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texture</title> <meta name="description" content="Search results for: statistical texture"> <meta name="keywords" content="statistical texture"> <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 d-lg-none 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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="statistical texture"> <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> 4527</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: statistical texture</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4527</span> Texture and Twinning in Selective Laser Melting Ti-6Al-4V Alloys</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=N.%20Kazantseva">N. Kazantseva</a>, <a href="https://publications.waset.org/abstracts/search?q=P.%20Krakhmalev"> P. Krakhmalev</a>, <a href="https://publications.waset.org/abstracts/search?q=I.%20Yadroitsev"> I. Yadroitsev</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Fefelov"> A. Fefelov</a>, <a href="https://publications.waset.org/abstracts/search?q=N.%20Vinogradova"> N. Vinogradova</a>, <a href="https://publications.waset.org/abstracts/search?q=I.%20Ezhov"> I. Ezhov</a>, <a href="https://publications.waset.org/abstracts/search?q=T.%20Kurennykh"> T. Kurennykh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Martensitic texture-phase transition in Selective Laser Melting (SLM) Ti-6Al-4V (ELI) alloys was found. Electron Backscatter Diffraction (EBSD) analysis showed the initial cubic beta < 100 > (001) BCC texture. Such kind of texture is observed in BCC metals with flat rolling texture when axis is in the direction of rolling and the texture plane coincides with the plane of rolling. It was found that the texture of the parent BCC beta-phase determined the texture of low-temperature HCP alpha-phase limited the choice of its orientation variants. The {10-12} < -1011 > twinning system in titanium alloys after SLM was determined. Analysis of the oxygen contamination in SLM alloys was done. Comparison of the obtained results with the conventional titanium alloys is also provided. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=additive%20technology" title="additive technology">additive technology</a>, <a href="https://publications.waset.org/abstracts/search?q=texture" title=" texture"> texture</a>, <a href="https://publications.waset.org/abstracts/search?q=twins" title=" twins"> twins</a>, <a href="https://publications.waset.org/abstracts/search?q=Ti-6Al-4V" title=" Ti-6Al-4V"> Ti-6Al-4V</a>, <a href="https://publications.waset.org/abstracts/search?q=oxygen%20content" title=" oxygen content"> oxygen content</a> </p> <a href="https://publications.waset.org/abstracts/63604/texture-and-twinning-in-selective-laser-melting-ti-6al-4v-alloys" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/63604.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">637</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">4526</span> Texture Identification Using Vision System: A Method to Predict Functionality of a Component</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Varsha%20Singh">Varsha Singh</a>, <a href="https://publications.waset.org/abstracts/search?q=Shraddha%20Prajapati"> Shraddha Prajapati</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20B.%20Kiran"> M. B. Kiran</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Texture identification is useful in predicting the functionality of a component. Many of the existing texture identification methods are of contact in nature, which limits its measuring speed. These contact measurement techniques use a diamond stylus and the diamond stylus being sharp going to damage the surface under inspection and hence these techniques can be used in statistical sampling. Though these contact methods are very accurate, they do not give complete information for full characterization of surface. In this context, the presented method assumes special significance. The method uses a relatively low cost vision system for image acquisition. Software is developed based on wavelet transform, for analyzing texture images. Specimens are made using different manufacturing process (shaping, grinding, milling etc.) During experimentation, the specimens are illuminated using proper lighting and texture images a capture using CCD camera connected to the vision system. The software installed in the vision system processes these images and subsequently identify the texture of manufacturing processes. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=diamond%20stylus" title="diamond stylus">diamond stylus</a>, <a href="https://publications.waset.org/abstracts/search?q=manufacturing%20process" title=" manufacturing process"> manufacturing process</a>, <a href="https://publications.waset.org/abstracts/search?q=texture%20identification" title=" texture identification"> texture identification</a>, <a href="https://publications.waset.org/abstracts/search?q=vision%20system" title=" vision system"> vision system</a> </p> <a href="https://publications.waset.org/abstracts/61722/texture-identification-using-vision-system-a-method-to-predict-functionality-of-a-component" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/61722.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">289</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">4525</span> A Biologically Inspired Approach to Automatic Classification of Textile Fabric Prints Based On Both Texture and Colour Information</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Babar%20Khan">Babar Khan</a>, <a href="https://publications.waset.org/abstracts/search?q=Wang%20Zhijie"> Wang Zhijie</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Machine Vision has been playing a significant role in Industrial Automation, to imitate the wide variety of human functions, providing improved safety, reduced labour cost, the elimination of human error and/or subjective judgments, and the creation of timely statistical product data. Despite the intensive research, there have not been any attempts to classify fabric prints based on printed texture and colour, most of the researches so far encompasses only black and white or grey scale images. We proposed a biologically inspired processing architecture to classify fabrics w.r.t. the fabric print texture and colour. We created a texture descriptor based on the HMAX model for machine vision, and incorporated colour descriptor based on opponent colour channels simulating the single opponent and double opponent neuronal function of the brain. We found that our algorithm not only outperformed the original HMAX algorithm on classification of fabric print texture and colour, but we also achieved a recognition accuracy of 85-100% on different colour and different texture fabric. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=automatic%20classification" title="automatic classification">automatic classification</a>, <a href="https://publications.waset.org/abstracts/search?q=texture%20descriptor" title=" texture descriptor"> texture descriptor</a>, <a href="https://publications.waset.org/abstracts/search?q=colour%20descriptor" title=" colour descriptor"> colour descriptor</a>, <a href="https://publications.waset.org/abstracts/search?q=opponent%20colour%20channel" title=" opponent colour channel"> opponent colour channel</a> </p> <a href="https://publications.waset.org/abstracts/31715/a-biologically-inspired-approach-to-automatic-classification-of-textile-fabric-prints-based-on-both-texture-and-colour-information" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/31715.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">485</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">4524</span> Statistical Feature Extraction Method for Wood Species Recognition System </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mohd%20Iz%27aan%20Paiz%20Bin%20Zamri">Mohd Iz'aan Paiz Bin Zamri</a>, <a href="https://publications.waset.org/abstracts/search?q=Anis%20Salwa%20Mohd%20Khairuddin"> Anis Salwa Mohd Khairuddin</a>, <a href="https://publications.waset.org/abstracts/search?q=Norrima%20Mokhtar"> Norrima Mokhtar</a>, <a href="https://publications.waset.org/abstracts/search?q=Rubiyah%20Yusof"> Rubiyah Yusof</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Effective statistical feature extraction and classification are important in image-based automatic inspection and analysis. An automatic wood species recognition system is designed to perform wood inspection at custom checkpoints to avoid mislabeling of timber which will results to loss of income to the timber industry. The system focuses on analyzing the statistical pores properties of the wood images. This paper proposed a fuzzy-based feature extractor which mimics the experts’ knowledge on wood texture to extract the properties of pores distribution from the wood surface texture. The proposed feature extractor consists of two steps namely pores extraction and fuzzy pores management. The total number of statistical features extracted from each wood image is 38 features. Then, a backpropagation neural network is used to classify the wood species based on the statistical features. A comprehensive set of experiments on a database composed of 5200 macroscopic images from 52 tropical wood species was used to evaluate the performance of the proposed feature extractor. The advantage of the proposed feature extraction technique is that it mimics the experts’ interpretation on wood texture which allows human involvement when analyzing the wood texture. Experimental results show the efficiency of the proposed method. <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=feature%20extraction" title=" feature extraction"> feature extraction</a>, <a href="https://publications.waset.org/abstracts/search?q=fuzzy" title=" fuzzy"> fuzzy</a>, <a href="https://publications.waset.org/abstracts/search?q=inspection%20system" title=" inspection system"> inspection system</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20analysis" title=" image analysis"> image analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=macroscopic%20images" title=" macroscopic images"> macroscopic images</a> </p> <a href="https://publications.waset.org/abstracts/36415/statistical-feature-extraction-method-for-wood-species-recognition-system" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/36415.