The Experts below are selected from a list of 19116 Experts worldwide ranked by ideXlab platform
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.
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Edge Detector of sar images using gaussian gamma shaped bi windows
IEEE Geoscience and Remote Sensing Letters, 2012Co-Authors: Penglang Shui, Dong ChengAbstract:By introducing Gaussian-Gamma-shaped (GGS) bi-windows instead of traditional rectangle bi-windows, a new ratio-based Edge Detector is proposed to extract thin Edges of synthetic aperture radar (SAR) images. As poor 2-D smoothing filters, the rectangle window functions are shown to be apt to incur false-Edge pixels near true Edges. Using the GGS window functions reduces false-Edge pixels near true Edges, which can be verified by analyzing effective false maxima in the Edge strength maps (ESMs). Operating the nonmaximum suppression and hysteresis thresholding on the ratio-based ESM using GGS bi-windows yields thin Edges of SAR images. The receiver-operating-characteristic curve is used to evaluate Edge Detectors. The experimental results to a synthetic SAR image show that the Detector using GGS bi-windows attains better performance than the one using rectangle bi-windows.
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.
Atr Key - One of the best experts on this subject based on the ideXlab platform.
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linear feature extraction for sar image based on fused Edge Detector
Journal of Electronics Information & Technology, 2009Co-Authors: Atr KeyAbstract:A linear feature extraction algorithm for Synthetic Aperture Radar (SAR) image is proposed, which is based on the low signal-to-noise quality of SAR image. Firstly, a new Edge Detector, which fuses the Canny operator and Ratio Of Average (ROA) operator, is used to get the Edge points. Then, radon transform is carried out to get the primitive line segments. Finally, the broken lines due to speckle noise are connected by means of the heuristic link idea. The experiment results which are based on the SAR images show, the proposed algorithm can describe the linear characteristic of SAR images precisely, and it can be used for SAR auto target recognition and scene matching.
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.
Weichuan Zhang - One of the best experts on this subject based on the ideXlab platform.
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noise robust image Edge detection based upon the automatic anisotropic gaussian kernels
Pattern Recognition, 2017Co-Authors: Weichuan Zhang, Yali Zhao, Toby P Breckon, Long ChenAbstract:This paper presents a novel noise robust Edge Detector based upon the automatic anisotropic Gaussian kernels (ANGKs), which also addresses the current problem that the seminal Canny Edge Detector may miss some obvious crossing Edge details. Firstly, automatic ANGKs are designed according to the noise suppression, Edge resolution and localization precision, which also conciliate the conflict between them. Secondly, reasons why cross-Edge points are missing from Canny Detector results using isotropic Gaussian kernel are analyzed. Thirdly, the automatic ANGKs are used to smooth image and a revised Edge extraction method is used to extract Edges. Finally, the aggregate test receiver-operating-characteristic (ROC) curves and Pratt's Figure of Merit (FOM) are used to evaluate the proposed Detector against state-of-the-art Edge Detectors. The experiment results show that the proposed algorithm can obtain better performance for noise-free and noisy images.
