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image</title> <meta name="description" content="Search results for: color image"> <meta name="keywords" content="color image"> <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 navbar-toggler ml-auto" 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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="color image"> <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> 3659</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: color image</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3659</span> A Way of Converting Color Images to Gray Scale Ones for the Color-Blind: Applying to the part of the Tokyo Subway Map</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Katsuhiro%20Narikiyo">Katsuhiro Narikiyo</a>, <a href="https://publications.waset.org/abstracts/search?q=Shota%20Hashikawa"> Shota Hashikawa</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper proposes a way of removing noises and reducing the number of colors contained in a JPEG image. Main purpose of this project is to convert color images to monochrome images for the color-blind. We treat the crispy color images like the Tokyo subway map. Each color in the image has an important information. But for the color blinds, similar colors cannot be distinguished. If we can convert those colors to different gray values, they can distinguish them. Therefore we try to convert color images to monochrome images. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=color-blind" title="color-blind">color-blind</a>, <a href="https://publications.waset.org/abstracts/search?q=JPEG" title=" JPEG"> JPEG</a>, <a href="https://publications.waset.org/abstracts/search?q=monochrome%20image" title=" monochrome image"> monochrome image</a>, <a href="https://publications.waset.org/abstracts/search?q=denoise" title=" denoise"> denoise</a> </p> <a href="https://publications.waset.org/abstracts/2968/a-way-of-converting-color-images-to-gray-scale-ones-for-the-color-blind-applying-to-the-part-of-the-tokyo-subway-map" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/2968.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">355</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">3658</span> Image Segmentation Using 2-D Histogram in RGB Color Space in Digital Libraries </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=El%20Asnaoui%20Khalid">El Asnaoui Khalid</a>, <a href="https://publications.waset.org/abstracts/search?q=Aksasse%20Brahim"> Aksasse Brahim</a>, <a href="https://publications.waset.org/abstracts/search?q=Ouanan%20Mohammed"> Ouanan Mohammed </a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents an unsupervised color image segmentation method. It is based on a hierarchical analysis of 2-D histogram in RGB color space. This histogram minimizes storage space of images and thus facilitates the operations between them. The improved segmentation approach shows a better identification of objects in a color image and, at the same time, the system is fast. <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=hierarchical%20analysis" title=" hierarchical analysis"> hierarchical analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=2-D%20histogram" title=" 2-D histogram"> 2-D histogram</a>, <a href="https://publications.waset.org/abstracts/search?q=classification" title=" classification"> classification</a> </p> <a href="https://publications.waset.org/abstracts/42096/image-segmentation-using-2-d-histogram-in-rgb-color-space-in-digital-libraries" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/42096.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">380</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">3657</span> Evaluating the Performance of Color Constancy Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Damanjit%20Kaur">Damanjit Kaur</a>, <a href="https://publications.waset.org/abstracts/search?q=Avani%20Bhatia"> Avani Bhatia</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Color constancy is significant for human vision since color is a pictorial cue that helps in solving different visions tasks such as tracking, object recognition, or categorization. Therefore, several computational methods have tried to simulate human color constancy abilities to stabilize machine color representations. Two different kinds of methods have been used, i.e., normalization and constancy. While color normalization creates a new representation of the image by canceling illuminant effects, color constancy directly estimates the color of the illuminant in order to map the image colors to a canonical version. Color constancy is the capability to determine colors of objects independent of the color of the light source. This research work studies the most of the well-known color constancy algorithms like white point and gray world. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=color%20constancy" title="color constancy">color constancy</a>, <a href="https://publications.waset.org/abstracts/search?q=gray%20world" title=" gray world"> gray world</a>, <a href="https://publications.waset.org/abstracts/search?q=white%20patch" title=" white patch"> white patch</a>, <a href="https://publications.waset.org/abstracts/search?q=modified%20white%20patch" title=" modified white patch "> modified white patch </a> </p> <a href="https://publications.waset.org/abstracts/4799/evaluating-the-performance-of-color-constancy-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/4799.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">319</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">3656</span> Content-Based Image Retrieval Using HSV Color Space Features</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hamed%20Qazanfari">Hamed Qazanfari</a>, <a href="https://publications.waset.org/abstracts/search?q=Hamid%20Hassanpour"> Hamid Hassanpour</a>, <a href="https://publications.waset.org/abstracts/search?q=Kazem%20Qazanfari"> Kazem Qazanfari</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, a method is provided for content-based image retrieval. Content-based image retrieval system searches query an image based on its visual content in an image database to retrieve similar images. In this paper, with the aim of simulating the human visual system sensitivity to image's edges and color features, the concept of color difference histogram (CDH) is used. CDH includes the perceptually color difference between two neighboring pixels with regard to colors and edge orientations. Since the HSV color space is close to the human visual system, the CDH is calculated in this color space. In addition, to improve the color features, the color histogram in HSV color space is also used as a feature. Among the extracted features, efficient features are selected using entropy and correlation criteria. The final features extract the content of images most efficiently. The proposed method has been evaluated on three standard databases Corel 5k, Corel 10k and UKBench. Experimental results show that the accuracy of the proposed image retrieval method is significantly improved compared to the recently developed methods. