Multi Scale and Multi Directional Edge Detection by using the Primary Vision Model and Contractive Fusion of Image Gradients

Document Type : Research Paper



Edge detection is one of the most important and basic problems in image processing and image analysis and machine vision. The importance of this issue is that the output of many image analysis algorithms are dependent on accurately and correctly edge detection algorithm. However a comprehensive and perfect solution that be able to do edge detection with considering all of the edge patterns and factors affecting it, so far not provided. In this paper, firstly, inspired by computational model of retina and primary visual cortex cells in the brain, we propose an appropriate Gabor function as a gradient operation different directions and scales with adaptive parameters. Then, for fusion of rectified gradient responses, we introduce an algorithm based on edge segments contraction from large-scales toward small-scales. This research is trying to satisfy three criteria introduced by Canny properly. With the implementation of multi-scale and multi-directional analysis and contractive fusion of image gradients, better detection and more precise positioning of edges has been obtained. The experimental results indicate that the effective ness of this method on natural and artificial images is better than the conventional methods (e.g. Canny).