/search?q=underwater" title=" underwater"> underwater</a> </p> <a href="https://publications.waset.org/abstracts/73700/evaluation-of-fusion-sonar-and-stereo-camera-system-for-3d-reconstruction-of-underwater-archaeological-object" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/73700.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">299</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">3166</span> Keypoint Detection Method Based on Multi-Scale Feature Fusion of Attention Mechanism</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Xiaoxiao%20Li">Xiaoxiao Li</a>, <a href="https://publications.waset.org/abstracts/search?q=Shuangcheng%20Jia"> Shuangcheng Jia</a>, <a href="https://publications.waset.org/abstracts/search?q=Qian%20Li"> Qian Li</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Keypoint detection has always been a challenge in the field of image recognition. This paper proposes a novelty keypoint detection method which is called Multi-Scale Feature Fusion Convolutional Network with Attention (MFFCNA). We verified that the multi-scale features with the attention mechanism module have better feature expression capability. The feature fusion between different scales makes the information that the network model can express more abundant, and the network is easier to converge. On our self-made street sign corner dataset, we validate the MFFCNA model with an accuracy of 97.8% and a recall of 81%, which are 5 and 8 percentage points higher than the HRNet network, respectively. On the COCO dataset, the AP is 71.9%, and the AR is 75.3%, which are 3 points and 2 points higher than HRNet, respectively. Extensive experiments show that our method has a remarkable improvement in the keypoint recognition tasks, and the recognition effect is better than the existing methods. Moreover, our method can be applied not only to keypoint detection but also to image classification and semantic segmentation with good generality. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=keypoint%20detection" title="keypoint detection">keypoint detection</a>, <a href="https://publications.waset.org/abstracts/search?q=feature%20fusion" title=" feature fusion"> feature fusion</a>, <a href="https://publications.waset.org/abstracts/search?q=attention" title=" attention"> attention</a>, <a href="https://publications.waset.org/abstracts/search?q=semantic%20segmentation" title=" semantic segmentation"> semantic segmentation</a> </p> <a href="https://publications.waset.org/abstracts/147796/keypoint-detection-method-based-on-multi-scale-feature-fusion-of-attention-mechanism" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/147796.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">119</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">3165</span> Variations in the Angulation of the First Sacral Spinous Process Angle Associated with Sacrocaudal Fusion in Greyhounds</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sa%27ad%20M.%20Ismail">Sa'ad M. Ismail</a>, <a href="https://publications.waset.org/abstracts/search?q=Hung-Hsun%20Yen"> Hung-Hsun Yen</a>, <a href="https://publications.waset.org/abstracts/search?q=Christina%20M.%20Murray"> Christina M. Murray</a>, <a href="https://publications.waset.org/abstracts/search?q=Helen%20M.%20S.%20Davies"> Helen M. S. Davies</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In the dog, the median sacral crest is formed by the fusion of three sacral spinous processes. In greyhounds with standard sacrums, this fusion in the median sacral crest consists of the fusion of three sacral spinous processes while it consists of four in greyhounds with sacrocaudal fusion. In the present study, variations in the angulation of the first sacral spinous process in association with different types of sacrocaudal fusion in the greyhound were investigated. Sacrums were collected from 207 greyhounds (102 sacrums; type A (unfused) and 105 with different types of sacrocaudal fusion; types: B, C and D). Sacrums were cleaned by boiling and dried and then were placed on their ventral surface on a flat surface and photographed from the left side using a digital camera at a fixed distance. The first sacral spinous process angle (1st SPA) was defined as the angle formed between the cranial border of the cranial ridge of the first sacral spinous process and the line extending across the most dorsal surface points of the spinous processes of the S1, S2, and S3. Image-Pro Express Version 5.0 imaging software was used to draw and measure the angles. Two photographs were taken for each sacrum and two repeat measurements were also taken of each angle. The mean value of the 1st SPA in greyhounds with sacrocaudal fusion was less (98.99°, SD ± 11, n = 105) than those in greyhounds with standard sacrums (99.77°, SD ± 9.18, n = 102) but was not significantly different (P < 0.05). Among greyhounds with different types of sacrocaudal fusion