Analytic Curve Detection from a Noisy Binary Edge Map Using Genetic Algorithm

Currently Hough transform and its variants are the most common methods for detecting analytic curves from a binary edge image. However, these methods do not scale well when applied to complex noisy images where correct data is very small compared to the amount of incorrect data. We propose a Genetic Algorithm in combination with the Randomized Hough Transform, along with a different scoring function, to deal with such environments. This approach is also an improvement over random search and in contrast to standard Hough transform algorithms, is not limited to simple curves like straight line or circle.

[1]  Ping Liang,et al.  A new and efficient transform for curve detection , 1991, J. Field Robotics.

[2]  Michael de la Maza,et al.  Book review: Genetic Algorithms + Data Structures = Evolution Programs by Zbigniew Michalewicz (Springer-Verlag, 1992) , 1993 .

[3]  Amy R. Reibman,et al.  Hough transform and signal detection theory performance for images with additive noise , 1990, Comput. Vis. Graph. Image Process..

[4]  Ruud M. Bolle,et al.  Generalized neighborhoods: a new approach to complex parameter feature extraction , 1989, Proceedings CVPR '89: IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[5]  Henri Maître.  Contribution to the Prediction of Performances of the Hough Transform , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[6]  Christopher J. Taylor,et al.  Model-based image interpretation using genetic algorithms , 1992, Image Vis. Comput..

[7]  Heikki Kälviäinen,et al.  Connective randomized Hough transform (CRHT) , 1996 .

[8]  Erkki Oja,et al.  Randomized hough transform (rht) : Basic mech-anisms, algorithms, and computational complexities , 1993 .

[9]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[10]  Heikki Kälviäinen,et al.  An extension to the randomized hough transform exploiting connectivity , 1997, Pattern Recognit. Lett..

[11]  V. F. Leavers,et al.  Which Hough transform , 1993 .

[12]  Thomas Risse,et al.  Hough transform for line recognition: Complexity of evidence accumulation and cluster detection , 1989, Comput. Vis. Graph. Image Process..

[13]  S. Shapiro.  Transformations for the Computer Detection of Curves in Noisy Pictures , 1975 .

[14]  Godfried T. Toussaint,et al.  On the detection of structures in noisy pictures , 1977, Pattern Recognit..

[15]  James R. Bergen,et al.  A Probabilistic Algorithm for Computing Hough Transforms , 1991, J. Algorithms.

[16]  Erkki Oja,et al.  A new curve detection method: Randomized Hough transform (RHT) , 1990, Pattern Recognit. Lett..

[17]  Erkki Oja,et al.  Probabilistic and non-probabilistic Hough transforms: overview and comparisons , 1995, Image Vis. Comput..

[18]  W. Eric L. Grimson,et al.  On the Sensitivity of the Hough Transform for Object Recognition , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[19]  Martin D. Levine,et al.  Geometric Primitive Extraction Using a Genetic Algorithm , 1994, IEEE Trans. Pattern Anal. Mach. Intell..

[20]  Shiu Yin Yuen,et al.  Connective hough transform , 1993, Image Vis. Comput..

[21]  Violet F. Leavers,et al.  The dynamic generalized Hough transform: Its relationship to the probabilistic Hough transforms and an application to the concurrent detection of circles and ellipses , 1992, CVGIP Image Underst..