Circle detection on images using genetic algorithms

Ayala-Ramírez, Víctor; García-Capulín, Carlos H.; Perez-Garcia, A.; Sánchez-Yáñez, Raúl E.

doi:10.1016/j.patrec.2005.10.003

Cited by 167 publications

(145 citation statements)

References 14 publications

Supporting

Mentioning

130

Contrasting

Unclassified

Order By: Relevance

“…Like the method proposed by Schuster et al [34], they formulated the problem of finding circles as the estimation based on weighted minimum mean square error. Ayala-Ramirez et al [35] gave another choice. They placed the task in the genetic algorithm.…”

Section: Some Other Methodsmentioning

confidence: 99%

Circle Detection Based on Voting for Maximum Compatibility

Liu

Huang

2012

IEICE Trans. Inf. & Syst.

View full text Add to dashboard Cite

SUMMARYWe present a fast voting scheme for localizing circular objects among clutter and occlusion. Typical solutions for the problem are based on Hough transform that evaluates an instance of circle by counting the number of edge points along its boundary. The evaluated value is proportional to radius, making the normalization with respect to the factor necessary for detecting circles with different radii. By representing circle with a number of sampled points, we get rid of the step. To evaluate an instance then involves obtaining the same number of edge points, each close to a sampled point in both spatial position and orientation. The closeness is measured by compatibility function, where a truncating operation is used to suppress noise and deal with occlusion. To evaluate all instances of circle is fulfilled by letting edge point vote in a maximizing way such that any instance possesses a set of maximally compatible edge points. The voting process is further separated into the radius-independent anddependent parts. The time-consuming independent part can be shared by different radii and outputs the sparse matrices. The radius-dependent part shifts these sparse matrices according to the radius. We present precisionrecall curves showing that the proposed approach outperforms the solutions based on Hough transform, at the same time, achieves the comparable time complexity as algorithm of Hough transform using 2D accumulator array.

show abstract

Section: Some Other Methodsmentioning

confidence: 99%

Circle Detection Based on Voting for Maximum Compatibility

Liu

Huang

2012

IEICE Trans. Inf. & Syst.

View full text Add to dashboard Cite

show abstract

“…In order to enhance the algorithm analysis, the LA algorithm is compared to the GA [12] and the IRHT [7] circle detectors over a set of different images.…”

Section: Performance Comparisonmentioning

confidence: 99%

“…In this comparison, the GA-detector follows the design from Ayala-Ramirez et al in [12], considering a population size of 70 individuals. The crossover probability, which fixes the threshold for dividing the actual parents' contribution prior to their re-combination into a new individual, is set to 0.55.…”

Section: Parametric Setupmentioning

confidence: 99%

“…The number of elite individuals is set to two, implying that only the best two individuals remain unaltered for the next generation. Moreover, as it is reported in [12], the GAdetector generates the test point set The comparative study also includes the IRHT algorithm [7]. …”

Section: Parametric Setupmentioning

confidence: 99%

“…Experimental evidence shows its effectiveness for detecting circles under different conditions. A comparison to other state-of-the-art methods such as the GA algorithm [12] and the Iterative Randomized Hough Transform approach (IRHT) [7] on multiple images has demonstrated the improved performance of the proposed method.…”

mentioning

confidence: 94%

See 2 more Smart Citations

Circle detection on images using learning automata

et al. 2012

View full text Add to dashboard Cite

Circle detection over digital images has received considerable attention from the computer vision community over the last few years devoting a tremendous amount of research seeking for an optimal detector. This article presents an algorithm for the automatic detection of circular shapes from complicated and noisy images with no consideration of conventional Hough transform principles. The proposed algorithm is based on Learning Automata (LA) which is a probabilistic optimization method that explores an unknown random environment by progressively improving the performance via a reinforcement signal (objective function). The approach uses the encoding of three non-collinear points as a candidate circle over the edge image. A reinforcement signal (matching function) indicates if such candidate circles are actually present in the edge map. Guided by the values of such reinforcement signal, the probability set of the encoded candidate circles is modified through the LA algorithm so that they can fit to the actual circles on the edge map. Experimental results over several complex synthetic and natural images have validated the efficiency of the proposed technique regarding accuracy, speed and robustness.

show abstract