The Experts below are selected from a list of 216 Experts worldwide ranked by ideXlab platform
Yu Cheng - One of the best experts on this subject based on the ideXlab platform.
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An improved collaborative representation based classification with regularized least square (CRCRLS) method for robust face recognition
Neurocomputing, 2016Co-Authors: Yu Cheng, Hongcai Chen, Zhigang Jin, Tao Gao, Nikola KasabovAbstract:Fast and robust face recognition is a challenging research topic in the field of computer vision. A recently proposed Collaborative Representation based Classification with Regularized Least Square (CRCRLS) algorithm shows very lower computational cost but with poor robustness. In order to solve this problem, we propose an improved CRCRLS method. Firstly, the image Gabor features were extracted and used to construct Initial Dictionary. Secondly, we learn a discriminative Dictionary by a label consistent K-SVD (LC-KSVD) method which combines the sparse coding error with the reconstruction error and the classification error. Finally, l2-norm of coding residual in CRCRLS is computed and the classification problem is transformed into solving linear programing problem. Experiments on two benchmark face databases with variations of illumination, expression, occlusion show that the proposed method can achieve high classification accuracy and has a very low time-consuming.
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A fast and robust face recognition approach combining Gabor learned dictionaries and collaborative representation
International Journal of Machine Learning and Cybernetics, 2015Co-Authors: Yu Cheng, Hongcai Chen, Yanchun ZhangAbstract:Proposed is a simple yet fast and robust approach to face recognition. This approach is developed specifically to address the challenges due to variations of illumination, expression and occlusion, when studying the facial images of a large population. The proposed approach exploits an improved collaborative representation algorithm. First, we construct an Initial Dictionary by extracting the multi-scale and multi-orientation Gabor features of the image. Second, we design a new discriminative Dictionary by an improved K-SVD algorithm, so that the query sample can be better represented for classification. Finally, l 2-norm of coding residual is calculated by collaborative representation based classification with regularized least square method (CRC-RLS) and the class of test sample is obtained. Experiments on two benchmark face datasets show that the proposed method can achieve high classification accuracy and is comparatively low in terms of time-consumption, compared to the CRC-RLS algorithm.
Hosseini Rabbani - One of the best experts on this subject based on the ideXlab platform.
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Three-dimensional curvelet-based Dictionary learning for speckle noise removal of optical coherence tomography.
Biomedical optics express, 2020Co-Authors: Mahad Esmaeili, Alireza Mehri Dehnavi, Fedra Hajizadeh, Hosseini RabbaniAbstract:Optical coherence tomography (OCT) is a recently emerging non-invasive diagnostic tool useful in several medical applications such as ophthalmology, cardiology, gastroenterology and dermatology. One of the major problems with OCT pertains to its low contrast due to the presence of multiplicative speckle noise, which limits the signal-to-noise ratio (SNR) and obscures low-intensity and small features. In this paper, we recommend a new method using the 3D curvelet based K-times singular value decomposition (K-SVD) algorithm for speckle noise reduction and contrast enhancement of the intra-retinal layers of 3D Spectral-Domain OCT (3D-SDOCT) images. In order to benefit from the near-optimum properties of curvelet transform (such as good directional selectivity) on top of Dictionary learning, we propose a new plan in Dictionary learning by using the curvelet atoms as the Initial Dictionary. For this reason, the curvelet transform of the noisy image is taken and then the noisy coefficients matrix in each scale, rotation and spatial coordinates is passed through the K-SVD denoising algorithm with predefined 3D Initial Dictionary that is adaptively selected from thresholded coefficients in the same subband of the image. During the denoising of curvelet coefficients, we can also modify them for the purpose of contrast enhancement of intra-retinal layers. We demonstrate the ability of our proposed algorithm in the speckle noise reduction of 17 publicly available 3D OCT data sets, each of which contains 100 B-scans of size 512×1000 with and without neovascular age-related macular degeneration (AMD) images acquired using SDOCT, Bioptigen imaging systems. Experimental results show that an improvement from 1.27 to 7.81 in contrast to noise ratio (CNR), and from 38.09 to 1983.07 in equivalent number of looks (ENL) is achieved, which would outperform existing state-of-the-art OCT despeckling methods.
Paul G Biondich - One of the best experts on this subject based on the ideXlab platform.
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concept Dictionary creation and maintenance under resource constraints lessons from the ampath medical record system
American Medical Informatics Association Annual Symposium, 2007Co-Authors: Martin C Were, Burke W Mamlin, William M Tierney, Benjamin A Wolfe, Paul G BiondichAbstract:The challenges of creating and maintaining concept dictionaries are compounded in resource-limited settings. Approaches to alleviate this burden need to be based on information derived in these settings. We created a concept Dictionary and evaluated new concept proposals for an open source EMR in a resource-limited setting. Overall, 87% of the concepts in the Initial Dictionary were used. There were 5137 new concepts proposed, with 77% of these proposed only once. Further characterization of new concept proposals revealed that 41% were due to deficiency in the existing Dictionary, and 19% were synonyms to existing concepts. 25% of the requests contained misspellings, 41% were complex terms, and 17% were ambiguous. Given the resource-intensive nature of Dictionary creation and maintenance, there should be considerations for centralizing the concept Dictionary service, using standards, prioritizing concept proposals, and redesigning the user-interface to reduce this burden in settings with limited resources.
