Recognizing Articulated Objects with Information Theoretic Methods

  • Authors:
  • Davi Geiger;Tyng-Luh Liu

  • Affiliations:
  • -;-

  • Venue:
  • FG '96 Proceedings of the 2nd International Conference on Automatic Face and Gesture Recognition (FG '96)
  • Year:
  • 1996

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Abstract

This paper addresses the problem of recognizing articulated and deformable objects. In particular we are interested in human arm and leg articulations. Our approach is a Bayesian-Information integration of shape similarity and snakes, and naturally combines top-down & bottom-up algorithms. The bottom-up method extracts edges, then constructs snakes (or contours) by grouping edge elements and feeds the shape analysis. The top-down one uses shape analysis, by comparing the object model with the extracted snakes, to guide/prune the search for other snakes. The optimizations are based on Dijkstra algorithm and further pruning of this algorithm is obtained by ``integration by parts''. Our approach is general enough to handle three dimensional objects, but our focus here is on two dimensional contours.