• Ritisha Raval

Edges characterize boundaries and are therefore a problem of fundamental importance in image processing. Image Edge detection reduces the amount of data and filters out useless information from image, while preserving the important structural properties in an image. Since edge detection is using in image processing for object detection. Ant Colony Optimization (ACO) is used to detect edges in digital images. The techniques generate a pheromone matrix , it represents the edge information at each pixel position on the routes formed by ants dispatched on the image. The edge map of image is obtained using thresholding process . The end points obtained from threshoding process are calculated and the ants are placed at these end points.All the movement of the ants is decided by the local variation of the pixel intensity values. By using an thresholding technique the edges from the final pheromone matrix are extracted. The results are analyze using Shanon’s Entropy function and SNR Values .

Edge Detection Methods, Edge Detection using Ant Colony Optimization, Ant Colony Optimization, Edge Detector, Pheromone matrix.

"Advanced Edge Detection using Ant Colony Optimization", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.2, Issue 9, page no.71-74, October-2015, Available :http://www.jetir.org/papers/JETIR1509013.pdf

"Advanced Edge Detection using Ant Colony Optimization", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.2, Issue 9, page no. pp71-74, October-2015, Available at : http://www.jetir.org/papers/JETIR1509013.pdf