Circle detection on images using genetic algorithms

Circle detection using electro-magnetism optimization

This paper describes a circle detection method based on Electromagnetism-Like Optimization (EMO). Circle detection has received considerable attention over the last years thanks to its relevance for many computer vision tasks. EMO is a heuristic method for solving complex optimization problems inspired in electromagnetism principles. This algorithm searches a solution based in the attraction and repulsion among prototype candidates. In this paper the detection process is considered to be similar to an optimization problem, the algorithm uses the combination of three edge points (x, y, r) as parameters to determine circles candidates in the scene. An objective function determines if such circle candidates are actually present in the image. The EMO algorithm is used to find the circle candidate that is better related with the real circle present in the image according to the objective function. The final algorithm is a fast circle detector that locates circles with sub-pixel accuracy even considering complicated conditions and noisy images.

This article presents an algorithm for the automatic detection of circular shapes from complicated and noisy images without using the conventional Hough transform methods. The proposed algorithm is based on a recently developed swarm intelligence technique, known as the bacterial foraging optimization (BFO). A new objective function has been derived to measure the resemblance of a candidate circle with an actual circle on the edge map of a given image based on the difference of their center locations and radii lengths. Guided by the values of this objective function (smaller means better), a set of encoded candidate circles are evolved using the BFO algorithm so that they can fit to the actual circles on the edge map of the image. The proposed method is able to detect single or multiple circles from a digital image through one shot of optimization. Simulation results over several synthetic as well as natural images with varying range of complexity validate the efficacy of the proposed technique in terms of its final accuracy, speed, and robustness.

Automatic circle detection on digital images with an adaptive bacterial foraging algorithm

The micro-Doppler (m-D) effect is caused by fast moving reflectors. This effect may significantly decrease the readability of the inverse synthetic aperture radar/synthetic aperture radar (ISAR/SAR) images. An L-statistics-based method for m-D effects removal is proposed. The L-statistics approach is performed on the spectrogram, while the rigid body signal synthesis is done in the complex time-frequency (TF) domain. The proposed method is very simple to use and produces better results than the other TF-based approaches. In addition to being capable of separating the rigid body and the m-D parts, this approach is robust to the noise influence. It may also separate close rigid body points, which are not separated in the original radar image. In the numerical implementation of this approach for radar imaging, the computational efficiency is further improved by using two thresholds. The first threshold determines whether there is a target signal in a range cell, while the second threshold determines whether there are m-D effects in this range cell. These thresholds could significantly decrease the computation time in real-time applications. The theory is illustrated by examples.

Micro-Doppler Removal in the Radar Imaging Analysis

We investigate the use of genetic algorithms (GAs) for image primitives extraction (such as segments, circles, ellipses or quadrilaterals). This approach completes the well-known Hough transform, in the sense that GAs are efficient when the Hough approach becomes too expensive in memory, i.e. when we search for complex primitives having more than 3 or 4 parameters. A GA is a stochastic technique, relatively slow, but which provides with an efficient tool to search in a high dimensional space. The philosophy of the method is very similar to the Hough transform, which is to search an optimum in a parameter space. However, we will see that the implementation is different.

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P. Martinez

Papers

A genetic algorithm for the detection of 2D geometric primitives in images