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Recognition of geons by parametric deformable contour models

Part of the Lecture Notes in Computer Science book series (LNCS,volume 1064)

Abstract

This paper presents a novel approach to the detection and recognition of qualitative parts like geons from real 2D intensity images. Previous works relied on semi-local properties of either line drawings or good region segmentation. Here, in the framework of Model-Based Optimisation, whole geons or substantial sub-parts are recognised by fitting parametric deformable contour models to the edge image by means of a Maximum A Posteriori estimation performed by Adaptive Simulated Annealing, accounting for image clutter and limited occlusions. A number of experiments, carried out both on synthetic and real edge images, are presented.

Keywords

  • Edge Image
  • Qualitative Part
  • Adaptive Simulated Annealing
  • Occlude Contour
  • Model Prior Probability

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.

Partially supported by SGS-THOMSON Microelectronics.

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© 1996 Springer-Verlag Berlin Heidelberg

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Pilu, M., Fisher, R.B. (1996). Recognition of geons by parametric deformable contour models. In: Buxton, B., Cipolla, R. (eds) Computer Vision — ECCV '96. ECCV 1996. Lecture Notes in Computer Science, vol 1064. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0015524

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  • DOI: https://doi.org/10.1007/BFb0015524

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