Multi-circle detection on images inspired by collective animal behavior
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Multi-circle detection on images inspired by collective animal behavior Erik Cuevas · Mauricio González
Published online: 24 November 2012 © Springer Science+Business Media New York 2012
Abstract Hough transform (HT) has been the most common method for circle detection that delivers robustness but adversely demands considerable computational efforts and large memory requirements. As an alternative to HTbased techniques, the problem of shape recognition has also been handled through optimization methods. In particular, extracting multiple circle primitives falls into the category of multi-modal optimization as each circle represents an optimum which must be detected within the feasible solution space. However, since all optimization-based circle detectors focus on finding only a single optimal solution, they need to be applied several times in order to extract all the primitives which results on time-consuming algorithms. This paper presents an algorithm for automatic detection of multiple circular shapes that considers the overall process as a multi-modal optimization problem. In the detection, the approach employs an evolutionary algorithm based on the way in which the animals behave collectively. In such an algorithm, searcher agents emulate a group of animals which interact to each other using simple biological rules. These rules are modeled as evolutionary operators. Such operators are applied to each agent considering that the complete group maintains a memory which stores the optimal solutions seen so-far by applying a competition principle. The detector uses a combination of three non-collinear edge points as parameters to determine circle candidates (possible solutions). A matching function determines if such circle E. Cuevas () · M. González Departamento de Ciencias Computacionales, Universidad de Guadalajara, CUCEI, Av. Revolución 1500, Guadalajara, Jal, México e-mail: [email protected] M. González e-mail: [email protected]
candidates are actually present in the image. Guided by the values of such matching functions, the set of encoded candidate circles are evolved through the evolutionary algorithm so that the best candidate (global optimum) can be fitted into an actual circle within the edge-only image. Subsequently, an analysis of the incorporated memory is executed in order to identify potential local optima which represent other circles. Experimental results over several complex synthetic and natural images have validated the efficiency of the proposed technique regarding accuracy, speed and robustness. Keywords Circle detection · Bio-inspired methods · Evolutionary algorithms · Optimization
1 Introduction The problem of detecting circular features holds paramount importance for image analysis in industrial applications such as automatic inspection of manufactured products and components, aided vectorization of drawings, target detection, etc. [1]. In the last decade, the detection accuracy and computation performance are two main concerned issues and many circle detection meth
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