the mean value of the 1st SPA was as follows: type B; 97.73°, SD ± 10.94, n = 39, type C: 101.42°, SD ± 10.51, n = 52, and type D: 94.22°, SD ± 11.30, n = 12. For all types of fusion these angles were significantly different from each other (P < 0.05). Comparing the mean value of the1st SPA in standard sacrums (Type A) with that for each type of fusion separately showed that the only significantly different angulation (P < 0.05) was between standard sacrums and sacrums with sacrocaudal fusion sacrum type D (only body fusion between the S1 and Ca1). Different types of sacrocaudal fusion were associated with variations in the angle of the first sacral spinous process. These variations may affect the alignment and biomechanics of the sacral area and the pattern of movement and/or the force produced by both hind limbs to the cranial parts of the body and may alter the loading of other parts of the body. We concluded that any variations in the sacrum anatomical features might change the function of the sacrum or surrounding anatomical structures during movement. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=angulation%20of%20first%20sacral%20spinous%20process" title="angulation of first sacral spinous process">angulation of first sacral spinous process</a>, <a href="https://publications.waset.org/abstracts/search?q=biomechanics" title=" biomechanics"> biomechanics</a>, <a href="https://publications.waset.org/abstracts/search?q=greyhound" title=" greyhound"> greyhound</a>, <a href="https://publications.waset.org/abstracts/search?q=locomotion" title=" locomotion"> locomotion</a>, <a href="https://publications.waset.org/abstracts/search?q=sacrocaudal%20fusion" title=" sacrocaudal fusion"> sacrocaudal fusion</a> </p> <a href="https://publications.waset.org/abstracts/74942/variations-in-the-angulation-of-the-first-sacral-spinous-process-angle-associated-with-sacrocaudal-fusion-in-greyhounds" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/74942.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">311</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">3164</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">3163</span> Preprocessing and Fusion of Multiple Representation of Finger Vein patterns using Conventional and Machine Learning techniques</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Tomas%20Trainys">Tomas Trainys</a>, <a href="https://publications.waset.org/abstracts/search?q=Algimantas%20Venckauskas"> Algimantas Venckauskas</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Application of biometric features to the cryptography for human identification and authentication is widely studied and promising area of the development of high-reliability cryptosystems. Biometric cryptosystems typically are designed for patterns recognition, which allows biometric data acquisition from an individual, extracts feature sets, compares the feature set against the set stored in the vault and gives a result of the comparison. Preprocessing and fusion of biometric data are the most important phases in generating a feature vector for key generation or authentication. Fusion of biometric features is critical for achieving a higher level of security and prevents from possible spoofing attacks. The paper focuses on the tasks of initial processing and fusion of multiple representations of finger vein modality patterns. These tasks are solved by applying conventional image preprocessing methods and machine learning techniques, Convolutional Neural Network (SVM) method for image segmentation and feature extraction. An article presents a method for generating sets of biometric features from a finger vein network using several instances of the same modality. Extracted features sets were fused at the feature level. The proposed method was tested and compared with the performance and accuracy results of other authors. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=bio-cryptography" title="bio-cryptography">bio-cryptography</a>, <a href="https://publications.waset.org/abstracts/search?q=biometrics" title=" biometrics"> biometrics</a>, <a href="https://publications.waset.org/abstracts/search?q=cryptographic%20key%20generation" title=" cryptographic key generation"> cryptographic key generation</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20fusion" title=" data fusion"> data fusion</a>, <a href="https://publications.waset.org/abstracts/search?q=information%20security" title=" information security"> information security</a>, <a href="https://publications.waset.org/abstracts/search?q=SVM" title=" SVM"> SVM</a>, <a href="https://publications.waset.org/abstracts/search?q=pattern%20recognition" title=" pattern recognition"> pattern recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=finger%20vein%20method." title=" finger vein method."> finger vein method.