Edge detection of images based on improved Sobel operator and genetic algorithms

• Published in: 2009 International Conference on Image Analysis and Signal Processing pp. 31 - 35
• Main Authors: Zhang Jin-Yu, Chen Yan, Huang Xian-Xiang
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.04.2009
• Subjects: Classes Square Error; Computer errors; Computer vision; Data mining; Edge Detection; Genetic algorithms; Image color analysis; Image edge detection; Image generation; Image processing; Image segmentation; Image texture analysis; Sobel Operator
• ISBN: 9781424439874; 1424439876
• ISSN: 2156-0110
• DOI: 10.1109/IASP.2009.5054605

Edge detection of images is a classical problem in computer vision and image processing. The key of edge detection is the choice of threshold; the choice of threshold directly determines the results of edge detection. How to automatically determine an optimal threshold is one of difficult points of edge detection. In this paper, Sobel edge detection operator and its improved algorithm are first discussed in term of optimal thresholding. Then based on genetic algorithms and improved Sobel operator, a new automatic threshold algorithm for images processing is proposed. Finally, the edge detection experiments of two real images are conducted by means of two algorithms. The comparative experiment results show that the new algorithm of automatic threshold is very effective. The results are also better than the classical Otsu methods.