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">4523</span> Evaluation of Robust Feature Descriptors for Texture Classification</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jia-Hong%20Lee">Jia-Hong Lee</a>, <a href="https://publications.waset.org/abstracts/search?q=Mei-Yi%20Wu"> Mei-Yi Wu</a>, <a href="https://publications.waset.org/abstracts/search?q=Hsien-Tsung%20Kuo"> Hsien-Tsung Kuo</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Texture is an important characteristic in real and synthetic scenes. Texture analysis plays a critical role in inspecting surfaces and provides important techniques in a variety of applications. Although several descriptors have been presented to extract texture features, the development of object recognition is still a difficult task due to the complex aspects of texture. Recently, many robust and scaling-invariant image features such as SIFT, SURF and ORB have been successfully used in image retrieval and object recognition. In this paper, we have tried to compare the performance for texture classification using these feature descriptors with k-means clustering. Different classifiers including K-NN, Naive Bayes, Back Propagation Neural Network , Decision Tree and Kstar were applied in three texture image sets － UIUCTex, KTH-TIPS and Brodatz, respectively. Experimental results reveal SIFTS as the best average accuracy rate holder in UIUCTex, KTH-TIPS and SURF is advantaged in Brodatz texture set. BP neuro network works best in the test set classification among all used classifiers. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=texture%20classification" title="texture classification">texture classification</a>, <a href="https://publications.waset.org/abstracts/search?q=texture%20descriptor" title=" texture descriptor"> texture descriptor</a>, <a href="https://publications.waset.org/abstracts/search?q=SIFT" title=" SIFT"> SIFT</a>, <a href="https://publications.waset.org/abstracts/search?q=SURF" title=" SURF"> SURF</a>, <a href="https://publications.waset.org/abstracts/search?q=ORB" title=" ORB"> ORB</a> </p> <a href="https://publications.waset.org/abstracts/11046/evaluation-of-robust-feature-descriptors-for-texture-classification" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/11046.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">369</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">4522</span> Review on Effective Texture Classification Techniques</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sujata%20S.%20Kulkarni">Sujata S. Kulkarni</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Effective and efficient texture feature extraction and classification is an important problem in image understanding and recognition. This paper gives a review on effective texture classification method. The objective of the problem of texture representation is to reduce the amount of raw data presented by the image, while preserving the information needed for the task. Texture analysis is important in many applications of computer image analysis for classification include industrial and biomedical surface inspection, for example for defects and disease, ground classification of satellite or aerial imagery and content-based access to image databases. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=compressed%20sensing" title="compressed sensing">compressed sensing</a>, <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=image%20classification" title=" image classification"> image classification</a>, <a href="https://publications.waset.org/abstracts/search?q=texture%20analysis" title=" texture analysis"> texture analysis</a> </p> <a href="https://publications.waset.org/abstracts/24461/review-on-effective-texture-classification-techniques" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/24461.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">4521</span> Automatic Moment-Based Texture Segmentation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Tudor%20Barbu">Tudor Barbu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> An automatic moment-based texture segmentation approach is proposed in this paper. First, we describe the related work in this computer vision domain. Our texture feature extraction, the first part of the texture recognition process, produces a set of moment-based feature vectors. For each image pixel, a texture feature vector is computed as a sequence of area moments. Second, an automatic pixel classification approach is proposed. The feature vectors are clustered using some unsupervised classification algorithm, the optimal number of clusters being determined using a measure based on validation indexes. From the resulted pixel classes one determines easily the desired texture regions of the image. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=image%20segmentation" title="image segmentation">image segmentation</a>, <a href="https://publications.waset.org/abstracts/search?q=moment-based" title=" moment-based"> moment-based</a>, <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=automatic%20classification" title=" automatic classification"> automatic classification</a>, <a href="https://publications.waset.org/abstracts/search?q=validation%20indexes" title=" validation indexes"> validation indexes</a> </p> <a href="https://publications.waset.org/abstracts/3065/automatic-moment-based-texture-segmentation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/3065.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">416</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">4520</span> Computer Aided Classification of Architectural Distortion in Mammograms Using Texture Features</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Birmohan%20Singh">Birmohan Singh</a>, <a href="https://publications.waset.org/abstracts/search?q=V.K.Jain"> V.K.Jain</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Computer aided diagnosis systems provide vital opinion to radiologists in the detection of early signs of breast cancer from mammogram images. Masses and microcalcifications, architectural distortions are the major abnormalities. In this paper, a computer aided diagnosis system has been proposed for distinguishing abnormal mammograms with architectural distortion from normal mammogram. Four types of texture features GLCM texture, GLRLM texture, fractal texture and spectral texture features for the regions of suspicion are extracted. Support Vector Machine has been used as classifier in this study. The proposed system yielded an overall sensitivity of 96.47% and accuracy of 96% for the detection of abnormalities with mammogram images collected from Digital Database for Screening Mammography (DDSM) database. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=architecture%20distortion" title="architecture distortion">architecture distortion</a>, <a href="https://publications.waset.org/abstracts/search?q=mammograms" title=" mammograms"> mammograms</a>, <a href="https://publications.waset.org/abstracts/search?q=GLCM%20texture%20features" title=" GLCM texture features"> GLCM texture features</a>, <a href="https://publications.waset.org/abstracts/search?q=GLRLM%20texture%20features" title=" GLRLM texture features"> GLRLM texture features</a>, <a href="https://publications.waset.org/abstracts/search?q=support%20vector%20machine%20classifier" title=" support vector machine classifier"> support vector machine classifier</a> </p> <a href="https://publications.waset.org/abstracts/29952/computer-aided-classification-of-architectural-distortion-in-mammograms-using-texture-features" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/29952.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">491</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">4519</span> A Neural Approach for Color-Textured Images Segmentation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Khalid%20Salhi">Khalid Salhi</a>, <a href="https://publications.waset.org/abstracts/search?q=El%20Miloud%20Jaara"> El Miloud Jaara</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohammed%20Talibi%20Alaoui"> Mohammed Talibi Alaoui</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we present a neural approach for unsupervised natural color-texture image segmentation, which is based on both Kohonen maps and mathematical morphology, using a combination of the texture and the image color information of the image, namely, the fractal features based on fractal dimension are selected to present the information texture, and the color features presented in RGB color space. These features are then used to train the network Kohonen, which will be represented by the underlying probability density function, the segmentation of this map is made by morphological watershed transformation. The performance of our color-texture segmentation approach is compared first, to color-based methods or texture-based methods only, and then to k-means method. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=segmentation" title="segmentation">segmentation</a>, <a href="https://publications.waset.org/abstracts/search?q=color-texture" title=" color-texture"> color-texture</a>, <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=fractal" title=" fractal"> fractal</a>, <a href="https://publications.waset.org/abstracts/search?q=watershed" title=" watershed"> watershed</a> </p> <a href="https://publications.waset.org/abstracts/51740/a-neural-approach-for-color-textured-images-segmentation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/51740.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">346</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">4518</span> A Molding Surface Auto-inspection System</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ssu-Han%20Chen">Ssu-Han Chen</a>, <a href="https://publications.waset.org/abstracts/search?q=Der-Baau%20Perng"> Der-Baau Perng</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Molding process in IC manufacturing secures chips against the harms done by hot, moisture or other external forces. While a chip was being molded, defects like cracks, dilapidation, or voids may be embedding on the molding surface. The molding surfaces the study poises to treat and the ones on the market, though, differ in the surface where texture similar to defects is everywhere. Manual inspection usually passes over low-contrast cracks or voids; hence an automatic optical inspection system for molding surface is necessary. The proposed system is consisted of a CCD, a coaxial light, a back light as well as a motion control unit. Based on the property of statistical textures of the molding surface, a series of digital image processing and classification procedure is carried out. After training of the parameter associated with above algorithm, result of the experiment suggests that the accuracy rate is up to 93.75%, contributing to the inspection quality of IC molding surface. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=molding%20surface" title="molding surface">molding surface</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20vision" title=" machine vision"> machine vision</a>, <a href="https://publications.waset.org/abstracts/search?q=statistical%20texture" title=" statistical texture"> statistical texture</a>, <a href="https://publications.waset.org/abstracts/search?q=discrete%20Fourier%20transformation" title=" discrete Fourier transformation"> discrete Fourier transformation</a> </p> <a href="https://publications.waset.org/abstracts/4170/a-molding-surface-auto-inspection-system" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/4170.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">431</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">4517</span> Effect of Different Contact Rollers on the Surface Texture during the Belt Grinding Process</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Amine%20Hamdi">Amine Hamdi</a>, <a href="https://publications.waset.org/abstracts/search?q=Sidi%20Mohammed%20Merghache"> Sidi Mohammed Merghache</a>, <a href="https://publications.waset.org/abstracts/search?q=Brahim%20Fernini"> Brahim Fernini</a> </p> <p class="card-text"><strong>Abstract:</strong></p> During abrasive machining of hard steels by belt grinding, the finished surface texture is influenced by the pressure between the abrasive belt and the workpiece; this pressure is the force applied by the contact roller on the workpiece. Therefore, the contact roller has an important role and has a direct impact on process efficiency. The objective of this article is to study and compare the influence of different contact rollers on the belt ground surface texture. The quality of the surface texture is characterized by eight roughness parameters (Ra, Rz, Rp, Rv, Rsk, Rku, Rsm, and Rdq) and five parameters of the bearing area curve (Rpk, Rk, Rvk, Mr1, and Mr2). The results of the experimental tests indicate a better surface texture obtained by the PA 6 polyamide roller (hardness 60 Shore D) compared to that obtained with other rollers of the same hardness or of different hardness. Simultaneously, optimum medium pressure between the belt and the workpiece allows chip removal without fracturing the abrasive grains. This generates a good surface texture. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=belt%20grinding" title="belt grinding">belt grinding</a>, <a href="https://publications.waset.org/abstracts/search?q=contact%20roller" title=" contact roller"> contact roller</a>, <a href="https://publications.waset.org/abstracts/search?q=pressure" title=" pressure"> pressure</a>, <a href="https://publications.waset.org/abstracts/search?q=abrasive%20belt" title=" abrasive belt"> abrasive belt</a>, <a href="https://publications.waset.org/abstracts/search?q=surface%20texture" title=" surface texture"> surface texture</a> </p> <a href="https://publications.waset.org/abstracts/132926/effect-of-different-contact-rollers-on-the-surface-texture-during-the-belt-grinding-process" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/132926.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">184</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">4516</span> Assessment of Runway Micro Texture Using Surface Laser Scanners: An Explorative Study</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Gerard%20Van%20Es">Gerard Van Es</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this study, the use of a high resolution surface laser scanner to assess the micro texture of runway surfaces was investigated experimentally. Micro texture is one of the important surface components that helps to provide high braking friction between aircraft tires and a wet runway surface. Algorithms to derive different parameters that characterise micro texture was developed. Surface scans with a high resolution laser scanner were conducted on 40 different runway (like) surfaces. For each surface micro texture parameters were calculated from the laser scan data. These results were correlated with results obtained from a British pendulum tester that was used on the same surface. Results obtained with the British pendulum tester are generally considered to be indicative for the micro texture related friction characteristics. The results show that a meaningful correlation can be found between different parameters that characterise micro texture obtained with the laser scanner and the British pendulum tester results. Surface laser scanners are easier to operate and give more consistent results than a British pendulum tester. Therefore for airport operators surface laser scanners can be a useful tool to determine if their runway becomes slippery when wet due to a smooth micro texture. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=runway%20friction" title="runway friction">runway friction</a>, <a href="https://publications.waset.org/abstracts/search?q=micro%20texture" title=" micro texture"> micro texture</a>, <a href="https://publications.waset.org/abstracts/search?q=aircraft%20braking%20performance" title=" aircraft braking performance"> aircraft braking performance</a>, <a href="https://publications.waset.org/abstracts/search?q=slippery%20runways" title=" slippery runways"> slippery runways</a> </p> <a href="https://publications.waset.org/abstracts/151466/assessment-of-runway-micro-texture-using-surface-laser-scanners-an-explorative-study" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/151466.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">121</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">4515</span> Estimation of Asphalt Pavement Surfaces Using Image Analysis Technique</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mohammad%20A.%20Khasawneh">Mohammad A. Khasawneh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Asphalt concrete pavements gradually lose their skid resistance causing safety problems especially under wet conditions and high driving speeds. In order to enact the actual field polishing and wearing process of asphalt pavement surfaces in a laboratory setting, several laboratory-scale accelerated polishing devices were developed by different agencies. To mimic the actual process, friction and texture measuring devices are needed to quantify surface deterioration at different polishing intervals that reflect different stages of the pavement life. The test could still be considered lengthy and to some extent labor-intensive. Therefore, there is a need to come up with another method that can assist in investigating the bituminous pavement surface characteristics in a practical and time-efficient test procedure. The purpose of this paper is to utilize a well-developed image analysis technique to characterize asphalt pavement surfaces without the need to use conventional friction and texture measuring devices in an attempt to shorten and simplify the polishing procedure in the lab. Promising findings showed the possibility of using image analysis in lieu of the labor-sensitive-variable-in-nature friction and texture measurements. It was found that the exposed aggregate surface area of asphalt specimens made from limestone and gravel aggregates produced solid evidence of the validity of this method in describing asphalt pavement surfaces. Image analysis results correlated well with the British Pendulum Numbers (BPN), Polish Values (PV) and Mean Texture Depth (MTD) values. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=friction" title="friction">friction</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20analysis" title=" image analysis"> image analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=polishing" title=" polishing"> polishing</a>, <a href="https://publications.waset.org/abstracts/search?q=statistical%20analysis" title=" statistical analysis"> statistical analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=texture" title=" texture"> texture</a> </p> <a href="https://publications.waset.org/abstracts/9372/estimation-of-asphalt-pavement-surfaces-using-image-analysis-technique" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/9372.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">306</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">4514</span> Polishing Machine Based on High-Pressure Water Jet</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mohammad%20A.%20Khasawneh">Mohammad A. Khasawneh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The design of high pressure water jet based polishing equipment and its fabrication conducted in this study is reported herein, together with some preliminary test results for assessing its applicability for HMA surface polishing. This study also provides preliminary findings concerning the test variables, such as the rotational speed, the water jet pressure, the abrasive agent used, and the impact angel that were experimentally investigated in this study. The preliminary findings based on four trial tests (two on large slab specimens and two on small size gyratory compacted specimens), however, indicate that both friction and texture values tend to increase with the polishing durations for two combinations of pressure and rotation speed of the rotary deck. It seems that the more polishing action the specimen is subjected to; the aggregate edges are created such that the surface texture values are increased with the accompanied increase in friction values. It may be of interest (but which is outside the scope of this study) to investigate if the similar trend exist for HMA prepared with aggregate source that is sand and gravel. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=high-pressure" title="high-pressure">high-pressure</a>, <a href="https://publications.waset.org/abstracts/search?q=water%20jet" title=" water jet"> water jet</a>, <a href="https://publications.waset.org/abstracts/search?q=friction" title=" friction"> friction</a>, <a href="https://publications.waset.org/abstracts/search?q=texture" title=" texture"> texture</a>, <a href="https://publications.waset.org/abstracts/search?q=polishing" title=" polishing"> polishing</a>, <a href="https://publications.waset.org/abstracts/search?q=statistical%20analysis" title=" statistical analysis"> statistical analysis</a> </p> <a href="https://publications.waset.org/abstracts/21332/polishing-machine-based-on-high-pressure-water-jet" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/21332.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">487</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">4513</span> Muscle: The Tactile Texture Designed for the Blind</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Chantana%20Insra">Chantana Insra</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The research objective focuses on creating a prototype media of the tactile texture of muscles for educational institutes to help visually impaired students learn massage extra learning materials further than the ordinary curriculum. This media is designed as an extra learning material. The population in this study was 30 blinded students between 4th - 6th grades who were able to read Braille language. The research was conducted during the second semester in 2012 at The Bangkok School for the Blind. The method in choosing the population in the study was purposive sampling. The methodology of the research includes collecting data related to visually impaired people, the production of the tactile texture media, human anatomy and Thai traditional massage from literature reviews and field studies. This information was used for analyzing and designing 14 tactile texture pictures presented to experts to evaluate and test the media. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=blind" title="blind">blind</a>, <a href="https://publications.waset.org/abstracts/search?q=tactile%20texture" title=" tactile texture"> tactile texture</a>, <a href="https://publications.waset.org/abstracts/search?q=muscle" title=" muscle"> muscle</a>, <a href="https://publications.waset.org/abstracts/search?q=visual%20arts%20and%20design" title=" visual arts and design"> visual arts and design</a> </p> <a href="https://publications.waset.org/abstracts/6200/muscle-the-tactile-texture-designed-for-the-blind" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/6200.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">269</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">4512</span> Effect of Depth on Texture Features of Ultrasound Images</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=M.%20A.%20Alqahtani">M. A. Alqahtani</a>, <a href="https://publications.waset.org/abstracts/search?q=D.%20P.%20Coleman"> D. P. Coleman</a>, <a href="https://publications.waset.org/abstracts/search?q=N.%20D.%20Pugh"> N. D. Pugh</a>, <a href="https://publications.waset.org/abstracts/search?q=L.%20D.%20M.%20Nokes"> L. D. M. Nokes</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In diagnostic ultrasound, the echo graphic B-scan texture is an important area of investigation since it can be analyzed to characterize the histological state of internal tissues. An important factor requiring consideration when evaluating ultrasonic tissue texture is the depth. The effect of attenuation with depth of ultrasound, the size of the region of interest, gain, and dynamic range are important variables to consider as they can influence the analysis of texture features. These sources of variability have to be considered carefully when evaluating image texture as different settings might influence the resultant image. The aim of this study is to investigate the effect of depth on the texture features in-vivo using a 3D ultrasound probe. The left leg medial head of the gastrocnemius muscle of 10 healthy subjects were scanned. Two regions A and B were defined at different depth within the gastrocnemius muscle boundary. The size of both ROI’s was 280*20 pixels and the distance between region A and B was kept constant at 5 mm. Texture parameters include gray level, variance, skewness, kurtosis, co-occurrence matrix; run length matrix, gradient, autoregressive (AR) model and wavelet transform were extracted from the images. The paired t –test was used to test the depth effect for the normally distributed data and the Wilcoxon–Mann-Whitney test was used for the non-normally distributed data. The gray level, variance, and run length matrix were significantly lowered when the depth increased. The other texture parameters showed similar values at different depth. All the texture parameters showed no significant difference between depths A and B (p > 0.05) except for gray level, variance and run length matrix (p < 0.05). This indicates that gray level, variance, and run length matrix are depth dependent. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ultrasound%20image" title="ultrasound image">ultrasound image</a>, <a href="https://publications.waset.org/abstracts/search?q=texture%20parameters" title=" texture parameters"> texture parameters</a>, <a href="https://publications.waset.org/abstracts/search?q=computational%20biology" title=" computational biology"> computational biology</a>, <a href="https://publications.waset.org/abstracts/search?q=biomedical%20engineering" title=" biomedical engineering"> biomedical engineering</a> </p> <a href="https://publications.waset.org/abstracts/2991/effect-of-depth-on-texture-features-of-ultrasound-images" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/2991.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">295</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">4511</span> Characterization of Pure Nickel Coatings Fabricated under Pulse Current Conditions</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=M.%20Sajjadnejad">M. Sajjadnejad</a>, <a href="https://publications.waset.org/abstracts/search?q=H.