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چکیده
Edge detection has attracted the attention of many researchers and is one of the most important areas in low level computer vision. In recent years, the focus of edge detection has shifted from grayscale single component images to multicomponent color images that utilize the inter-spectral correlation of the neighboring color samples to eliminate color artifacts and increase the accuracy of edge detection process. This project presents the study of color edge detection based on vector order statistics operators. Variations are introduced in the vector order statistics color edge operator to improve noise performance and we demonstrate their ability to attenuate noise with added algorithm complexity. We present a performance evaluation framework to assess the quality of color edge detectors and compare it with the Canny edge detector which is the optimal edge detector used for grayscale images. The edge detectors are evaluated with subjective and objective tests using statistical indices by comparing it with human ground truth images extracted from the BSDS300 dataset.
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