Image Edge Detection Based on Swarm Optimization Technique

Abstract

Detecting of the edges in the image is used for highlighting sharp values of intensities and also which is used to extracted the relevant data. In the traditional methods of detection the results will be in broken edges and thereby there is loss of contours. The Ant Colony Optimization (AnCO) is originated to have faith in the detection problems wherever the aim is to extract the sting data which is present in the input picture, which is necessary to grab the information. Most procedures regarding Ant Colony Optimization is the inventions of fine explore regeneration over across the secretion upgraded by the army of ants. AnCO is galvanized from the actions of hunting the food displayed by hymenopteran community to seek out estimate results to the robust problems. An Ant Colony Optimization algorithmic rule is that the combination of previous information relating to the structure of an answer with the data relating to the arrangement of antecedent to acquire smart results. This methods uses 5 steps as initialization, construction, updation, decision and conceptualization method. Here the proposed methodology is carried with test images such as lena and cameraman. This method in finding the edges from a binary image by investigation results shows the successful outcomes. This proposed work can be applied in biomedical image processing in finding out the contour of tumor tissues.