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=content-based%20image%20retrieval" title="content-based image retrieval">content-based image retrieval</a>, <a href="https://publications.waset.org/abstracts/search?q=color%20difference%20histogram" title=" color difference histogram"> color difference histogram</a>, <a href="https://publications.waset.org/abstracts/search?q=efficient%20features%20selection" title=" efficient features selection"> efficient features selection</a>, <a href="https://publications.waset.org/abstracts/search?q=entropy" title=" entropy"> entropy</a>, <a href="https://publications.waset.org/abstracts/search?q=correlation" title=" correlation"> correlation</a> </p> <a href="https://publications.waset.org/abstracts/75068/content-based-image-retrieval-using-hsv-color-space-features" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/75068.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">249</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">3655</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">3654</span> An Efficient Encryption Scheme Using DWT and Arnold Transforms</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ali%20Abdrhman%20M.%20Ukasha">Ali Abdrhman M. Ukasha</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Data security needed in data transmission, storage, and communication to ensure the security. The color image is decomposed into red, green, and blue channels. The blue and green channels are compressed using 3-levels discrete wavelet transform. The Arnold transform uses to changes the locations of red image channel pixels as image scrambling process. Then all these channels are encrypted separately using a key image that has same original size and is generating using private keys and modulo operations. Performing the X-OR and modulo operations between the encrypted channels images for image pixel values change purpose. The extracted contours of color image recovery can be obtained with accepted level of distortion using Canny edge detector. Experiments have demonstrated that proposed algorithm can fully encrypt 2D color image and completely reconstructed without any distortion. It has shown that the color image can be protected with a higher security level. The presented method has easy hardware implementation and suitable for multimedia protection in real time applications such as wireless networks and mobile phone services. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=color%20image" title="color image">color image</a>, <a href="https://publications.waset.org/abstracts/search?q=wavelet%20transform" title=" wavelet transform"> wavelet transform</a>, <a href="https://publications.waset.org/abstracts/search?q=edge%20detector" title=" edge detector"> edge detector</a>, <a href="https://publications.waset.org/abstracts/search?q=Arnold%20transform" title=" Arnold transform"> Arnold transform</a>, <a href="https://publications.waset.org/abstracts/search?q=lossy%20image%20encryption" title=" lossy image encryption"> lossy image encryption</a> </p> <a href="https://publications.waset.org/abstracts/16468/an-efficient-encryption-scheme-using-dwt-and-arnold-transforms" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/16468.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">482</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">3653</span> Design and Development of 5-DOF Color Sorting Manipulator for Industrial Applications</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Atef%20A.%20Ata">Atef A. Ata</a>, <a href="https://publications.waset.org/abstracts/search?q=Sohair%20F.%20Rezeka"> Sohair F. Rezeka</a>, <a href="https://publications.waset.org/abstracts/search?q=Ahmed%20El-Shenawy"> Ahmed El-Shenawy</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohammed%20Diab"> Mohammed Diab</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Image processing in today’s world grabs massive attentions as it leads to possibilities of broaden application in many fields of high technology. The real challenge is how to improve existing sorting system applications which consists of two integrated stations of processing and handling with a new image processing feature. Existing color sorting techniques use a set of inductive, capacitive, and optical sensors to differentiate object color. This research presents a mechatronics color sorting system solution with the application of image processing. A 5-DOF robot arm is designed and developed with pick and place operation to be main part of the color sorting system. Image processing procedure senses the circular objects in an image captured in real time by a webcam attached at the end-effector then extracts color and position information out of it. This information is passed as a sequence of sorting commands to the manipulator that has pick-and-place mechanism. Performance analysis proves that this color based object sorting system works very accurate under ideal condition in term of adequate illumination, circular objects shape and color. The circular objects tested for sorting are red, green and blue. For non-ideal condition, such as unspecified color the accuracy reduces to 80%. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=robotics%20manipulator" title="robotics manipulator">robotics manipulator</a>, <a href="https://publications.waset.org/abstracts/search?q=5-DOF%20manipulator" title=" 5-DOF manipulator"> 5-DOF manipulator</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=color%20sorting" title=" color sorting"> color sorting</a>, <a href="https://publications.waset.org/abstracts/search?q=pick-and-place" title=" pick-and-place"> pick-and-place</a> </p> <a href="https://publications.waset.org/abstracts/1473/design-and-development-of-5-dof-color-sorting-manipulator-for-industrial-applications" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/1473.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">374</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">3652</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">3651</span> Contrast Enhancement of Color Images with Color Morphing Approach</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Javed%20Khan">Javed Khan</a>, <a href="https://publications.waset.org/abstracts/search?q=Aamir%20Saeed%20Malik"> Aamir Saeed Malik</a>, <a href="https://publications.waset.org/abstracts/search?q=Nidal%20Kamel"> Nidal Kamel</a>, <a href="https://publications.waset.org/abstracts/search?q=Sarat%20Chandra%20Dass"> Sarat Chandra Dass</a>, <a href="https://publications.waset.org/abstracts/search?q=Azura%20Mohd%20Affandi"> Azura Mohd Affandi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Low contrast images can result from the wrong setting of image acquisition or poor illumination conditions. Such images may not be visually appealing and can be difficult for feature extraction. Contrast enhancement of color images can be useful in medical area for visual inspection. In this paper, a new technique is proposed to improve the contrast of color images. The RGB (red, green, blue) color image is transformed into normalized RGB color space. Adaptive histogram equalization technique is applied to each of the three channels of normalized RGB color space. The corresponding channels in the original image (low contrast) and that of contrast enhanced image with adaptive histogram equalization (AHE) are morphed together in proper proportions. The proposed technique is tested on seventy color images of acne patients. The results of the proposed technique are analyzed using cumulative variance and contrast improvement factor measures. The results are also compared with decorrelation stretch. Both subjective and quantitative analysis demonstrates that the proposed techniques outperform the other techniques. