Circle detection by Harmony Search Optimization

TL;DR

This paper introduces a novel circle detection method in images using the Harmony Search Algorithm, framing the task as an optimization problem to improve accuracy and robustness in identifying circular shapes.

Contribution

It applies the Harmony Search Algorithm to circle detection, offering a derivative-free optimization approach that enhances detection performance over traditional methods.

Findings

High accuracy in synthetic and natural images

Fast detection with robustness to noise

Effective in complex image scenarios

Abstract

Automatic circle detection in digital images has received considerable attention over the last years in computer vision as several efforts have aimed for an optimal circle detector. This paper presents an algorithm for automatic detection of circular shapes that considers the overall process as an optimization problem. The approach is based on the Harmony Search Algorithm (HSA), a derivative free meta-heuristic optimization algorithm inspired by musicians while improvising new harmonies. The algorithm uses the encoding of three points as candidate circles (harmonies) over the edge-only image. An objective function evaluates (harmony quality) if such candidate circles are actually present in the edge image. Guided by the values of this objective function, the set of encoded candidate circles are evolved using the HSA so that they can fit to the actual circles on the edge map of the image (optimal harmony). Experimental results from several tests on synthetic and natural images with a varying complexity range have been included to validate the efficiency of the proposed technique regarding accuracy, speed and robustness.