Color Image Edge Detection using Multi-scale and Multi-directional Gabor filter
This is an incremental improvement for computer vision applications requiring precise edge detection in noisy color images.
The paper tackles color image edge detection by using multi-scale and multi-directional Gabor filters to improve accuracy and noise robustness, achieving better performance in detection accuracy and noise-robustness compared to existing methods.
In this paper, a color edge detection method is proposed where the multi-scale Gabor filter are used to obtain edges from input color images. The main advantage of the proposed method is that high edge detection accuracy is attained while maintaining good noise robustness. The proposed method consists of three aspects: First, the RGB color image is converted to CIE L*a*b* space because of its wide coloring area and uniform color distribution. Second, a set of Gabor filters are used to smooth the input images and the color edge strength maps are extracted, which are fused into a new ESM with the noise robustness and accurate edge extraction. Third, Embedding the fused ESM in the route of the Canny detector yields a noise-robust color edge detector. The results show that the proposed detector has the better experience in detection accuracy and noise-robustness.