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A comparison between the standard Hough Transform and the Mahalanobis distance Hough Transform

  • Chengping Xu
  • Sergio A. Velastin
Image Features
Part of the Lecture Notes in Computer Science book series (LNCS, volume 800)

Abstract

The Hough Transform is a class of medium-level vision techniques generally recognised as a robust way to detect geometric features from a 2D image. This paper presents two related techniques. First, a new Hough function is proposed based on a Mahalanobis distance measure that incorporates a formal stochastic model for measurement and model noise. Thus, the effects of image and parameter space quantisation can be incorporated directly. Given a resolution of the parameter space, the method provides better results than the Standard Hough Transform (SHT), including under high geometric feature densities. Secondly, Extended Kalman Filtering is used as a further refinement process which achieves not only higher accuracy but also better performance than the SHT. The algorithms are compared with the SHT theoretically and experimentally.

Keywords

Mahalanobis Distance Extend Kalman Filter Hough Transform Pixel Error Detect Feature Point 
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 1994

Authors and Affiliations

  • Chengping Xu
    • 1
  • Sergio A. Velastin
    • 1
  1. 1.Department of Electronic and Electrical Engineering, King's College LondonUniversity of LondonLondonEngland

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