The Experts below are selected from a list of 10920 Experts worldwide ranked by ideXlab platform
A N Venetsanopoulos - One of the best experts on this subject based on the ideXlab platform.
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vector order statistics operators as color Edge Detectors
Systems Man and Cybernetics, 1996Co-Authors: Panos Trahanias, A N VenetsanopoulosAbstract:Color Edge detection is approached in this paper using vector order statistics. Based on the R-ordering method, a class of color Edge Detectors is defined. These Detectors function as vector operators as opposed to component-wise operators. Specific Edge Detectors can be obtained as special cases of this class. Various such Detectors are defined and analyzed. Experimental results show the noise robustness of the vector order statistics operators. A quantitative evaluation and comparison to other color Edge Detectors favors our approach. Edge detection results obtained from real color images demonstrate the effectiveness of the proposed approach in real applications.
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color Edge detection using vector order statistics
IEEE Transactions on Image Processing, 1993Co-Authors: Panos Trahanias, A N VenetsanopoulosAbstract:A method is proposed whereby a color image is treated as a vector field and the Edge information carried directly by the vectors is exploited. A class of color Edge Detectors is defined as the minimum over the magnitudes of linear combinations of the sorted vector samples. From this class, a specific Edge detector is obtained and its performance characteristics studied. Results of a quantitative evaluation and comparison to other color Edge Detectors, using Pratt's (1991) figure of merit and an artificially generated test image, are presented. Edge detection results obtained for real color images demonstrate the efficiency of the detector. >
Penglang Shui - One of the best experts on this subject based on the ideXlab platform.
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noise robust color Edge detection using anisotropic morphological directional derivative matrix
Signal Processing, 2019Co-Authors: Penglang ShuiAbstract:Abstract In this paper, a color Edge detector using the anisotropic morphological directional derivatives (AMDDs) is presented to detect Edges in color images corrupted by Gaussian or impulsive noise. The AMDD matrix, robust to impulsive noise owing to the underlying morphological operators, is constructed to represent Edge information at each pixel of a color image. The color Edge strength map and color Edge direction map of a color image are extracted by spatial and directional matched filtering and singular value decomposition of the AMDD matrices. Embedding them in the route of the Canny detector yields a noise-robust color Edge detector. Moreover, a color image database with groundtruths (GTs) of Edges are built. The GT of a color image is generated in three steps. First, the contours and results of multiple color Edge Detectors are fused into a candidate Edge map (CEM). Next, the CEM, the original image, and a special software for Edge modification are sent to twenty experienced observers to modify the CEM. Finally, their results are used to yield the Edge pixels, non-Edge pixels, and don't care regions in the GT by the voting rule. The proposed detector is compared with existing color Edge Detectors on the database.
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noise robust color Edge detector using gradient matrix and anisotropic gaussian directional derivative matrix
Pattern Recognition, 2016Co-Authors: Fuping Wang, Penglang ShuiAbstract:In this paper, a noise-robust color Edge detector using gradient matrix and anisotropic Gaussian directional derivative (ANDD) matrix is proposed. In order to alleviate the conflict between high Edge resolution and noise robustness in the color Canny detector where the isotropic Gaussian kernels and gradient matrix of the R, G, B components are used, the ANDDs of the three components of a color image are arranged into the ANDD matrix. From its singular value decomposition (SVD), the ANDD-based color Edge strength map (CESM) is constructed and is relevant to the pixelwise optimal fusion of the ANDDs of the three components in the sense of color Edge enhancement. The merits and defects of the ANDD-based CESM and gradient-based CESM are contrasted to show their complementarity in color Edge detection. The two CESMs are fused to develop a new color Edge detector. It is compared with the color Canny detector and two recent color Edge Detectors. The results show that the proposed detector attains better detection performance for noiseless and noisy color images corrupted by white Gaussian noise or impulsive noise of small percentage. A color Edge detector using gradient matrix and ANDD matrix is proposed.The SVD of the ANDD matrix is used to determine CESM and CESD of a color image.The new detector has both high Edge resolution and noise-robustness.It attains better performance than the two existing color Edge Detectors.
