An efficient circle detection scheme in digital images using ant system algorithm

TL;DR

This paper introduces a novel, efficient circle detection method in digital images using an ant system algorithm, which outperforms traditional techniques in speed and can detect intersecting and non-intersecting circles suitable for real-time applications.

Contribution

The paper presents a new ant system-based scheme for circle detection that is more efficient and capable of handling complex circle configurations compared to existing methods.

Findings

Detects both intersecting and non-intersecting circles

Operates with high time efficiency suitable for real-time applications

Uses a novel ant system to find closed loops and test for circles

Abstract

Detection of geometric features in digital images is an important exercise in image analysis and computer vision. The Hough Transform techniques for detection of circles require a huge memory space for data processing hence requiring a lot of time in computing the locations of the data space, writing to and searching through the memory space. In this paper we propose a novel and efficient scheme for detecting circles in edge-detected grayscale digital images. We use Ant-system algorithm for this purpose which has not yet found much application in this field. The main feature of this scheme is that it can detect both intersecting as well as non-intersecting circles with a time efficiency that makes it useful in real time applications. We build up an ant system of new type which finds out closed loops in the image and then tests them for circles.