2 research outputs found
Circle detection using Discrete Differential Evolution Optimization
This paper introduces a circle detection method based on Differential
Evolution (DE) optimization. Just as circle detection has been lately
considered as a fundamental component for many computer vision algorithms, DE
has evolved as a successful heuristic method for solving complex optimization
problems, still keeping a simple structure and an easy implementation. It has
also shown advantageous convergence properties and remarkable robustness. The
detection process is considered similar to a combinational optimization
problem. The algorithm uses the combination of three edge points as parameters
to determine circles candidates in the scene yielding a reduction of the search
space. The objective function determines if some circle candidates are actually
present in the image. This paper focuses particularly on one DE-based algorithm
known as the Discrete Differential Evolution (DDE), which eventually has shown
better results than the original DE in particular for solving combinatorial
problems. In the DDE, suitable conversion routines are incorporated into the
DE, aiming to operate from integer values to real values and then getting
integer values back, following the crossover operation. The final algorithm is
a fast circle detector that locates circles with sub-pixel accuracy even
considering complicated conditions and noisy images. Experimental results on
several synthetic and natural images with varying range of complexity validate
the efficiency of the proposed technique considering accuracy, speed, and
robustness.Comment: 20 Pages. arXiv admin note: text overlap with arXiv:1405.724
Automatic Circle Detection on Images with Annealed Differential Evolution
This article presents an algorithm for the automatic detection of circular shapes from complicated and noisy images. The algorithm is based on a hybrid technique composed of simulated annealing and differential evolution. A new fuzzy objective function has been derived for the edge map of a given image. Minimization of this function with a hybrid annealed differential evolution algorithm leads to the automatic detection of circles on the image. 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