</a> </p> <a href="https://publications.waset.org/abstracts/97366/preprocessing-and-fusion-of-multiple-representation-of-finger-vein-patterns-using-conventional-and-machine-learning-techniques" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/97366.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">150</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">3162</span> The Optimization of Decision Rules in Multimodal Decision-Level Fusion Scheme</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Andrey%20V.%20Timofeev">Andrey V. Timofeev</a>, <a href="https://publications.waset.org/abstracts/search?q=Dmitry%20V.%20Egorov"> Dmitry V. Egorov</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper introduces an original method of parametric optimization of the structure for multimodal decision-level fusion scheme which combines the results of the partial solution of the classification task obtained from assembly of the mono-modal classifiers. As a result, a multimodal fusion classifier which has the minimum value of the total error rate has been obtained. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=classification%20accuracy" title="classification accuracy">classification accuracy</a>, <a href="https://publications.waset.org/abstracts/search?q=fusion%20solution" title=" fusion solution"> fusion solution</a>, <a href="https://publications.waset.org/abstracts/search?q=total%20error%20rate" title=" total error rate"> total error rate</a>, <a href="https://publications.waset.org/abstracts/search?q=multimodal%20fusion%20classifier" title=" multimodal fusion classifier"> multimodal fusion classifier</a> </p> <a href="https://publications.waset.org/abstracts/26088/the-optimization-of-decision-rules-in-multimodal-decision-level-fusion-scheme" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/26088.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">466</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">3161</span> Age Determination from Epiphyseal Union of Bones at Shoulder Joint in Girls of Central India</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=B.%20Tirpude">B. Tirpude</a>, <a href="https://publications.waset.org/abstracts/search?q=V.%20Surwade"> V. Surwade</a>, <a href="https://publications.waset.org/abstracts/search?q=P.%20Murkey"> P. Murkey</a>, <a href="https://publications.waset.org/abstracts/search?q=P.%20Wankhade"> P. Wankhade</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20Meena"> S. Meena </a> </p> <p class="card-text"><strong>Abstract:</strong></p> There is no statistical data to establish variation in epiphyseal fusion in girls in central India population. This significant oversight can lead to exclusion of persons of interest in a forensic investigation. Epiphyseal fusion of proximal end of humerus in eighty females were analyzed on radiological basis to assess the range of variation of epiphyseal fusion at each age. In the study, the X ray films of the subjects were divided into three groups on the basis of degree of fusion. Firstly, those which were showing No Epiphyseal Fusion (N), secondly those showing Partial Union (PC), and thirdly those showing Complete Fusion (C). Observations made were compared with the previous studies. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=epiphyseal%20union" title="epiphyseal union">epiphyseal union</a>, <a href="https://publications.waset.org/abstracts/search?q=shoulder%20joint" title=" shoulder joint"> shoulder joint</a>, <a href="https://publications.waset.org/abstracts/search?q=proximal%20end%20of%20humerus" title=" proximal end of humerus"> proximal end of humerus</a> </p> <a href="https://publications.waset.org/abstracts/19684/age-determination-from-epiphyseal-union-of-bones-at-shoulder-joint-in-girls-of-central-india" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/19684.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">495</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">3160</span> Reliable Soup: Reliable-Driven Model Weight Fusion on Ultrasound Imaging Classification</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Shuge%20Lei">Shuge Lei</a>, <a href="https://publications.waset.org/abstracts/search?q=Haonan%20Hu"> Haonan Hu</a>, <a href="https://publications.waset.org/abstracts/search?q=Dasheng%20Sun"> Dasheng Sun</a>, <a href="https://publications.waset.org/abstracts/search?q=Huabin%20Zhang"> Huabin Zhang</a>, <a href="https://publications.waset.org/abstracts/search?q=Kehong%20Yuan"> Kehong Yuan</a>, <a href="https://publications.waset.org/abstracts/search?q=Jian%20Dai"> Jian Dai</a>, <a href="https://publications.waset.org/abstracts/search?q=Yan%20Tong"> Yan Tong</a> </p> <p class="card-text"><strong>Abstract:</strong></p> It remains challenging to measure reliability from classification results from different machine learning models. This paper proposes a reliable soup optimization algorithm based on the model weight fusion algorithm Model Soup, aiming to improve reliability by using dual-channel reliability as the objective function to fuse a series of weights in the breast ultrasound classification models. Experimental results on breast ultrasound clinical datasets demonstrate that reliable soup significantly enhances the reliability of breast ultrasound image classification tasks. The effectiveness of the proposed approach was verified via multicenter trials. The results from five centers indicate that the reliability optimization algorithm can enhance the reliability of the breast ultrasound image classification model and exhibit low multicenter correlation. