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Tracking line segments

  • Rachid Deriche
  • Olivier Faugeras
Tracking
Part of the Lecture Notes in Computer Science book series (LNCS, volume 427)

Abstract

This paper describes the development and the implementation of a line segments based token tracker. Given a sequence of time-varying images, the goal is to track line segments corresponding to the edges extracted from the image being analyzed. We will present a tracking approach that combines a prediction and a matching steps. The prediction step is a Kalman filtering based approach that is used in order to provide reasonable estimates of the region where the matching process has to seek for a possible match between tokens. Correspondence in the search area is done through the use of a similarity function based on Mahalanobis distance between attributs carefully chosen of the line segments. The efficiency of the proposed approach will be illustrated in several experiments that have been carried out considering noisy synthetic data and real scenes obtained from the INRIA mobile robot.

Keywords

Line Segment Kalman Filter Tracking Algorithm Mahalanobis Distance Search Area 
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.

References

  1. [1]
    R. Deriche. Using canny's criteria to derive a recursively implemented optimal edge detector. International Journal of Computer Vision, 1(2):167–187, May 1987.Google Scholar
  2. [2]
    O.D. Faugeras. R. Deriche. N.Navab From Optical Flow of Lines to 3D Motion and structure. Proceedings IEEE Int. Work. on Intell. Syst. pp 646–649, Sept 1989. Tsukuba, Japan.Google Scholar
  3. [3]
    A.Gelb and al Applied Optimal Estimation The Analytic Sciences Corporation ed. Arthur Gelb. M.I.T PressGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1990

Authors and Affiliations

  • Rachid Deriche
    • 1
  • Olivier Faugeras
    • 1
  1. 1.INRIA Sophia-AntipolisValbonne CedexFrance

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