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=contrast%20enhacement" title="contrast enhacement">contrast enhacement</a>, <a href="https://publications.waset.org/abstracts/search?q=normalized%20RGB" title=" normalized RGB"> normalized RGB</a>, <a href="https://publications.waset.org/abstracts/search?q=adaptive%20histogram%20equalization" title=" adaptive histogram equalization"> adaptive histogram equalization</a>, <a href="https://publications.waset.org/abstracts/search?q=cumulative%20variance." title=" cumulative variance."> cumulative variance.</a> </p> <a href="https://publications.waset.org/abstracts/42755/contrast-enhancement-of-color-images-with-color-morphing-approach" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/42755.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">377</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">3650</span> Lifting Wavelet Transform and Singular Values Decomposition for Secure Image Watermarking</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Siraa%20Ben%20Ftima">Siraa Ben Ftima</a>, <a href="https://publications.waset.org/abstracts/search?q=Mourad%20Talbi"> Mourad Talbi</a>, <a href="https://publications.waset.org/abstracts/search?q=Tahar%20Ezzedine"> Tahar Ezzedine</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we present a technique of secure watermarking of grayscale and color images. This technique consists in applying the Singular Value Decomposition (SVD) in LWT (Lifting Wavelet Transform) domain in order to insert the watermark image (grayscale) in the host image (grayscale or color image). It also uses signature in the embedding and extraction steps. The technique is applied on a number of grayscale and color images. The performance of this technique is proved by the PSNR (Pick Signal to Noise Ratio), the MSE (Mean Square Error) and the SSIM (structural similarity) computations. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=lifting%20wavelet%20transform%20%28LWT%29" title="lifting wavelet transform (LWT)">lifting wavelet transform (LWT)</a>, <a href="https://publications.waset.org/abstracts/search?q=sub-space%20vectorial%20decomposition" title=" sub-space vectorial decomposition"> sub-space vectorial decomposition</a>, <a href="https://publications.waset.org/abstracts/search?q=secure" title=" secure"> secure</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20watermarking" title=" image watermarking"> image watermarking</a>, <a href="https://publications.waset.org/abstracts/search?q=watermark" title=" watermark"> watermark</a> </p> <a href="https://publications.waset.org/abstracts/70998/lifting-wavelet-transform-and-singular-values-decomposition-for-secure-image-watermarking" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/70998.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">276</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">3649</span> Review on Quaternion Gradient Operator with Marginal and Vector Approaches for Colour Edge Detection</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Nadia%20Ben%20Youssef">Nadia Ben Youssef</a>, <a href="https://publications.waset.org/abstracts/search?q=Aicha%20Bouzid"> Aicha Bouzid</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Gradient estimation is one of the most fundamental tasks in the field of image processing in general, and more particularly for color images since that the research in color image gradient remains limited. The widely used gradient method is Di Zenzo’s gradient operator, which is based on the measure of squared local contrast of color images. The proposed gradient mechanism, presented in this paper, is based on the principle of the Di Zenzo’s approach using quaternion representation. This edge detector is compared to a marginal approach based on multiscale product of wavelet transform and another vector approach based on quaternion convolution and vector gradient approach. The experimental results indicate that the proposed color gradient operator outperforms marginal approach, however, it is less efficient then the second vector approach. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=gradient" title="gradient">gradient</a>, <a href="https://publications.waset.org/abstracts/search?q=edge%20detection" title=" edge detection"> edge detection</a>, <a href="https://publications.waset.org/abstracts/search?q=color%20image" title=" color image"> color image</a>, <a href="https://publications.waset.org/abstracts/search?q=quaternion" title=" quaternion"> quaternion</a> </p> <a href="https://publications.waset.org/abstracts/141138/review-on-quaternion-gradient-operator-with-marginal-and-vector-approaches-for-colour-edge-detection" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/141138.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">234</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">3648</span> Bag of Words Representation Based on Fusing Two Color Local Descriptors and Building Multiple Dictionaries </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Fatma%20Abdedayem">Fatma Abdedayem</a> </p> <p class="card-text"><strong>Abstract:</strong></p> We propose an extension to the famous method called Bag of words (BOW) which proved a successful role in the field of image categorization. Practically, this method based on representing image with visual words. In this work, firstly, we extract features from images using Spatial Pyramid Representation (SPR) and two dissimilar color descriptors which are opponent-SIFT and transformed-color-SIFT. Secondly, we fuse color local features by joining the two histograms coming from these descriptors. Thirdly, after collecting of all features, we generate multi-dictionaries coming from n random feature subsets that obtained by dividing all features into n random groups. Then, by using these dictionaries separately each image can be represented by n histograms which are lately concatenated horizontally and form the final histogram, that allows to combine Multiple Dictionaries (MDBoW). In the final step, in order to classify image we have applied Support Vector Machine (SVM) on the generated histograms. Experimentally, we have used two dissimilar image datasets in order to test our proposition: Caltech 256 and PASCAL VOC 2007. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=bag%20of%20words%20%28BOW%29" title="bag of words (BOW)">bag of words (BOW)</a>, <a href="https://publications.waset.org/abstracts/search?q=color%20descriptors" title=" color descriptors"> color descriptors</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-dictionaries" title=" multi-dictionaries"> multi-dictionaries</a>, <a href="https://publications.waset.org/abstracts/search?q=MDBoW" title=" MDBoW"> MDBoW</a> </p> <a href="https://publications.waset.org/abstracts/14637/bag-of-words-representation-based-on-fusing-two-color-local-descriptors-and-building-multiple-dictionaries" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/14637.