Multi-ellipses detection on images inspired by collective animal behavior

TL;DR

This paper introduces a novel evolutionary algorithm inspired by collective animal behavior for detecting multiple ellipses in images, demonstrating improved accuracy, speed, and robustness over complex synthetic and natural images.

Contribution

It presents a new multi-ellipses detection method using an animal-inspired evolutionary algorithm with a memory-based competition mechanism.

Findings

Effective detection of multiple ellipses in complex images.

High accuracy and robustness demonstrated in experiments.

Fast processing suitable for real-time applications.

Abstract

This paper presents a novel and effective technique for extracting multiple ellipses from an image. The approach employs an evolutionary algorithm to mimic the way animals behave collectively assuming the overall detection process as a multi-modal optimization problem. In the algorithm, searcher agents emulate a group of animals that interact to each other using simple biological rules which are modeled as evolutionary operators. In turn, such operators are applied to each agent considering that the complete group has a memory to store optimal solutions (ellipses) seen so-far by applying a competition principle. The detector uses a combination of five edge points as parameters to determine ellipse candidates (possible solutions) while a matching function determines if such ellipse candidates are actually present in the image. Guided by the values of such matching functions, the set of encoded candidate ellipses are evolved through the evolutionary algorithm so that the best candidates can be fitted into the actual ellipses within the image. Just after the optimization process ends, an analysis over the embedded memory is executed in order to find the best obtained solution (the best ellipse) and significant local minima (remaining ellipses). Experimental results over several complex synthetic and natural images have validated the efficiency of the proposed technique regarding accuracy, speed and robustness.