Automatic multiple circle detection based on artificial immune systems

详细信息    查看全文

• 作者：Erik Cuevas ^{erik.cuevas@cucei.udg.mx} ; Valentí ; n Osuna-Enciso ^{valentin.osuna@cucei.udg.mx} ; Fernando Wario ^{fernando.wario@cucei.udg.mx} ; Daniel Zaldí ; var ; ^{daniel.zaldivar@cucei.udg.mx} ; Marco Pé ; rez-Cisneros ^{marco.perez@cucei.udg.mx}
• 关键词：Artificial immune systems ; Computer vision ; Circle detection ; Clonal selection algorithms
• 刊名：Expert Systems with Applications
• 出版年：2012
• 出版时间：January, 2012
• 年：2012
• 卷：39
• 期：1
• 页码：713-722
• 全文大小：1544 K

文摘

Hough transform (HT) has been the most common method for circle detection, exhibiting robustness but adversely demanding a considerable computational load and large storage. Alternative approaches for multiple circle detection include heuristic methods built over iterative optimization procedures which confine the search to only one circle per optimization cycle yielding longer execution times. On the other hand, artificial immune systems (AIS) mimic the behavior of the natural immune system for solving complex optimization problems. The clonal selection algorithm (CSA) is arguably the most widely employed AIS approach. It is an effective search method which optimizes its response according to the relationship between patterns to be identified, i.e. antigens (Ags) and their feasible solutions also known as antibodies (Abs). Although CSA converges to one global optimum, its incorporated CSA-Memory holds valuable information regarding other local minima which have emerged during the optimization process. Accordingly, the detection is considered as a multi-modal optimization problem which supports the detection of multiple circular shapes through only one optimization procedure. The algorithm uses a combination of three non-collinear edge points as parameters to determine circles candidates. A matching function determines if such circle candidates are actually present in the image. Guided by the values of such function, the set of encoded candidate circles are evolved through the CSA so the best candidate (global optimum) can fit into an actual circle within the edge map of the image. Once the optimization process has finished, the CSA-Memory is revisited in order to find other local optima representing potential circle candidates. The overall approach is a fast multiple-circle detector despite considering complicated conditions in the image.