Edge Detection based on Ant Colony Optimization Using Adaptive Thresholding Technique

Abstract

Abstract-Image edge detection is a process where true edges of an image are identified. In past, gradient based methods in which first or second order pixel difference is used to find discontinuities and if magnitude value of gradient is higher than certain threshold then that pixel under observation is identified as edge pixel. These methods are full of error, because in addition to true edges they also find false edges and infect false edges are more in comparison to true edges. To solve such problem, swarm intelligence based ant colony optimization based edge detection method is detailed where numbers of falsely detected edges are very small. The performance of the ant colony optimization (ACO) is done in terms of Peak Signal to Noise Ratio, Performance Ratio and Efficiency.

Ant Colony Optimization Technique for Edge Detection of an Image

“…Anna Veronica Baterina and Carlos Oppus suggested edge detection employing ant colony optimization wherein they constructed a pheromone matrix that reflects the edge data at every pixel relying on the routes shaped by the ants dispatched on the image. to combinatorial optimization and routing in communications networks [7].…”

Edge Detection of Image Using Image Divergence and Downsampling Method

“…Acharya et al describe a curve fitting-based picture segmentation approach employing Modified Vandermonde [3], which yields a higher quality segmented output in a short amount of time. Gautam et al offer an edge detection algorithm based on ant colony optimization and adaptive thresholding to identify the target object's edge lines [13]. Dagar et al conducted a comparative study on edge detection algorithms founded on Computational Intelligence.…”

“…Entropy sum HC log HC  (6) HC contains the normalized histogram counts returned from image histogram. Entropy determines the detail information content of the image.EBCM[13,14] …”