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• W2099950433 abstract "Automatic circle detection in digital images is considered as an important and complex task for the computer vision community which has consequently devoted a tremendous amount of research to find an optimal circle detector. On the other hand, Evolutionary Algorithms (EA) are earning popularity as computational intelligence approaches for solving complex problems that are encountered in many engineering disciplines. They have exhibited robustness and suitability to locate global optimum as compared to deterministic (gradient based) optimization methods. Over the last decade, new algorithms based on EA have been applied to image processing. However, one critical difficulty in deploying EA to real-world problems is their high computational time associated to a large number of function evaluations which are required to deliver a satisfactory result. This chapter presents an algorithm for the automatic detection of circular shapes from complicated and noisy images with no consideration of the conventional Hough transform principles. The algorithm is based on a newly developed evolutionary algorithm called the Adaptive Population with Reduced Evaluations (APRE). The algorithm reduces the number of function evaluations through the use of two mechanisms: (1) adapting dynamically the size of the population and (2) incorporating a fitness calculation strategy which decides whether the calculation or estimation of the new generated individuals is feasible. As a result, the approach can substantially reduce the number of function evaluations, yet preserving the good search capabilities of an evolutionary approach. The algorithm uses the encoding of three pixels as candidate circles over the edge image. An objective function evaluates 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 operators defined by APRE so that they can fit into the actual circles on the edge map of the image. Experimental results over several synthetic and natural images, with a varying range of complexity, validate the efficiency of the resultant technique with regard to accuracy, speed, and robustness." @default.
• W2099950433 created "2016-06-24" @default.
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• W2099950433 date "2015-11-07" @default.
• W2099950433 modified "2023-09-27" @default.
• W2099950433 title "Circle Detection on Images Based on an Evolutionary Algorithm that Reduces the Number of Function Evaluations" @default.
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• W2099950433 doi "https://doi.org/10.1007/978-3-319-26462-2_7" @default.
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