The Experts below are selected from a list of 3747 Experts worldwide ranked by ideXlab platform
B Bhargavendra - One of the best experts on this subject based on the ideXlab platform.
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Image Retrieval by Fusion of Color Histogram and Edge Orientation Histogram and wavelets
International Journal of Research, 2017Co-Authors: Seetharam Shiva Krishna, B BhargavendraAbstract:It has very important practical significance to analyze and research minority costume from the perspective of computer vision for minority culture protection and inheritance. As first exploration in minority costume image retrieval, this paper proposed a novel image feature representation method to describe the rich information of minority costume image. Firstly, the color Histogram and edge Orientation Histogram are calculated for divided sub-blocks of minority costume image. Then, the final feature vector for minority costume image is formed by effective fusion of color Histogram and edge Orientation Histogram. Finally, the improved Canberra distance is introduced to measure the similarity between query image and retrieval image. We have evaluated the performances of the proposed algorithm on self-build minority costume image dataset, and the experimental results show that our method can effectively express the integrated feature of minority costume images, including color, texture, shape and spatial information. Compared with some conventional methods, our method has higher and stable retrieval accuracy.
Allwin Stephen - One of the best experts on this subject based on the ideXlab platform.
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Research Article Nominated Texture Based Cervical Cancer Classification
2016Co-Authors: Edwin Jayasingh Mariarputham, Allwin StephenAbstract:Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Accurate classification of Pap smear images becomes the challenging task in medical image processing. This can be improved in two ways. One way is by selecting suitable well defined specific features and the other is by selecting the best classifier. This paper presents a nominated texture based cervical cancer (NTCC) classification systemwhich classifies the Pap smear images into any one of the seven classes.This can be achieved by extracting well defined texture features and selecting best classifier. Seven sets of texture features (24 features) are extracted which include relative size of nucleus and cytoplasm, dynamic range and first four moments of intensities of nucleus and cytoplasm, relative displacement of nucleus within the cytoplasm, gray level cooccurrence matrix, local binary pattern Histogram, tamura features, and edge Orientation Histogram. Few types of support vectormachine (SVM) and neural network (NN) classifiers are used for the classification. The performance of the NTCC algorithm is tested and compared to othe
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Nominated Texture Based Cervical Cancer Classification
Hindawi Limited, 2015Co-Authors: Edwin Jayasingh Mariarputham, Allwin StephenAbstract:Accurate classification of Pap smear images becomes the challenging task in medical image processing. This can be improved in two ways. One way is by selecting suitable well defined specific features and the other is by selecting the best classifier. This paper presents a nominated texture based cervical cancer (NTCC) classification system which classifies the Pap smear images into any one of the seven classes. This can be achieved by extracting well defined texture features and selecting best classifier. Seven sets of texture features (24 features) are extracted which include relative size of nucleus and cytoplasm, dynamic range and first four moments of intensities of nucleus and cytoplasm, relative displacement of nucleus within the cytoplasm, gray level cooccurrence matrix, local binary pattern Histogram, tamura features, and edge Orientation Histogram. Few types of support vector machine (SVM) and neural network (NN) classifiers are used for the classification. The performance of the NTCC algorithm is tested and compared to other algorithms on public image database of Herlev University Hospital, Denmark, with 917 Pap smear images. The output of SVM is found to be best for the most of the classes and better results for the remaining classes
B Sreedevi - One of the best experts on this subject based on the ideXlab platform.
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image retrieval by fusion of color Histogram and edge Orientation Histogram based on wavelet
International Journal of Research, 2017Co-Authors: Devunoori Mounika, B SreedeviAbstract:It has very crucial realistic importance to research and research minority gown from the angle of pc imaginative and prescient for minority life-style protection and inheritance. As first exploration in minority get dressed image retrieval, this paper proposed a unique picture characteristic instance technique to offer a reason behind the rich records of minority get dressed photograph. Firstly, the color Histogram and detail Orientation Histogram are calculated for divided sub-blocks of minority get dressed photo. Then, the final function vector for minority dress photo is typical with the resource of effective fusion of color Histogram and detail Orientation Histogram. Finally, the improved Canberra distance is delivered to diploma the similarity amongst query photograph and retrieval photograph. We have evaluated the performances of the proposed algorithm on self-assemble minority robe photograph dataset, and the experimental consequences display that our Approach can efficiently specific the blanketed function of minority dress images, consisting of coloration, texture, shape and spatial facts. Compared with some conventional techniques, our approach has higher and solid retrieval accuracy.
Seetharam Shiva Krishna - One of the best experts on this subject based on the ideXlab platform.
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Image Retrieval by Fusion of Color Histogram and Edge Orientation Histogram and wavelets
International Journal of Research, 2017Co-Authors: Seetharam Shiva Krishna, B BhargavendraAbstract:It has very important practical significance to analyze and research minority costume from the perspective of computer vision for minority culture protection and inheritance. As first exploration in minority costume image retrieval, this paper proposed a novel image feature representation method to describe the rich information of minority costume image. Firstly, the color Histogram and edge Orientation Histogram are calculated for divided sub-blocks of minority costume image. Then, the final feature vector for minority costume image is formed by effective fusion of color Histogram and edge Orientation Histogram. Finally, the improved Canberra distance is introduced to measure the similarity between query image and retrieval image. We have evaluated the performances of the proposed algorithm on self-build minority costume image dataset, and the experimental results show that our method can effectively express the integrated feature of minority costume images, including color, texture, shape and spatial information. Compared with some conventional methods, our method has higher and stable retrieval accuracy.
Feng Huanqing - One of the best experts on this subject based on the ideXlab platform.
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recognition of chinese sign language based on Orientation Histogram
Computer Simulation, 2009Co-Authors: Feng HuanqingAbstract:Traditional recognition of Chinese sign language mostly uses static feature vectors.This paper proposes an algorithm that extracts efficient feature vectors to recognize a hand gesture for sign language.The proposed algorithm recognizes hand gesture based on visual information without using any special gesture glove.To recognize hand gesture,the proposed method is divided into three steps.First,by using an edge-based hand area search algorithm,a hand block is found and segmented efficiently from the monochrome input images.Secondly,if hand area is successfully extracted,the feature vectors representing the hand shape are analyzed applying Orientation Histogram scheme.Also,the feature vectors of moving hand is obtained by motion estimation.Lastly,the hand gesture is recognized by feature vectors of hand's shape and movements.The proposed algorithm can not only segment the hand area,but also extract the feature vectors from the gray scaled motion images representing language words.