Color Edge Detection Using the Normalization Anisotropic Gaussian Kernel and Multichannel Fusion

Color edge detection is a key technique in image processing for vision engineering. In this paper, a new edge detector based on normalized Anisotropic Gaussian Directional Derivative and Multi-channel Gradient Matrix Fusion is proposed. Firstly, the color image is decomposed into six components in the RGB model and the HSV model, respectively. The gradient amplitude of the image edge is emphasized by Contrast Limited Adaptive Histogram Equalization (CLAHE). A normalized Anisotropic Gaussian Derivative is constructed by Multi-direction ANGK to extract the edge strength map of original color image. Finally, Singular Value Decomposition (SVD) was adopted to fuse each channel component in combination with a Multi-channel Morphological Gradient Derivative Matrix to improve the accuracy of edge detection. The proposed detector is compared with three state-of-art edge detectors with the Berkeley dataset (BSDS500) as the database. The results show that the proposed algorithm is more prominent in the performance of noise robustness and edge detection resolution.