Ant Colony Optimization Algorithm for Improving Efficiency of Canny Edge Detection Technique for Images

Edge detection is one of the important parts of image processing. It is essentially involved in the pre-processing stage of image analysis and computer vision. It generally detects the contour of an image and thus provides important details about an image. So, it reduces the content to process for the high-level processing tasks like object recognition and image segmentation. The most important step in the edge detection based on Canny edge detection algorithm, on which the success of generation of true edge map depends, lies on the determination of threshold. In this work, purpose of edge detection, inspired from Ant Colonies, is fulfilled by Ant Colony Optimization (ACO). The success of the work done is tested visually with the help of test images and empirically tested on the basis of several statistical parameter of comparison. The process of extracting the important features present in an image, keeping the unnecessary or unimportant information present in the form of noise out as much as possible. There are many methods that have been developed in these field, but the most trustworthy and used among them is canny algorithm with ACO method with thresholding. The proposed novel method presented in this thesis is tested on the images better edge detection. The Canny Edge detected images obtained on the images are showing better results than the other conventional edge detectors.