Abstract: Aiming at the shortcomings of image segmentation algorithm, such as poor noise robustness, small boundary information easily loss and narrow application scopes, we improve the threshold value of Canny edge extraction algorithm and extract weighted fusion with chromatic feature. According to the adaptive problem of the Canny operator threshold, the threshold is determined by calculating the class variance between the image background and the target to reduce the error probability. Then, we propose three color features of R, G and B on colorful images with rich information. The new segmentation images are fused by thresholding and segmented images extracted by chromaticity feature extraction. The algorithm not only overcomes the problem of edge information loss and poor robustness of the traditional segmentation and extraction algorithm, but also improves the unit accuracy of the detail. The experimental results show the effectiveness of the improved Canny edge algorithm.

Key words: adaptability, chroma features, image fusion