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Road Approximation in Euclidean and v-Disparity Space: A Comparative Study

  • Angel D. Sappa
  • Rosa Herrero
  • Fadi Dornaika
  • David Gerónimo
  • Antonio López
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4739)

Abstract

This paper presents a comparative study between two road approximation techniques—planar surfaces—from stereo vision data. The first approach is carried out in the v-disparity space and is based on a voting scheme, the Hough transform. The second one consists in computing the best fitting plane for the whole 3D road data points, directly in the Euclidean space, by using least squares fitting. The comparative study is initially performed over a set of different synthetic surfaces (e.g., plane, quadratic surface, cubic surface) digitized by a virtual stereo head; then real data obtained with a commercial stereo head are used. The comparative study is intended to be used as a criterion for fining the best technique according to the road geometry. Additionally, it highlights common problems driven from a wrong assumption about the scene’s prior knowledge.

Keywords

Euclidean Space IEEE Computer Society Stereo Vision Hough Transform Obstacle Detection 
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 2007

Authors and Affiliations

  • Angel D. Sappa
    • 1
  • Rosa Herrero
    • 1
  • Fadi Dornaika
    • 2
  • David Gerónimo
    • 1
  • Antonio López
    • 1
  1. 1.Computer Vision Center, Edifici O Campus UAB, 08193 Bellaterra, BarcelonaSpain
  2. 2.Institut Géographique National, 94165 Saint MandéFrance

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