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Feature-Based Deformable Surface Detection with Self-Occlusion Reasoning

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Abstract

This paper presents a method for detecting a textured deformed surface in an image. It uses (wide-baseline) point matches between a template and the input image. The main contribution of the paper is twofold. First, we propose a robust method based on local surface smoothness capable of discarding outliers from the set of point matches. Our method handles large proportions of outliers (beyond 70% with less than 15% of false positives) even when the surface self-occludes. Second, we propose a method to estimate a self-occlusion resistant warp from point matches. Our method allows us to realistically retexture the input image. A pixel-based (direct) registration approach is also proposed. Bootstrapped by our robust point-based method, it finely tunes the warp parameters using the value (intensity or color) of all the visible surface pixels. The proposed framework was tested with simulated and real data. Convincing results are shown for the detection and retexturing of deformed surfaces in challenging images.

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Authors and Affiliations

  1. University of Alcala, Alcala de Henares, Spain

    Daniel Pizarro

  2. Clermont Université, Clermont-Ferrand, France

    Adrien Bartoli

Authors
  1. Daniel Pizarro
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  2. Adrien Bartoli
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Correspondence to Daniel Pizarro.

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Pizarro, D., Bartoli, A. Feature-Based Deformable Surface Detection with Self-Occlusion Reasoning. Int J Comput Vis 97, 54–70 (2012). https://doi.org/10.1007/s11263-011-0452-0

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  • DOI: https://doi.org/10.1007/s11263-011-0452-0

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