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AMIA - Concept Dictionary creation and maintenance under resource constraints: lessons from the AMPATH Medical Record System.
AMIA ... Annual Symposium proceedings. AMIA Symposium, 2007Co-Authors: Martin C Were, Burke W Mamlin, William M Tierney, Benjamin A Wolfe, Paul G BiondichAbstract:The challenges of creating and maintaining concept dictionaries are compounded in resource-limited settings. Approaches to alleviate this burden need to be based on information derived in these settings. We created a concept Dictionary and evaluated new concept proposals for an open source EMR in a resource-limited setting. Overall, 87% of the concepts in the Initial Dictionary were used. There were 5137 new concepts proposed, with 77% of these proposed only once. Further characterization of new concept proposals revealed that 41% were due to deficiency in the existing Dictionary, and 19% were synonyms to existing concepts. 25% of the requests contained misspellings, 41% were complex terms, and 17% were ambiguous. Given the resource-intensive nature of Dictionary creation and maintenance, there should be considerations for centralizing the concept Dictionary service, using standards, prioritizing concept proposals, and redesigning the user-interface to reduce this burden in settings with limited resources.
Hongcai Chen - One of the best experts on this subject based on the ideXlab platform.
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An improved collaborative representation based classification with regularized least square (CRCRLS) method for robust face recognition
Neurocomputing, 2016Co-Authors: Yu Cheng, Hongcai Chen, Zhigang Jin, Tao Gao, Nikola KasabovAbstract:Fast and robust face recognition is a challenging research topic in the field of computer vision. A recently proposed Collaborative Representation based Classification with Regularized Least Square (CRCRLS) algorithm shows very lower computational cost but with poor robustness. In order to solve this problem, we propose an improved CRCRLS method. Firstly, the image Gabor features were extracted and used to construct Initial Dictionary. Secondly, we learn a discriminative Dictionary by a label consistent K-SVD (LC-KSVD) method which combines the sparse coding error with the reconstruction error and the classification error. Finally, l2-norm of coding residual in CRCRLS is computed and the classification problem is transformed into solving linear programing problem. Experiments on two benchmark face databases with variations of illumination, expression, occlusion show that the proposed method can achieve high classification accuracy and has a very low time-consuming.
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A fast and robust face recognition approach combining Gabor learned dictionaries and collaborative representation
International Journal of Machine Learning and Cybernetics, 2015Co-Authors: Yu Cheng, Hongcai Chen, Yanchun ZhangAbstract:Proposed is a simple yet fast and robust approach to face recognition. This approach is developed specifically to address the challenges due to variations of illumination, expression and occlusion, when studying the facial images of a large population. The proposed approach exploits an improved collaborative representation algorithm. First, we construct an Initial Dictionary by extracting the multi-scale and multi-orientation Gabor features of the image. Second, we design a new discriminative Dictionary by an improved K-SVD algorithm, so that the query sample can be better represented for classification. Finally, l 2-norm of coding residual is calculated by collaborative representation based classification with regularized least square method (CRC-RLS) and the class of test sample is obtained. Experiments on two benchmark face datasets show that the proposed method can achieve high classification accuracy and is comparatively low in terms of time-consumption, compared to the CRC-RLS algorithm.
Nikola Kasabov - One of the best experts on this subject based on the ideXlab platform.
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An improved collaborative representation based classification with regularized least square (CRCRLS) method for robust face recognition
Neurocomputing, 2016Co-Authors: Yu Cheng, Hongcai Chen, Zhigang Jin, Tao Gao, Nikola KasabovAbstract:Fast and robust face recognition is a challenging research topic in the field of computer vision. A recently proposed Collaborative Representation based Classification with Regularized Least Square (CRCRLS) algorithm shows very lower computational cost but with poor robustness. In order to solve this problem, we propose an improved CRCRLS method. Firstly, the image Gabor features were extracted and used to construct Initial Dictionary. Secondly, we learn a discriminative Dictionary by a label consistent K-SVD (LC-KSVD) method which combines the sparse coding error with the reconstruction error and the classification error. Finally, l2-norm of coding residual in CRCRLS is computed and the classification problem is transformed into solving linear programing problem. Experiments on two benchmark face databases with variations of illumination, expression, occlusion show that the proposed method can achieve high classification accuracy and has a very low time-consuming.