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">297</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">3647</span> Perceptual Image Coding by Exploiting Internal Generative Mechanism</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kuo-Cheng%20Liu">Kuo-Cheng Liu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In the perceptual image coding, the objective is to shape the coding distortion such that the amplitude of distortion does not exceed the error visibility threshold, or to remove perceptually redundant signals from the image. While most researches focus on color image coding, the perceptual-based quantizer developed for luminance signals are always directly applied to chrominance signals such that the color image compression methods are inefficient. In this paper, the internal generative mechanism is integrated into the design of a color image compression method. The internal generative mechanism working model based on the structure-based spatial masking is used to assess the subjective distortion visibility thresholds that are visually consistent to human eyes better. The estimation method of structure-based distortion visibility thresholds for color components is further presented in a locally adaptive way to design quantization process in the wavelet color image compression scheme. Since the lowest subband coefficient matrix of images in the wavelet domain preserves the local property of images in the spatial domain, the error visibility threshold inherent in each coefficient of the lowest subband for each color component is estimated by using the proposed spatial error visibility threshold assessment. The threshold inherent in each coefficient of other subbands for each color component is then estimated in a local adaptive fashion based on the distortion energy allocation. By considering that the error visibility thresholds are estimated using predicting and reconstructed signals of the color image, the coding scheme incorporated with locally adaptive perceptual color quantizer does not require side information. Experimental results show that the entropies of three color components obtained by using proposed IGM-based color image compression scheme are lower than that obtained by using the existing color image compression method at perceptually lossless visual quality. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=internal%20generative%20mechanism" title="internal generative mechanism">internal generative mechanism</a>, <a href="https://publications.waset.org/abstracts/search?q=structure-based%20spatial%20masking" title=" structure-based spatial masking"> structure-based spatial masking</a>, <a href="https://publications.waset.org/abstracts/search?q=visibility%20threshold" title=" visibility threshold"> visibility threshold</a>, <a href="https://publications.waset.org/abstracts/search?q=wavelet%20domain" title=" wavelet domain"> wavelet domain</a> </p> <a href="https://publications.waset.org/abstracts/75216/perceptual-image-coding-by-exploiting-internal-generative-mechanism" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/75216.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">248</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">3646</span> Enhancing the Bionic Eye: A Real-time Image Optimization Framework to Encode Color and Spatial Information Into Retinal Prostheses</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=William%20Huang">William Huang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Retinal prostheses are currently limited to low resolution grayscale images that lack color and spatial information. This study develops a novel real-time image optimization framework and tools to encode maximum information to the prostheses which are constrained by the number of electrodes. One key idea is to localize main objects in images while reducing unnecessary background noise through region-contrast saliency maps. A novel color depth mapping technique was developed through MiniBatchKmeans clustering and color space selection. The resulting image was downsampled using bicubic interpolation to reduce image size while preserving color quality. In comparison to current schemes, the proposed framework demonstrated better visual quality in tested images. The use of the region-contrast saliency map showed improvements in efficacy up to 30%. Finally, the computational speed of this algorithm is less than 380 ms on tested cases, making real-time retinal prostheses feasible. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=retinal%20implants" title="retinal implants">retinal implants</a>, <a href="https://publications.waset.org/abstracts/search?q=virtual%20processing%20unit" title=" virtual processing unit"> virtual processing unit</a>, <a href="https://publications.waset.org/abstracts/search?q=computer%20vision" title=" computer vision"> computer vision</a>, <a href="https://publications.waset.org/abstracts/search?q=saliency%20maps" title=" saliency maps"> saliency maps</a>, <a href="https://publications.waset.org/abstracts/search?q=color%20quantization" title=" color quantization"> color quantization</a> </p> <a href="https://publications.waset.org/abstracts/147972/enhancing-the-bionic-eye-a-real-time-image-optimization-framework-to-encode-color-and-spatial-information-into-retinal-prostheses" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/147972.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">152</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">3645</span> Improved Color-Based K-Mean Algorithm for Clustering of Satellite Image</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sangeeta%20Yadav">Sangeeta Yadav</a>, <a href="https://publications.waset.org/abstracts/search?q=Mantosh%20Biswas"> Mantosh Biswas</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we proposed an improved color based K-mean algorithm for clustering of satellite Image (SAR). Our method comprises of two stages. The first step is an interactive selection process where users are required to input the number of colors (ncolor), number of clusters, and then they are prompted to select the points in each color cluster. In the second step these points are given as input to K-mean clustering algorithm that clusters the image based on color and Minimum Square Euclidean distance. The proposed method reduces the mixed pixel problem to a great extent. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=cluster" title="cluster">cluster</a>, <a href="https://publications.waset.org/abstracts/search?q=ncolor%20method" title=" ncolor method"> ncolor method</a>, <a href="https://publications.waset.org/abstracts/search?q=K-mean%20method" title=" K-mean method"> K-mean method</a>, <a href="https://publications.waset.org/abstracts/search?q=interactive%20selection%20process" title=" interactive selection process"> interactive selection process</a> </p> <a href="https://publications.waset.org/abstracts/64532/improved-color-based-k-mean-algorithm-for-clustering-of-satellite-image" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/64532.