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=breast%20ultrasound%20image%20classification" title="breast ultrasound image classification">breast ultrasound image classification</a>, <a href="https://publications.waset.org/abstracts/search?q=feature%20attribution" title=" feature attribution"> feature attribution</a>, <a href="https://publications.waset.org/abstracts/search?q=reliability%20assessment" title=" reliability assessment"> reliability assessment</a>, <a href="https://publications.waset.org/abstracts/search?q=reliability%20optimization" title=" reliability optimization"> reliability optimization</a> </p> <a href="https://publications.waset.org/abstracts/176773/reliable-soup-reliable-driven-model-weight-fusion-on-ultrasound-imaging-classification" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/176773.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">85</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">3159</span> Changes in the Median Sacral Crest Associated with Sacrocaudal Fusion in the Greyhound</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=S.%20M.%20Ismail">S. M. Ismail</a>, <a href="https://publications.waset.org/abstracts/search?q=H-H%20Yen"> H-H Yen</a>, <a href="https://publications.waset.org/abstracts/search?q=C.%20M.%20Murray"> C. M. Murray</a>, <a href="https://publications.waset.org/abstracts/search?q=H.%20M.%20S.%20Davies"> H. M. S. Davies</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A recent study reported a 33% incidence of complete sacrocaudal fusion in greyhounds compared to a 3% incidence in other dogs. In the dog, the median sacral crest is formed by the fusion of sacral spinous processes. Separation of the 1st spinous process from the median crest of the sacrum in the dog has been reported as a diagnostic tool of type one lumbosacral transitional vertebra (LTV). LTV is a congenital spinal anomaly, which includes either sacralization of the caudal lumbar part or lumbarization of the most cranial sacral segment of the spine. In this study, the absence or reduction of fusion (presence of separation) between the 1st and 2ndspinous processes of the median sacral crest has been identified in association with sacrocaudal fusion in the greyhound, without any feature of LTV. In order to provide quantitative data on the absence or reduction of fusion in the median sacral crest between the 1st and 2nd sacral spinous processes, in association with sacrocaudal fusion. 204 dog sacrums free of any pathological changes (192 greyhound, 9 beagles and 3 labradors) were grouped based on the occurrence and types of fusion and the presence, absence, or reduction in the median sacral crest between the 1st and 2nd sacral spinous processes., Sacrums were described and classified as follows: F: Complete fusion (crest is present), N: Absence (fusion is absent), and R: Short crest (fusion reduced but not absent (reduction). The incidence of sacrocaudal fusion in the 204 sacrums: 57% of the sacrums were standard (3 vertebrae) and 43% were fused (4 vertebrae). Type of sacrum had a significant (p < .05) association with the absence and reduction of fusion between the 1st and 2nd sacral spinous processes of the median sacral crest. In the 108 greyhounds with standard sacrums (3 vertebrae) the percentages of F, N and R were 45% 23% and 23% respectively, while in the 84 fused (4 vertebrae) sacrums, the percentages of F, N and R were 3%, 87% and 10% respectively and these percentages were significantly different between standard (3 vertebrae) and fused (4 vertebrae) sacrums (p < .05). This indicates that absence of spinous process fusion in the median sacral crest was found in a large percentage of the greyhounds in this study and was found to be particularly prevalent in those with sacrocaudal fusion – therefore in this breed, at least, absence of sacral spinous process fusion may be unlikely to be associated with LTV. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=greyhound" title="greyhound">greyhound</a>, <a href="https://publications.waset.org/abstracts/search?q=median%20sacral%20crest" title=" median sacral crest"> median sacral crest</a>, <a href="https://publications.waset.org/abstracts/search?q=sacrocaudal%20fusion" title=" sacrocaudal fusion"> sacrocaudal fusion</a>, <a href="https://publications.waset.org/abstracts/search?q=sacral%20spinous%20process" title=" sacral spinous process"> sacral spinous process</a> </p> <a href="https://publications.waset.org/abstracts/47980/changes-in-the-median-sacral-crest-associated-with-sacrocaudal-fusion-in-the-greyhound" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/47980.