%20Omidvar"> H. Omidvar</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Javanbakht"> M. Javanbakht</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Mozafari"> A. Mozafari</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Pure nickel coatings have been successfully electrodeposited on copper substrates by the pulse plating technique. The influence of current density, duty cycle and pulse frequency on the surface morphology, crystal orientation, and microhardness was determined. It was found that the crystallite size of the deposit increases with increasing current density and duty cycle. The crystal orientation progressively changed from a random texture at 1 A/dm2 to (200) texture at 10 A/dm2. Increasing pulse frequency resulted in increased texture coefficient and peak intensity of (111) reflection. An increase in duty cycle resulted in considerable increase in texture coefficient and peak intensity of (311) reflection. Coatings obtained at high current densities and duty cycles present a mixed morphology of small and large grains. Maximum microhardness of 193 Hv was achieved at 4 A/dm2, 10 Hz and duty cycle of 50%. Nickel coatings with (200) texture are ductile while (111) texture improves the microhardness of the coatings. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=current%20density" title="current density">current density</a>, <a href="https://publications.waset.org/abstracts/search?q=duty%20cycle" title=" duty cycle"> duty cycle</a>, <a href="https://publications.waset.org/abstracts/search?q=microstructure" title=" microstructure"> microstructure</a>, <a href="https://publications.waset.org/abstracts/search?q=nickel" title=" nickel"> nickel</a>, <a href="https://publications.waset.org/abstracts/search?q=pulse%20frequency" title=" pulse frequency"> pulse frequency</a> </p> <a href="https://publications.waset.org/abstracts/36308/characterization-of-pure-nickel-coatings-fabricated-under-pulse-current-conditions" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/36308.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">369</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">4510</span> Effect of Heat Treatment on Columnar Grain Growth and Goss Texture on Surface in Grain-Oriented Electrical Steels</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jungkyun%20Na">Jungkyun Na</a>, <a href="https://publications.waset.org/abstracts/search?q=Jaesang%20Lee"> Jaesang Lee</a>, <a href="https://publications.waset.org/abstracts/search?q=Yang%20Mo%20Koo"> Yang Mo Koo</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this study to find a replacement for expensive secondary recrystallization in GO electrical steel production, effect of heat treatment on the formation of columnar grain and Goss texture is investigated. The composition of the sample is Fe-2.0Si-0.2C. This process involves repeating of cold rolling and decarburization as a replacement for secondary recrystallization. By cold-rolling shear band is made and Goss grain grows from shear band by decarburization. By doing another cold rolling, some Goss texture is newly formed from the shear band, and some Goss texture is retained in microbands. To determine whether additional heat treatment with H2 atmosphere is needed on decarburization process for growth of Goss texture, comparing between decarburization and heat treatment with H2 atmosphere is performed. Also, to find optimum condition for heat treatment, heat treatment with various time and temperature is performed. It was found that increase in the number of cold rolling and heat treatment increases Goss texture. Both high Goss texture and good columnar structure is achieved at 900℃, and this temperature is within a+r phase region. Heat treatment at a temperature higher than a+r phase region caused carbon diffusion and this made layer with Goss grain decrease. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=electrical%20steel" title="electrical steel">electrical steel</a>, <a href="https://publications.waset.org/abstracts/search?q=Goss%20texture" title=" Goss texture"> Goss texture</a>, <a href="https://publications.waset.org/abstracts/search?q=columnar%20structure" title=" columnar structure"> columnar structure</a>, <a href="https://publications.waset.org/abstracts/search?q=normal%20grain%20growth" title=" normal grain growth"> normal grain growth</a> </p> <a href="https://publications.waset.org/abstracts/74896/effect-of-heat-treatment-on-columnar-grain-growth-and-goss-texture-on-surface-in-grain-oriented-electrical-steels" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/74896.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">218</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">4509</span> Towards Integrating Statistical Color Features for Human Skin Detection</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mohd%20Zamri%20Osman">Mohd Zamri Osman</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohd%20Aizaini%20Maarof"> Mohd Aizaini Maarof</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohd%20Foad%20Rohani"> Mohd Foad Rohani</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Human skin detection recognized as the primary step in most of the applications such as face detection, illicit image filtering, hand recognition and video surveillance. The performance of any skin detection applications greatly relies on the two components: feature extraction and classification method. Skin color is the most vital information used for skin detection purpose. However, color feature alone sometimes could not handle images with having same color distribution with skin color. A color feature of pixel-based does not eliminate the skin-like color due to the intensity of skin and skin-like color fall under the same distribution. Hence, the statistical color analysis will be exploited such mean and standard deviation as an additional feature to increase the reliability of skin detector. In this paper, we studied the effectiveness of statistical color feature for human skin detection. Furthermore, the paper analyzed the integrated color and texture using eight classifiers with three color spaces of RGB, YCbCr, and HSV. The experimental results show that the integrating statistical feature using Random Forest classifier achieved a significant performance with an F1-score 0.969. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=color%20space" title="color space">color space</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=random%20forest" title=" random forest"> random forest</a>, <a href="https://publications.waset.org/abstracts/search?q=skin%20detection" title=" skin detection"> skin detection</a>, <a href="https://publications.waset.org/abstracts/search?q=statistical%20feature" title=" statistical feature"> statistical feature</a> </p> <a href="https://publications.waset.org/abstracts/43485/towards-integrating-statistical-color-features-for-human-skin-detection" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/43485.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">462</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">4508</span> Computer Aide Discrimination of Benign and Malignant Thyroid Nodules by Ultrasound Imaging</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Akbar%20Gharbali">Akbar Gharbali</a>, <a href="https://publications.waset.org/abstracts/search?q=Ali%20Abbasian%20Ardekani"> Ali Abbasian Ardekani</a>, <a href="https://publications.waset.org/abstracts/search?q=Afshin%20Mohammadi"> Afshin Mohammadi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Introduction: Thyroid nodules have an incidence of 33-68% in the general population. More than 5-15% of these nodules are malignant. Early detection and treatment of thyroid nodules increase the cure rate and provide optimal treatment. Between the medical imaging methods, Ultrasound is the chosen imaging technique for assessment of thyroid nodules. The confirming of the diagnosis usually demands repeated fine-needle aspiration biopsy (FNAB). So, current management has morbidity and non-zero mortality. Objective: To explore diagnostic potential of automatic texture analysis (TA) methods in differentiation benign and malignant thyroid nodules by ultrasound imaging in order to help for reliable diagnosis and monitoring of the thyroid nodules in their early stages with no need biopsy. Material and Methods: The thyroid US image database consists of 70 patients (26 benign and 44 malignant) which were reported by Radiologist and proven by the biopsy. Two slices per patient were loaded in Mazda Software version 4.6 for automatic texture analysis. Regions of interests (ROIs) were defined within the abnormal part of the thyroid nodules ultrasound images. Gray levels within an ROI normalized according to three normalization schemes: N1: default or original gray levels, N2: +/- 3 Sigma or dynamic intensity limited to µ+/- 3σ, and N3: present intensity limited to 1% - 99%. Up to 270 multiscale texture features parameters per ROIs per each normalization schemes were computed from well-known statistical methods employed in Mazda software. From the statistical point of view, all calculated texture features parameters are not useful for texture analysis. So, the features based on maximum Fisher coefficient and the minimum probability of classification error and average correlation coefficients (POE+ACC) eliminated to 10 best and most effective features per normalization schemes. We analyze this feature under two standardization states (standard (S) and non-standard (NS)) with Principle Component Analysis (PCA), Linear Discriminant Analysis (LDA) and Non-Linear Discriminant Analysis (NDA). The 1NN classifier was performed to distinguish between benign and malignant tumors. The confusion matrix and Receiver operating characteristic (ROC) curve analysis were used for the formulation of more reliable criteria of the performance of employed texture analysis methods. Results: The results demonstrated the influence of the normalization schemes and reduction methods on the effectiveness of the obtained features as a descriptor on discrimination power and classification results. The selected subset features under 1%-99% normalization, POE+ACC reduction and NDA texture analysis yielded a high discrimination performance with the area under the ROC curve (Az) of 0.9722, in distinguishing Benign from Malignant Thyroid Nodules which correspond to sensitivity of 94.45%, specificity of 100%, and accuracy of 97.14%. Conclusions: Our results indicate computer-aided diagnosis is a reliable method, and can provide useful information to help radiologists in the detection and classification of benign and malignant thyroid nodules. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ultrasound%20imaging" title="ultrasound imaging">ultrasound imaging</a>, <a href="https://publications.waset.org/abstracts/search?q=thyroid%20nodules" title=" thyroid nodules"> thyroid nodules</a>, <a href="https://publications.waset.org/abstracts/search?q=computer%20aided%20diagnosis" title=" computer aided diagnosis"> computer aided diagnosis</a>, <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=PCA" title=" PCA"> PCA</a>, <a href="https://publications.waset.org/abstracts/search?q=LDA" title=" LDA"> LDA</a>, <a href="https://publications.waset.org/abstracts/search?q=NDA" title=" NDA"> NDA</a> </p> <a href="https://publications.waset.org/abstracts/32236/computer-aide-discrimination-of-benign-and-malignant-thyroid-nodules-by-ultrasound-imaging" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/32236.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">279</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">4507</span> Microstructure and Texture Evolution of Cryo Rolled and Annealed Ductile TaNbHfZrTi Refractory High Entropy Alloy</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mokali%20Veeresham">Mokali Veeresham</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The microstructure and texture evolution of cryo rolled and annealed ductile TaHfNbZrTi refractory high entropy alloy was investigated. To obtain that, the alloy is severely cryo rolled and subsequently annealed for the recrystallization process. The cryo rolled – 90% shows the presence of very fine grains and microstructural heterogeneity. The cryo rolled samples are annealed at a temperature ranging from 800°C to 1400°C, the partial recrystallization is observed at 800°C annealed condition, and at higher annealing temperatures the complete recrystallization process is noticed. The development of ND fiber texture is observed after the annealing. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=refractory%20high%20entropy%20alloy" title="refractory high entropy alloy">refractory high entropy alloy</a>, <a href="https://publications.waset.org/abstracts/search?q=cryo-rolling" title=" cryo-rolling"> cryo-rolling</a>, <a href="https://publications.waset.org/abstracts/search?q=annealing" title=" annealing"> annealing</a>, <a href="https://publications.waset.org/abstracts/search?q=microstructure" title=" microstructure"> microstructure</a>, <a href="https://publications.waset.org/abstracts/search?q=texture" title=" texture"> texture</a> </p> <a href="https://publications.waset.org/abstracts/139305/microstructure-and-texture-evolution-of-cryo-rolled-and-annealed-ductile-tanbhfzrti-refractory-high-entropy-alloy" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/139305.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">176</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">4506</span> Local Texture and Global Color Descriptors for Content Based Image Retrieval</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Tajinder%20Kaur">Tajinder Kaur</a>, <a href="https://publications.waset.org/abstracts/search?q=Anu%20Bala"> Anu Bala</a> </p> <p class="card-text"><strong>Abstract:</strong></p> An image retrieval system is a computer system for browsing, searching, and retrieving images from a large database of digital images a new algorithm meant for content-based image retrieval (CBIR) is presented in this paper. The proposed method combines the color and texture features which are extracted the global and local information of the image. The local texture feature is extracted by using local binary patterns (LBP), which are evaluated by taking into consideration of local difference between the center pixel and its neighbors. For the global color feature, the color histogram (CH) is used which is calculated by RGB (red, green, and blue) spaces separately. In this paper, the combination of color and texture features are proposed for content-based image retrieval. The performance of the proposed method is tested on Corel 1000 database which is the natural database. The results after being investigated show a significant improvement in terms of their evaluation measures as compared to LBP and CH. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=color" title="color">color</a>, <a href="https://publications.waset.org/abstracts/search?q=texture" title=" texture"> texture</a>, <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%20binary%20patterns" title=" local binary patterns"> local binary patterns</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20retrieval" title=" image retrieval"> image retrieval</a> </p> <a href="https://publications.waset.org/abstracts/25503/local-texture-and-global-color-descriptors-for-content-based-image-retrieval" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/25503.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">366</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">4505</span> Validating Texture Analysis as a Tool for Determining Bioplastic (Bio)Degradation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sally%20J.%20Price">Sally J. Price</a>, <a href="https://publications.waset.org/abstracts/search?q=Greg%20F.%20Walker"> Greg F. Walker</a>, <a href="https://publications.waset.org/abstracts/search?q=Weiyi%20Liu"> Weiyi Liu</a>, <a href="https://publications.waset.org/abstracts/search?q=Craig%20R.%20Bunt"> Craig R. Bunt</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Plastics, due to their long lifespan, are becoming more of an environmental concern once their useful life has been completed. There are a vast array of different types of plastic, and they can be found in almost every ecosystem on earth and are of particular concern in terrestrial environments where they can become incorporated into the food chain. Hence bioplastics have become more of interest to manufacturers and the public recently as they have the ability to (bio)degrade in commercial and in home composting situations. However, tools in which to quantify how they degrade in response to environmental variables are still being developed -one such approach is texture analysis using a TA.XT Texture Analyser, Stable Microsystems, was used to determine the force required to break or punch holes in standard ASTM D638 Type IV 3D printed bioplastic “dogbones” depending on the thicknesses of them. Manufacturers’ recommendations for calibrating the Texture Analyser are one such approach for standardising results; however, an independent technique using dummy dogbones and a substitute for the bioplastic was used alongside the samples. This approach was unexpectedly more valuable than realised at the start of the trial as irregular results were later discovered with the substitute material before valuable samples collected from the field were lost due to possible machine malfunction. This work will show the value of having an independent approach to machine calibration for accurate sample analysis with a Texture Analyser when analysing bioplastic samples. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=bioplastic" title="bioplastic">bioplastic</a>, <a href="https://publications.waset.org/abstracts/search?q=degradation" title=" degradation"> degradation</a>, <a href="https://publications.waset.org/abstracts/search?q=environment" title=" environment"> environment</a>, <a href="https://publications.waset.org/abstracts/search?q=texture%20analyzer" title=" texture analyzer"> texture analyzer</a> </p> <a href="https://publications.waset.org/abstracts/145992/validating-texture-analysis-as-a-tool-for-determining-bioplastic-biodegradation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/145992.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">206</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">4504</span> Effective Texture Features for Segmented Mammogram Images Based on Multi-Region of Interest Segmentation Method</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ramayanam%20Suresh">Ramayanam Suresh</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Nagaraja%20Rao"> A. Nagaraja Rao</a>, <a href="https://publications.waset.org/abstracts/search?q=B.