A new curve detection method: randomized Hough transform (RHT)
Pattern Recognition Letters
Comparative study of Hough transform methods for circle finding
Image and Vision Computing - Special issue: 5th Alvey vision meeting
Parallel simulated annealing for shape detection
Computer Vision and Image Understanding
Deriving stopping rules for the probabilistic Hough transform by sequential analysis
Computer Vision and Image Understanding
A linear algorithm for incremental digital display of circular arcs
Communications of the ACM
An efficient randomized algorithm for detecting circles
Computer Vision and Image Understanding
Artificial Immune Systems: A New Computational Intelligence Paradigm
Artificial Immune Systems: A New Computational Intelligence Paradigm
Geometric Primitive Extraction Using a Genetic Algorithm
IEEE Transactions on Pattern Analysis and Machine Intelligence
Solving Multiobjective Optimization Problems Using an Artificial Immune System
Genetic Programming and Evolvable Machines
Circle detection on images using genetic algorithms
Pattern Recognition Letters
An Efficient Ellipse-Drawing Algorithm
IEEE Computer Graphics and Applications
An Improved Immune Evolutionary Algorithm for Multimodal Function Optimization
ICNC '07 Proceedings of the Third International Conference on Natural Computation - Volume 03
Foundations of Genetic Programming
Foundations of Genetic Programming
Clonal selection algorithms: a comparative case study using effective mutation potentials
ICARIS'05 Proceedings of the 4th international conference on Artificial Immune Systems
Advances in artificial immune systems
IEEE Computational Intelligence Magazine
Learning and optimization using the clonal selection principle
IEEE Transactions on Evolutionary Computation
EDCircles: A real-time circle detector with a false detection control
Pattern Recognition
Hi-index | 12.05 |
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.