Enis Gunay - One of the best experts on this subject based on the ideXlab platform.
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efficient Edge detection in digital images using a cellular neural network optimized by differential evolution algorithm
Expert Systems With Applications, 2009Co-Authors: Alper Basturk, Enis GunayAbstract:A cellular neural network (CNN) based Edge detector optimized by differential evolution (DE) algorithm is presented. Cloning template of the proposed CNN is adaptively tuned by using simple training images. The performance of the proposed Edge detector is evaluated on different test images and compared with popular Edge Detectors from the literature. Simulation results indicate that the proposed CNN operator outperforms competing Edge Detectors and offers superior performance in Edge detection in digital images.
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efficient Edge detection in digital images using a cellular neural network optimized by differential evolution algorithm
Expert Systems With Applications, 2009Co-Authors: Alper Basturk, Enis GunayAbstract:A cellular neural network (CNN) based Edge detector optimized by differential evolution (DE) algorithm is presented. Cloning template of the proposed CNN is adaptively tuned by using simple training images. The performance of the proposed Edge detector is evaluated on different test images and compared with popular Edge Detectors from the literature. Simulation results indicate that the proposed CNN operator outperforms competing Edge Detectors and offers superior performance in Edge detection in digital images.
Panos Trahanias - One of the best experts on this subject based on the ideXlab platform.
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vector order statistics operators as color Edge Detectors
Systems Man and Cybernetics, 1996Co-Authors: Panos Trahanias, A N VenetsanopoulosAbstract:Color Edge detection is approached in this paper using vector order statistics. Based on the R-ordering method, a class of color Edge Detectors is defined. These Detectors function as vector operators as opposed to component-wise operators. Specific Edge Detectors can be obtained as special cases of this class. Various such Detectors are defined and analyzed. Experimental results show the noise robustness of the vector order statistics operators. A quantitative evaluation and comparison to other color Edge Detectors favors our approach. Edge detection results obtained from real color images demonstrate the effectiveness of the proposed approach in real applications.
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color Edge detection using vector order statistics
IEEE Transactions on Image Processing, 1993Co-Authors: Panos Trahanias, A N VenetsanopoulosAbstract:A method is proposed whereby a color image is treated as a vector field and the Edge information carried directly by the vectors is exploited. A class of color Edge Detectors is defined as the minimum over the magnitudes of linear combinations of the sorted vector samples. From this class, a specific Edge detector is obtained and its performance characteristics studied. Results of a quantitative evaluation and comparison to other color Edge Detectors, using Pratt's (1991) figure of merit and an artificially generated test image, are presented. Edge detection results obtained for real color images demonstrate the efficiency of the detector. >
Alper Basturk - One of the best experts on this subject based on the ideXlab platform.
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efficient Edge detection in digital images using a cellular neural network optimized by differential evolution algorithm
Expert Systems With Applications, 2009Co-Authors: Alper Basturk, Enis GunayAbstract:A cellular neural network (CNN) based Edge detector optimized by differential evolution (DE) algorithm is presented. Cloning template of the proposed CNN is adaptively tuned by using simple training images. The performance of the proposed Edge detector is evaluated on different test images and compared with popular Edge Detectors from the literature. Simulation results indicate that the proposed CNN operator outperforms competing Edge Detectors and offers superior performance in Edge detection in digital images.
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efficient Edge detection in digital images using a cellular neural network optimized by differential evolution algorithm
Expert Systems With Applications, 2009Co-Authors: Alper Basturk, Enis GunayAbstract:A cellular neural network (CNN) based Edge detector optimized by differential evolution (DE) algorithm is presented. Cloning template of the proposed CNN is adaptively tuned by using simple training images. The performance of the proposed Edge detector is evaluated on different test images and compared with popular Edge Detectors from the literature. Simulation results indicate that the proposed CNN operator outperforms competing Edge Detectors and offers superior performance in Edge detection in digital images.