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">297</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">3644</span> Color Image Compression/Encryption/Contour Extraction using 3L-DWT and SSPCE Method</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ali%20A.%20Ukasha">Ali A. Ukasha</a>, <a href="https://publications.waset.org/abstracts/search?q=Majdi%20F.%20Elbireki"> Majdi F. Elbireki</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohammad%20F.%20Abdullah"> Mohammad F. Abdullah</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Data security needed in data transmission, storage, and communication to ensure the security. This paper is divided into two parts. This work interests with the color image which is decomposed into red, green and blue channels. The blue and green channels are compressed using 3-levels discrete wavelet transform. The Arnold transform uses to changes the locations of red image channel pixels as image scrambling process. Then all these channels are encrypted separately using the key image that has same original size and are generating using private keys and modulo operations. Performing the X-OR and modulo operations between the encrypted channels images for image pixel values change purpose. The extracted contours from color images recovery can be obtained with accepted level of distortion using single step parallel contour extraction (SSPCE) method. Experiments have demonstrated that proposed algorithm can fully encrypt 2D Color images and completely reconstructed without any distortion. Also shown that the analyzed algorithm has extremely large security against some attacks like salt and pepper and Jpeg compression. Its proof that the color images can be protected with a higher security level. The presented method has easy hardware implementation and suitable for multimedia protection in real time applications such as wireless networks and mobile phone services. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=SSPCE%20method" title="SSPCE method">SSPCE method</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20compression%20and%20salt%20and%20peppers%20attacks" title=" image compression and salt and peppers attacks"> image compression and salt and peppers attacks</a>, <a href="https://publications.waset.org/abstracts/search?q=bitplanes%20decomposition" title=" bitplanes decomposition"> bitplanes decomposition</a>, <a href="https://publications.waset.org/abstracts/search?q=Arnold%20transform" title=" Arnold transform"> Arnold transform</a>, <a href="https://publications.waset.org/abstracts/search?q=color%20image" title=" color image"> color image</a>, <a href="https://publications.waset.org/abstracts/search?q=wavelet%20transform" title=" wavelet transform"> wavelet transform</a>, <a href="https://publications.waset.org/abstracts/search?q=lossless%20image%20encryption" title=" lossless image encryption"> lossless image encryption</a> </p> <a href="https://publications.waset.org/abstracts/18519/color-image-compressionencryptioncontour-extraction-using-3l-dwt-and-sspce-method" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/18519.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">518</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3643</span> The Resistance Reader Program Based on Image Processing</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Janpen%20Srijan">Janpen Srijan</a>, <a href="https://publications.waset.org/abstracts/search?q=Nahathai%20%20Tanmang"> Nahathai Tanmang</a>, <a href="https://publications.waset.org/abstracts/search?q=Thanit%20Purathanang"> Thanit Purathanang</a>, <a href="https://publications.waset.org/abstracts/search?q=Anun%20Dowchern"> Anun Dowchern</a>, <a href="https://publications.waset.org/abstracts/search?q=Saksit%20Summart"> Saksit Summart</a>, <a href="https://publications.waset.org/abstracts/search?q=Seangduan%20Kampimpa"> Seangduan Kampimpa </a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents the resistance reader program based on image processing by using MATLAB. The proposed program is divided into six parts; the first part is the web camera; the second part is a watt selection before shooting the resistor; the third part is a part of finding the position of the color on the mid-point of resistor; the fourth part is a part of identifying color code of the resistor; the fifth part is a part of taking the number of values for each color for resistance calculation and the last part is a part of displaying result of resistance value. The experimental result of the resistance reader program based on image processing was able to display the resistance value of resistor. The accuracy of proposed program is 85 percent for 1 watt resistor. It has 15 percent of reading error because a problem with the color code of some resistor was too bright. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=resistance%20reader%20program" title="resistance reader program">resistance reader program</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=resistor" title=" resistor"> resistor</a>, <a href="https://publications.waset.org/abstracts/search?q=MATLAB" title=" MATLAB"> MATLAB</a> </p> <a href="https://publications.waset.org/abstracts/39785/the-resistance-reader-program-based-on-image-processing" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/39785.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">389</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">3642</span> Image Enhancement of Histological Slides by Using Nonlinear Transfer Function</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=D.%20Suman">D. Suman</a>, <a href="https://publications.waset.org/abstracts/search?q=B.%20Nikitha"> B. Nikitha</a>, <a href="https://publications.waset.org/abstracts/search?q=J.%20Sarvani"> J. Sarvani</a>, <a href="https://publications.waset.org/abstracts/search?q=V.%20Archana"> V. Archana</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Histological slides provide clinical diagnostic information about the subjects from the ancient times. Even with the advent of high resolution imaging cameras the image tend to have some background noise which makes the analysis complex. A study of the histological slides is done by using a nonlinear transfer function based image enhancement method. The method processes the raw, color images acquired from the biological microscope, which, in general, is associated with background noise. The images usually appearing blurred does not convey the intended information. In this regard, an enhancement method is proposed and implemented on 50 histological slides of human tissue by using nonlinear transfer function method. The histological image is converted into HSV color image. The luminance value of the image is enhanced (V component) because change in the H and S components could change the color balance between HSV components. The HSV image is divided into smaller blocks for carrying out the dynamic range compression by using a linear transformation function. Each pixel in the block is enhanced based on the contrast of the center pixel and its neighborhood. After the processing the V component, the HSV image is transformed into a colour image. The study has shown improvement of the characteristics of the image so that the significant details of the histological images were improved. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=HSV%20space" title="HSV space">HSV space</a>, <a href="https://publications.waset.org/abstracts/search?q=histology" title=" histology"> histology</a>, <a href="https://publications.waset.org/abstracts/search?q=enhancement" title=" enhancement"> enhancement</a>, <a href="https://publications.waset.org/abstracts/search?q=image" title=" image"> image</a> </p> <a href="https://publications.waset.org/abstracts/12167/image-enhancement-of-histological-slides-by-using-nonlinear-transfer-function" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/12167.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">329</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">3641</span> A Similar Image Retrieval System for Auroral All-Sky Images Based on Local Features and Color Filtering</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Takanori%20Tanaka">Takanori Tanaka</a>, <a href="https://publications.waset.org/abstracts/search?q=Daisuke%20Kitao"> Daisuke Kitao</a>, <a href="https://publications.waset.org/abstracts/search?q=Daisuke%20Ikeda"> Daisuke Ikeda</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The aurora is an attractive phenomenon but it is difficult to understand the whole mechanism of it. An approach of data-intensive science might be an effective approach to elucidate such a difficult phenomenon. To do that we need labeled data, which shows when and what types of auroras, have appeared. In this paper, we propose an image retrieval system for auroral all-sky images, some of which include discrete and diffuse aurora, and the other do not any aurora. The proposed system retrieves images which are similar to the query image by using a popular image recognition method. Using 300 all-sky images obtained at Tromso Norway, we evaluate two methods of image recognition methods with or without our original color filtering method. The best performance is achieved when SIFT with the color filtering is used and its accuracy is 81.7% for discrete auroras and 86.7% for diffuse auroras. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=data-intensive%20science" title="data-intensive science">data-intensive science</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=content-based%20image%20retrieval" title=" content-based image retrieval"> content-based image retrieval</a>, <a href="https://publications.waset.org/abstracts/search?q=aurora" title=" aurora"> aurora</a> </p> <a href="https://publications.waset.org/abstracts/19532/a-similar-image-retrieval-system-for-auroral-all-sky-images-based-on-local-features-and-color-filtering" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/19532.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">449</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">3640</span> New Efficient Method for Coding Color Images</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Walaa%20M.Abd-Elhafiez">Walaa M.Abd-Elhafiez</a>, <a href="https://publications.waset.org/abstracts/search?q=Wajeb%20Gharibi"> Wajeb Gharibi </a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper a novel color image compression technique for efficient storage and delivery of data is proposed. The proposed compression technique started by RGB to YCbCr color transformation process. Secondly, the canny edge detection method is used to classify the blocks into edge and non-edge blocks. Each color component Y, Cb, and Cr compressed by discrete cosine transform (DCT) process, quantizing and coding step by step using adaptive arithmetic coding. Our technique is concerned with the compression ratio, bits per pixel and peak signal to noise ratio, and produce better results than JPEG and more recent published schemes (like, CBDCT-CABS and MHC). The provided experimental results illustrate the proposed technique which is efficient and feasible in terms of compression ratio, bits per pixel and peak signal to noise ratio. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=image%20compression" title="image compression">image compression</a>, <a href="https://publications.waset.org/abstracts/search?q=color%20image" title=" color image"> color image</a>, <a href="https://publications.waset.org/abstracts/search?q=q-coder" title=" q-coder"> q-coder</a>, <a href="https://publications.waset.org/abstracts/search?q=quantization" title=" quantization"> quantization</a>, <a href="https://publications.waset.org/abstracts/search?q=edge-detection" title=" edge-detection"> edge-detection</a> </p> <a href="https://publications.waset.org/abstracts/2342/new-efficient-method-for-coding-color-images" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/2342.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">329</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">3639</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">3638</span> Multi-Spectral Medical Images Enhancement Using a Weber’s law</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Muna%20F.%20Al-Sammaraie">Muna F. Al-Sammaraie</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The aim of this research is to present a multi spectral image enhancement methods used to achieve highly real digital image populates only a small portion of the available range of digital values. Also, a quantitative measure of image enhancement is presented. This measure is related with concepts of the Webers Low of the human visual system. For decades, several image enhancement techniques have been proposed. Although most techniques require profuse amount of advance and critical steps, the result for the perceive image are not as satisfied. This study involves changing the original values so that more of the available range is used; then increases the contrast between features and their backgrounds. It consists of reading the binary image on the basis of pixels taking them byte-wise and displaying it, calculating the statistics of an image, automatically enhancing the color of the image based on statistics calculation using algorithms and working with RGB color bands. Finally, the enhanced image is displayed along with image histogram. A number of experimental results illustrated the performance of these algorithms. Particularly the quantitative measure has helped to select optimal processing parameters: the best parameters and transform. <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=multi-spectral" title=" multi-spectral"> multi-spectral</a>, <a href="https://publications.waset.org/abstracts/search?q=RGB" title=" RGB"> RGB</a>, <a href="https://publications.waset.org/abstracts/search?q=histogram" title=" histogram"> histogram</a> </p> <a href="https://publications.waset.org/abstracts/8574/multi-spectral-medical-images-enhancement-using-a-webers-law" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/8574.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">328</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">3637</span> A Survey on Lossless Compression of Bayer Color Filter Array Images</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Alina%20Trifan">Alina Trifan</a>, <a href="https://publications.waset.org/abstracts/search?q=Ant%C3%B3nio%20J.