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">446</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">3158</span> Implementation of Sensor Fusion Structure of 9-Axis Sensors on the Multipoint Control Unit</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jun%20Gil%20Ahn">Jun Gil Ahn</a>, <a href="https://publications.waset.org/abstracts/search?q=Jong%20Tae%20Kim"> Jong Tae Kim</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we study the sensor fusion structure on the multipoint control unit (MCU). Sensor fusion using Kalman filter for 9-axis sensors is considered. The 9-axis inertial sensor is the combination of 3-axis accelerometer, 3-axis gyroscope and 3-axis magnetometer. We implement the sensor fusion structure among the sensor hubs in MCU and measure the execution time, power consumptions, and total energy. Experiments with real data from 9-axis sensor in 20Mhz show that the average power consumptions are 44mW and 48mW on Cortx-M0 and Cortex-M3 MCU, respectively. Execution times are 613.03 us and 305.6 us respectively. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=9-axis%20sensor" title="9-axis sensor">9-axis sensor</a>, <a href="https://publications.waset.org/abstracts/search?q=Kalman%20filter" title=" Kalman filter"> Kalman filter</a>, <a href="https://publications.waset.org/abstracts/search?q=MCU" title=" MCU"> MCU</a>, <a href="https://publications.waset.org/abstracts/search?q=sensor%20fusion" title=" sensor fusion"> sensor fusion</a> </p> <a href="https://publications.waset.org/abstracts/84323/implementation-of-sensor-fusion-structure-of-9-axis-sensors-on-the-multipoint-control-unit" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/84323.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">504</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">3157</span> Liver Tumor Detection by Classification through FD Enhancement of CT Image</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=N.%20Ghatwary">N. Ghatwary</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Ahmed"> A. Ahmed</a>, <a href="https://publications.waset.org/abstracts/search?q=H.%20Jalab"> H. Jalab</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, an approach for the liver tumor detection in computed tomography (CT) images is represented. The detection process is based on classifying the features of target liver cell to either tumor or non-tumor. Fractional differential (FD) is applied for enhancement of Liver CT images, with the aim of enhancing texture and edge features. Later on, a fusion method is applied to merge between the various enhanced images and produce a variety of feature improvement, which will increase the accuracy of classification. Each image is divided into NxN non-overlapping blocks, to extract the desired features. Support vector machines (SVM) classifier is trained later on a supplied dataset different from the tested one. Finally, the block cells are identified whether they are classified as tumor or not. Our approach is validated on a group of patients’ CT liver tumor datasets. The experiment results demonstrated the efficiency of detection in the proposed technique. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=fractional%20differential%20%28FD%29" title="fractional differential (FD)">fractional differential (FD)</a>, <a href="https://publications.waset.org/abstracts/search?q=computed%20tomography%20%28CT%29" title=" computed tomography (CT)"> computed tomography (CT)</a>, <a href="https://publications.waset.org/abstracts/search?q=fusion" title=" fusion"> fusion</a>, <a href="https://publications.waset.org/abstracts/search?q=aplha" title=" aplha"> aplha</a>, <a href="https://publications.waset.org/abstracts/search?q=texture%20features." title=" texture features."> texture features.</a> </p> <a href="https://publications.waset.org/abstracts/39719/liver-tumor-detection-by-classification-through-fd-enhancement-of-ct-image" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/39719.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">358</span> </span> </div> </div> <ul class="pagination"> <li class="page-item disabled"><span class="page-link">&lsaquo;</span></li> <li class="page-item active"><span class="page-link">1</span></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=multi-focus%20image%20fusion&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=multi-focus%20image%20fusion&page=3">3</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=multi-focus%20image%20fusion&page=4">4</a></li> <li class="page-item"><a class="page-link" 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