Analytic curve detection from a noisy binary edge map using genetic algorithm

  • Samarjit Chakraborty
  • Kalyanmoy Deb
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1498)


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.


Genetic Algorithm Edge Point Curve Segment Noise Point Noise Region 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 1998

Authors and Affiliations

  • Samarjit Chakraborty
    • 1
  • Kalyanmoy Deb
    • 2
  1. 1.Department of Computer Science and EngineeringIndian Institute of Technology KanpurKanpurIndia
  2. 2.Department of Mechanical EngineeringIndian Institute of Technology KanpurKanpurIndia

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