%20R.%20Neves"> António J. R. Neves</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Although most digital cameras acquire images in a raw format, based on a Color Filter Array that arranges RGB color filters on a square grid of photosensors, most image compression techniques do not use the raw data; instead, they use the rgb result of an interpolation algorithm of the raw data. This approach is inefficient and by performing a lossless compression of the raw data, followed by pixel interpolation, digital cameras could be more power efficient and provide images with increased resolution given that the interpolation step could be shifted to an external processing unit. In this paper, we conduct a survey on the use of lossless compression algorithms with raw Bayer images. Moreover, in order to reduce the effect of the transition between colors that increase the entropy of the raw Bayer image, we split the image into three new images corresponding to each channel (red, green and blue) and we study the same compression algorithms applied to each one individually. This simple pre-processing stage allows an improvement of more than 15% in predictive based methods. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=bayer%20image" title="bayer image">bayer image</a>, <a href="https://publications.waset.org/abstracts/search?q=CFA" title=" CFA"> CFA</a>, <a href="https://publications.waset.org/abstracts/search?q=lossless%20compression" title=" lossless compression"> lossless compression</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20coding%20standards" title=" image coding standards"> image coding standards</a> </p> <a href="https://publications.waset.org/abstracts/39918/a-survey-on-lossless-compression-of-bayer-color-filter-array-images" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/39918.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">320</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">3636</span> A Custom Convolutional Neural Network with Hue, Saturation, Value Color for Malaria Classification</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ghazala%20Hcini">Ghazala Hcini</a>, <a href="https://publications.waset.org/abstracts/search?q=Imen%20Jdey"> Imen Jdey</a>, <a href="https://publications.waset.org/abstracts/search?q=Hela%20Ltifi"> Hela Ltifi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Malaria disease should be considered and handled as a potential restorative catastrophe. One of the most challenging tasks in the field of microscopy image processing is due to differences in test design and vulnerability of cell classifications. In this article, we focused on applying deep learning to classify patients by identifying images of infected and uninfected cells. We performed multiple forms, counting a classification approach using the Hue, Saturation, Value (HSV) color space. HSV is used since of its superior ability to speak to image brightness; at long last, for classification, a convolutional neural network (CNN) architecture is created. Clusters of focus were used to deliver the classification. The highlights got to be forbidden, and a few more clamor sorts are included in the information. The suggested method has a precision of 99.79%, a recall value of 99.55%, and provides 99.96% accuracy. <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=convolutional%20neural%20network" title=" convolutional neural network"> convolutional neural network</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=color%20transformation" title=" color transformation"> color transformation</a>, <a href="https://publications.waset.org/abstracts/search?q=HSV%20color" title=" HSV color"> HSV color</a>, <a href="https://publications.waset.org/abstracts/search?q=malaria%20diagnosis" title=" malaria diagnosis"> malaria diagnosis</a>, <a href="https://publications.waset.org/abstracts/search?q=malaria%20cells%20images" title=" malaria cells images"> malaria cells images</a> </p> <a href="https://publications.waset.org/abstracts/161232/a-custom-convolutional-neural-network-with-hue-saturation-value-color-for-malaria-classification" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/161232.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">88</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">3635</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">3634</span> Clustering Color Space, Time Interest Points for Moving Objects</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Insaf%20Bellamine">Insaf Bellamine</a>, <a href="https://publications.waset.org/abstracts/search?q=Hamid%20Tairi"> Hamid Tairi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Detecting moving objects in sequences is an essential step for video analysis. This paper mainly contributes to the Color Space-Time Interest Points (CSTIP) extraction and detection. We propose a new method for detection of moving objects. Two main steps compose the proposed method. First, we suggest to apply the algorithm of the detection of Color Space-Time Interest Points (CSTIP) on both components of the Color Structure-Texture Image Decomposition which is based on a Partial Differential Equation (PDE): a color geometric structure component and a color texture component. A descriptor is associated to each of these points. In a second stage, we address the problem of grouping the points (CSTIP) into clusters. Experiments and comparison to other motion detection methods on challenging sequences show the performance of the proposed method and its utility for video analysis. Experimental results are obtained from very different types of videos, namely sport videos and animation movies. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Color%20Space-Time%20Interest%20Points%20%28CSTIP%29" title="Color Space-Time Interest Points (CSTIP)">Color Space-Time Interest Points (CSTIP)</a>, <a href="https://publications.waset.org/abstracts/search?q=Color%20Structure-Texture%20Image%20Decomposition" title=" Color Structure-Texture Image Decomposition"> Color Structure-Texture Image Decomposition</a>, <a href="https://publications.waset.org/abstracts/search?q=Motion%20Detection" title=" Motion Detection"> Motion Detection</a>, <a href="https://publications.waset.org/abstracts/search?q=clustering" title=" clustering"> clustering</a> </p> <a href="https://publications.waset.org/abstracts/21989/clustering-color-space-time-interest-points-for-moving-objects" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/21989.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">378</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">3633</span> A Method of the Semantic on Image Auto-Annotation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Lin%20Huo">Lin Huo</a>, <a href="https://publications.waset.org/abstracts/search?q=Xianwei%20Liu"> Xianwei Liu</a>, <a href="https://publications.waset.org/abstracts/search?q=Jingxiong%20Zhou"> Jingxiong Zhou</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Recently, due to the existence of semantic gap between image visual features and human concepts, the semantic of image auto-annotation has become an important topic. Firstly, by extract low-level visual features of the image, and the corresponding Hash method, mapping the feature into the corresponding Hash coding, eventually, transformed that into a group of binary string and store it, image auto-annotation by search is a popular method, we can use it to design and implement a method of image semantic auto-annotation. Finally, Through the test based on the Corel image set, and the results show that, this method is effective. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=image%20auto-annotation" title="image auto-annotation">image auto-annotation</a>, <a href="https://publications.waset.org/abstracts/search?q=color%20correlograms" title=" color correlograms"> color correlograms</a>, <a href="https://publications.waset.org/abstracts/search?q=Hash%20code" title=" Hash code"> Hash code</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/15628/a-method-of-the-semantic-on-image-auto-annotation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/15628.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">497</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3632</span> Leukocyte Detection Using Image Stitching and Color Overlapping Windows</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Lina">Lina</a>, <a href="https://publications.waset.org/abstracts/search?q=Arlends%20Chris"> Arlends Chris</a>, <a href="https://publications.waset.org/abstracts/search?q=Bagus%20Mulyawan"> Bagus Mulyawan</a>, <a href="https://publications.waset.org/abstracts/search?q=Agus%20B.%20Dharmawan"> Agus B. Dharmawan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Blood cell analysis plays a significant role in the diagnosis of human health. As an alternative to the traditional technique conducted by laboratory technicians, this paper presents an automatic white blood cell (leukocyte) detection system using Image Stitching and Color Overlapping Windows. The advantage of this method is to present a detection technique of white blood cells that are robust to imperfect shapes of blood cells with various image qualities. The input for this application is images from a microscope-slide translation video. The preprocessing stage is performed by stitching the input images. First, the overlapping parts of the images are determined, then stitching and blending processes of two input images are performed. Next, the Color Overlapping Windows is performed for white blood cell detection which consists of color filtering, window candidate checking, window marking, finds window overlaps, and window cropping processes. Experimental results show that this method could achieve an average of 82.12% detection accuracy of the leukocyte images. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=color%20overlapping%20windows" title="color overlapping windows">color overlapping windows</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20stitching" title=" image stitching"> image stitching</a>, <a href="https://publications.waset.org/abstracts/search?q=leukocyte%20detection" title=" leukocyte detection"> leukocyte detection</a>, <a href="https://publications.waset.org/abstracts/search?q=white%20blood%20cell%20detection" title=" white blood cell detection"> white blood cell detection</a> </p> <a href="https://publications.waset.org/abstracts/46973/leukocyte-detection-using-image-stitching-and-color-overlapping-windows" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/46973.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">310</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">3631</span> An Online 3D Modeling Method Based on a Lossless Compression Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jiankang%20Wang">Jiankang Wang</a>, <a href="https://publications.waset.org/abstracts/search?q=Hongyang%20Yu"> Hongyang Yu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper proposes a portable online 3D modeling method. The method first utilizes a depth camera to collect data and compresses the depth data using a frame-by-frame lossless data compression method. The color image is encoded using the H.264 encoding format. After the cloud obtains the color image and depth image, a 3D modeling method based on bundlefusion is used to complete the 3D modeling. The results of this study indicate that this method has the characteristics of portability, online, and high efficiency and has a wide range of application prospects. <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=bundlefusion" title=" bundlefusion"> bundlefusion</a>, <a href="https://publications.waset.org/abstracts/search?q=lossless%20compression" title=" lossless compression"> lossless compression</a>, <a href="https://publications.waset.org/abstracts/search?q=depth%20image" title=" depth image"> depth image</a> </p> <a href="https://publications.waset.org/abstracts/163266/an-online-3d-modeling-method-based-on-a-lossless-compression-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/163266.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">82</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">3630</span> Study of the Optical Illusion Effects of Color Contrasts on Body Image Perception</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=A.%20Hadj%20Taieb">A. Hadj Taieb</a>, <a href="https://publications.waset.org/abstracts/search?q=H.%20Ennouri"> H. Ennouri</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The current study aimed to investigate the effect that optical illusion garments have on a woman’s self-perception of her own body shape. First, we created different optical illusion garment by using color contrasts. Second, a short survey based on visual perception is addressed to women in order to compare the different optical illusion garments to determine if they met the established 'ideal' body shape. A ‘visual analysis method’ was used to investigate the clothing models with optical illusions. The theories in relation with the optical illusion were used through this method. The effects of the optical illusion of color contrast on body shape in the fashion sector were tried to be revealed. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=optical%20illusion" title="optical illusion">optical illusion</a>, <a href="https://publications.waset.org/abstracts/search?q=color%20contrasts" title=" color contrasts"> color contrasts</a>, <a href="https://publications.waset.org/abstracts/search?q=body%20image%20perception" title=" body image perception"> body image perception</a>, <a href="https://publications.waset.org/abstracts/search?q=self-esteem" title=" self-esteem"> self-esteem</a> </p> <a href="https://publications.waset.org/abstracts/77953/study-of-the-optical-illusion-effects-of-color-contrasts-on-body-image-perception" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/77953.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">273</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=color%20image&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=color%20image&page=3">3</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=color%20image&page=4">4</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=color%20image&page=5">5</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=color%20image&page=6">6</a></li> <li class="page-item"><a class="page-link" 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