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Measures for silhouettes resemblance and representative silhouettes of curved objects

  • Yoram Gdalyahu
  • Daphna Weinshall
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1065)

Abstract

We claim that the task of object recognition necessitates a measure for image likelihood, that is: a measure for the probability that a given image is obtained from a familiar (pre-investigated) object. Moreover, in a system where objects are represented by 2D images, the best performance is achieved if those images are selected according to a maximum likelihood principle. This is equivalent to maximum stability of the image, or minimal change under a viewpoint perturbation. All of those qualities involve a quantitative comparison of similarity between images. We propose different metric functions which can be imposed on the image space of curved three dimensional objects. We use these metrics to detect the representative views (most stable and most likely views) of three test models. We find the same representative views under all the investigated metrics, suggesting that local maxima of stability and likelihood are metric independent. Our method of image comparison is based solely on the appearance of the occluding contour, hence our method is suitable for object recognition from silhouettes.

Keywords

Feature Point Object Recognition Image Space Interest Point Dimensional Object 
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 1996

Authors and Affiliations

  • Yoram Gdalyahu
    • 1
  • Daphna Weinshall
    • 1
  1. 1.Institute of Computer ScienceThe Hebrew UniversityJerusalemIsrael

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