%20Eswara%20Reddy"> B. Eswara Reddy</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Texture features of mammogram images are useful for finding masses or cancer cases in mammography, which have been used by radiologists. Textures are greatly succeeded for segmented images rather than normal images. It is necessary to perform segmentation for exclusive specification of cancer and non-cancer regions separately. Region of interest (ROI) is most commonly used technique for mammogram segmentation. Limitation of this method is that it is unable to explore segmentation for large collection of mammogram images. Therefore, this paper is proposed multi-ROI segmentation for addressing the above limitation. It supports greatly in finding the best texture features of mammogram images. Experimental study demonstrates the effectiveness of proposed work using benchmarked images. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=texture%20features" title="texture features">texture features</a>, <a href="https://publications.waset.org/abstracts/search?q=region%20of%20interest" title=" region of interest"> region of interest</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-ROI%20segmentation" title=" multi-ROI segmentation"> multi-ROI segmentation</a>, <a href="https://publications.waset.org/abstracts/search?q=benchmarked%20images" title=" benchmarked images "> benchmarked images </a> </p> <a href="https://publications.waset.org/abstracts/88666/effective-texture-features-for-segmented-mammogram-images-based-on-multi-region-of-interest-segmentation-method" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/88666.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">4503</span> Changes in Textural Properties of Zucchini Slices Under Effects of Partial Predrying and Deep-Fat-Frying</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=E.%20Karacabey">E. Karacabey</a>, <a href="https://publications.waset.org/abstracts/search?q=%C5%9E.%20G.%20%C3%96z%C3%A7elik"> Ş. G. Özçelik</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20S.%20Turan"> M. S. Turan</a>, <a href="https://publications.waset.org/abstracts/search?q=C.%20Baltac%C4%B1o%C4%9Flu"> C. Baltacıoğlu</a>, <a href="https://publications.waset.org/abstracts/search?q=E.%20K%C3%BC%C3%A7%C3%BCk%C3%B6ner"> E. Küçüköner</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Changes in textural properties of any food material during processing is significant for further consumer’s evaluation and directly affects their decisions. Thus any food material should be considered in terms of textural properties after any process. In the present study zucchini slices were partially predried to control and reduce the product’s final oil content. A conventional oven was used for partially dehydration of zucchini slices. Following frying was carried in an industrial fryer having temperature controller. This study was based on the effect of this predrying process on textural properties of fried zucchini slices. Texture profile analysis was performed. Hardness, elasticity, chewiness, cohesiveness were studied texture parameters of fried zucchini slices. Temperature and weight loss were monitored parameters of predrying process, whereas, in frying, oil temperature and process time were controlled. Optimization of two successive processes was done by response surface methodology being one of the common used statistical process optimization tools. Models developed for each texture parameters displayed high success to predict their values as a function of studied processes’ conditions. Process optimization was performed according to target values for each property determined for directly fried zucchini slices taking the highest score from sensory evaluation. Results indicated that textural properties of predried and then fried zucchini slices could be controlled by well-established equations. This is thought to be significant for fried stuff related food industry, where controlling of sensorial properties are crucial to lead consumer’s perception and texture related ones are leaders. This project (113R015) has been supported by TUBITAK. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=optimization" title="optimization">optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=response%20surface%20methodology" title=" response surface methodology"> response surface methodology</a>, <a href="https://publications.waset.org/abstracts/search?q=texture%20profile%20analysis" title=" texture profile analysis"> texture profile analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=conventional%20oven" title=" conventional oven"> conventional oven</a>, <a href="https://publications.waset.org/abstracts/search?q=modelling" title=" modelling"> modelling</a> </p> <a href="https://publications.waset.org/abstracts/27990/changes-in-textural-properties-of-zucchini-slices-under-effects-of-partial-predrying-and-deep-fat-frying" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/27990.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">433</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">4502</span> Fusion of Shape and Texture for Unconstrained Periocular Authentication</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=D.%20R.%20Ambika">D. R. Ambika</a>, <a href="https://publications.waset.org/abstracts/search?q=K.%20R.%20Radhika"> K. R. Radhika</a>, <a href="https://publications.waset.org/abstracts/search?q=D.%20Seshachalam"> D. Seshachalam</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Unconstrained authentication is an important component for personal automated systems and human-computer interfaces. Existing solutions mostly use face as the primary object of analysis. The performance of face-based systems is largely determined by the extent of deformation caused in the facial region and amount of useful information available in occluded face images. Periocular region is a useful portion of face with discriminative ability coupled with resistance to deformation. A reliable portion of periocular area is available for occluded images. The present work demonstrates that joint representation of periocular texture and periocular structure provides an effective expression and poses invariant representation. The proposed methodology provides an effective and compact description of periocular texture and shape. The method is tested over four benchmark datasets exhibiting varied acquisition conditions. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=periocular%20authentication" title="periocular authentication">periocular authentication</a>, <a href="https://publications.waset.org/abstracts/search?q=Zernike%20moments" title=" Zernike moments"> Zernike moments</a>, <a href="https://publications.waset.org/abstracts/search?q=LBP%20variance" title=" LBP variance"> LBP variance</a>, <a href="https://publications.waset.org/abstracts/search?q=shape%20and%20texture%20fusion" title=" shape and texture fusion"> shape and texture fusion</a> </p> <a href="https://publications.waset.org/abstracts/68833/fusion-of-shape-and-texture-for-unconstrained-periocular-authentication" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/68833.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">278</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">4501</span> Plastic Deformation of Mg-Gd Solid Solutions between 4K and 298K</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Anna%20Kula">Anna Kula</a>, <a href="https://publications.waset.org/abstracts/search?q=Raja%20K.%20Mishra"> Raja K. Mishra</a>, <a href="https://publications.waset.org/abstracts/search?q=Marek%20Niewczas"> Marek Niewczas</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Deformation behavior of Mg-Gd solid solutions have been studied by a combination of measurements of mechanical response, texture and dislocation substructure. Increase in Gd content strongly influences the work-hardening behavior and flow characteristics in tension and compression. Adiabatic instabilities have been observed in all alloys at 4K under both tension and compression. The frequency and the amplitude of adiabatic stress oscillations increase with Gd content. Profuse mechanical twinning has been observed under compression, resulting in a texture dominated by basal component parallel to the compression axis. Under tension, twining is less active and the texture evolution is affected mostly by slip. Increasing Gd concentration leads to the reduction of the tension and compression asymmetry due to weakening of the texture and stabilizing more homogenous twinning and slip, involving basal and non-basal slip systems. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mg-Gd%20alloys" title="Mg-Gd alloys">Mg-Gd alloys</a>, <a href="https://publications.waset.org/abstracts/search?q=mechanical%20properties" title=" mechanical properties"> mechanical properties</a>, <a href="https://publications.waset.org/abstracts/search?q=work%20hardening" title=" work hardening"> work hardening</a>, <a href="https://publications.waset.org/abstracts/search?q=twinning" title=" twinning"> twinning</a> </p> <a href="https://publications.waset.org/abstracts/21344/plastic-deformation-of-mg-gd-solid-solutions-between-4k-and-298k" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/21344.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">539</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">4500</span> Effect of Temperature and Deformation Mode on Texture Evolution of AA6061</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=M.%20Ghosh">M. Ghosh</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Miroux"> A. Miroux</a>, <a href="https://publications.waset.org/abstracts/search?q=L.%20A.%20I.