Fast and Robust Ellipse Detection J Yao, N Kharma et al. Computational Intelligence Lab Electrical & Computer Eng. Dept. Concordia University Montréal, Québec, Canada July 2006 A Novel Multi-Population Genetic Algorithm

GECCO 2006HCA 2 Criteria (A) The result is an improvement over a patented invention (B) The result is equal to or better than a result that was accepted as a new scientific result at the time when it was published in a peer-reviewed scientific journal. ≥ Multi-population GA Classical Hough Transform Randomized Hough Transform ≥ 1. Hough Transform Family 2. Multi-Population Genetic Algorithm 3. Comparison 4. Summary

GECCO 2006HCA 3 Agenda 1. Hough Transform Family

GECCO 2006HCA 4 Hough Transform Family Hough Transform Generalized Hough Transform 2 Generalized Hough Transform 2 Randomized Hough Transform 3 Randomized Hough Transform 3 U.S. Patent 3,069, Hough and P.V.C., Duda and Hart, Xu et. al., 1990

GECCO 2006HCA 5 Randomized Hough Transform = RHT Improvements over standard Hough Transform (McLaughlin, 1998) Accuracy MemorySpeed False positive

GECCO 2006HCA 6 RHT?! Coarse Approximation Inaccuracy False Positive

GECCO 2006HCA 7 Agenda 1. Hough Transform Family 2. Multiple Population Genetic Algorithm

GECCO 2006HCA 8 Multi-Population GA = MPGA Multiple population Clustering Bi-objective Specialized Mutation MPGA Essence of Multi-modality Enhancement Diversification Exploitation

GECCO 2006HCA 9 MPGA vs. RHT RHTMPGA Independent Blind Sampling Progressivelyenhanced HeuristicDirected Search Accumulative Blind Search Suitable Little noiseFew targets High noiseMultiple targets

GECCO 2006HCA 10 Agenda 1. Hough Transform Family 2. Multiple Population Genetic Algorithm 3. Comparison* * Yao, et. al., 2005

GECCO 2006HCA 11 Detection of Multiple Ellipses MPGARHT

GECCO 2006HCA 12 The Effect of Noise I MPGA RHT

GECCO 2006HCA 13 The Effect of Noise II

GECCO 2006HCA 14 Results on Real World Images MPGARHT Returns False Positives MPGA RHT Misses Smaller Ellipses MPGA RHT Provides Coarse Approximation Handwritt en Character s Road Signs Road Signs Microscop ic Images

GECCO 2006HCA 15 Real World Images - Statistics MPGARHT Accuracy (%) Average CPU Time (sec) False Positive (%)

GECCO 2006HCA 16 Agenda 1. Hough Transform Family 2. Multi-Population Genetic Algorithm 3. Comparison 4. Summary

GECCO 2006HCA 17 Summary Accuracy Robustness Efficiency -- MPGA Better than classical… -- RHT Oldest… -- classical HT

GECCO 2006HCA 18 References  Hough and P.V.C., Methods and Means for Recognizing Complex Patterns, U.S. Patent 3,069,654,  Duda, R. O. and P. E. Hart, "Use of the Hough Transformation to Detect Lines and Curves in Pictures," Comm. ACM, Vol. 15, pp ,  McLaughlin, R. A., “Randomized Hough Transform: Improved ellipse detection with comparison”, Pattern Recognition Letters 19 (3-4), ,  L. Xu, E. Oja, and P. Kultanen. Anew curve detection method: Randomized Hough Transform (RHT). Pattern Recognition Letters, 11: ,  Yao, J., Kharma, N., and Grogono, P, "A multi-population genetic algorithm for robust and fast ellipse detection", Pattern Analysis & Applications, Volume 8, Issue 1 - 2, Sep 2005, pp