%20Kestens"> L. A. I. Kestens</a> </p> <p class="card-text"><strong>Abstract:</strong></p> At molecular or micrometre scale, practically all materials are neither homogeneous nor isotropic. The concept of texture is used to identify the structural features that cause the properties of a material to be anisotropic. For metallic materials, the anisotropy of the mechanical behaviour originates from the crystallographic nature of plastic deformation, and is therefore controlled by the crystallographic texture. Anisotropy in mechanical properties often constitutes a disadvantage in the application of materials, as it is often illustrated by the earing phenomena during drawing. However, advantages may also be attained when considering other properties (e.g. optimization of magnetic behaviour to a specific direction) by controlling texture through thermo-mechanical processing). Nevertheless, in order to have better control over the final properties it is essential to relate texture with materials processing route and subsequently optimise their performance. However, up to date, few studies have been reported about the evolution of texture in 6061 aluminium alloy during warm processing (from room temperature to 250ºC). In present investigation, recrystallized 6061 aluminium alloy samples were subjected to tensile and plane strain compression (PSC) at room and warm temperatures. The gradual change of texture following both deformation modes were measured and discussed. Tensile tests demonstrate the mechanism at low strain while PSC does the same at high strain and eventually simulate the condition of rolling. Cube dominated texture of the initial rolled and recrystallized AA6061 sheets were replaced by domination of S and R components after PSC at room temperature, warm temperature (250ºC) though did not reflect any noticeable deviation from room temperature observation. It was also noticed that temperature has no significant effect on the evolution of grain morphology during PSC. The band contrast map revealed that after 30% deformation the substructure inside the grain is mainly made of series of parallel bands. A tendency for decrease of Cube and increase of Goss was noticed after tensile deformation compared to as-received material. Like PSC, texture does not change after deformation at warm temperature though. n-fibre was noticed for all the three textures from Goss to Cube. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=AA%206061" title="AA 6061">AA 6061</a>, <a href="https://publications.waset.org/abstracts/search?q=deformation" title=" deformation"> deformation</a>, <a href="https://publications.waset.org/abstracts/search?q=temperature" title=" temperature"> temperature</a>, <a href="https://publications.waset.org/abstracts/search?q=tensile" title=" tensile"> tensile</a>, <a href="https://publications.waset.org/abstracts/search?q=PSC" title=" PSC"> PSC</a>, <a href="https://publications.waset.org/abstracts/search?q=texture" title=" texture"> texture</a> </p> <a href="https://publications.waset.org/abstracts/5951/effect-of-temperature-and-deformation-mode-on-texture-evolution-of-aa6061" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/5951.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">484</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">4499</span> Best-Performing Color Space for Land-Sea Segmentation Using Wavelet Transform Color-Texture Features and Fusion of over Segmentation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Seynabou%20Toure">Seynabou Toure</a>, <a href="https://publications.waset.org/abstracts/search?q=Oumar%20Diop"> Oumar Diop</a>, <a href="https://publications.waset.org/abstracts/search?q=Kidiyo%20Kpalma"> Kidiyo Kpalma</a>, <a href="https://publications.waset.org/abstracts/search?q=Amadou%20S.%20Maiga"> Amadou S. Maiga</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Color and texture are the two most determinant elements for perception and recognition of the objects in an image. For this reason, color and texture analysis find a large field of application, for example in image classification and segmentation. But, the pioneering work in texture analysis was conducted on grayscale images, thus discarding color information. Many grey-level texture descriptors have been proposed and successfully used in numerous domains for image classification: face recognition, industrial inspections, food science medical imaging among others. Taking into account color in the definition of these descriptors makes it possible to better characterize images. Color texture is thus the subject of recent work, and the analysis of color texture images is increasingly attracting interest in the scientific community. In optical remote sensing systems, sensors measure separately different parts of the electromagnetic spectrum; the visible ones and even those that are invisible to the human eye. The amounts of light reflected by the earth in spectral bands are then transformed into grayscale images. The primary natural colors Red (R) Green (G) and Blue (B) are then used in mixtures of different spectral bands in order to produce RGB images. Thus, good color texture discrimination can be achieved using RGB under controlled illumination conditions. Some previous works investigate the effect of using different color space for color texture classification. However, the selection of the best performing color space in land-sea segmentation is an open question. Its resolution may bring considerable improvements in certain applications like coastline detection, where the detection result is strongly dependent on the performance of the land-sea segmentation. The aim of this paper is to present the results of a study conducted on different color spaces in order to show the best-performing color space for land-sea segmentation. In this sense, an experimental analysis is carried out using five different color spaces (RGB, XYZ, Lab, HSV, YCbCr). For each color space, the Haar wavelet decomposition is used to extract different color texture features. These color texture features are then used for Fusion of Over Segmentation (FOOS) based classification; this allows segmentation of the land part from the sea one. By analyzing the different results of this study, the HSV color space is found as the best classification performance while using color and texture features; which is perfectly coherent with the results presented in the literature. <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=coastline" title=" coastline"> coastline</a>, <a href="https://publications.waset.org/abstracts/search?q=color" title=" color"> color</a>, <a href="https://publications.waset.org/abstracts/search?q=sea-land%20segmentation" title=" sea-land segmentation"> sea-land segmentation</a> </p> <a href="https://publications.waset.org/abstracts/84598/best-performing-color-space-for-land-sea-segmentation-using-wavelet-transform-color-texture-features-and-fusion-of-over-segmentation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/84598.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">247</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">4498</span> The Role of Deformation Strain and Annealing Temperature on Grain Boundary Engineering and Texture Evolution of Haynes 230</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mohsen%20Sanayei">Mohsen Sanayei</a>, <a href="https://publications.waset.org/abstracts/search?q=Jerzy%20Szpunar"> Jerzy Szpunar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The present study investigates the effects of deformation strain and annealing temperature on the formation of twin boundaries, deformation and recrystallization texture evolution and grain boundary networks and connectivity. The resulting microstructures were characterized using Electron Backscatter Diffraction (EBSD) and X-Ray Diffraction (XRD) both immediately following small amount of deformation and after short time annealing at high temperature to correlate the micro and macro texture evolution of these alloys. Furthermore, this study showed that the process of grain boundary engineering, consisting cycles of deformation and annealing, is found to substantially reduce the mass and size of random boundaries and increase the proportion of low Coincidence Site Lattice (CSL) grain boundaries. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=coincidence%20site%20lattice" title="coincidence site lattice">coincidence site lattice</a>, <a href="https://publications.waset.org/abstracts/search?q=grain%20boundary%20engineering" title=" grain boundary engineering"> grain boundary engineering</a>, <a href="https://publications.waset.org/abstracts/search?q=electron%20backscatter%20diffraction" title=" electron backscatter diffraction"> electron backscatter diffraction</a>, <a href="https://publications.waset.org/abstracts/search?q=texture" title=" texture"> texture</a>, <a href="https://publications.waset.org/abstracts/search?q=x-ray%20diffraction" title=" x-ray diffraction"> x-ray diffraction</a> </p> <a href="https://publications.waset.org/abstracts/70079/the-role-of-deformation-strain-and-annealing-temperature-on-grain-boundary-engineering-and-texture-evolution-of-haynes-230" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/70079.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> <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=statistical%20texture&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=statistical%20texture&page=3">3</a></li> <li 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