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Transductive Segmentation of Textured Meshes

  • Anne-Laure Chauve
  • Jean-Philippe Pons
  • Jean-Yves Audibert
  • Renaud Keriven
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5995)

Abstract

This paper addresses the problem of segmenting a textured mesh into objects or object classes, consistently with user-supplied seeds. We view this task as transductive learning and use the flexibility of kernel-based weights to incorporate a various number of diverse features. Our method combines a Laplacian graph regularizer that enforces spatial coherence in label propagation and an SVM classifier that ensures dissemination of the seeds characteristics. Our interactive framework allows to easily specify classes seeds with sketches drawn on the mesh and potentially refine the segmentation. We obtain qualitatively good segmentations on several architectural scenes and show the applicability of our method to outliers removing.

Keywords

Point Cloud Test Point Training Point Label Propagation Sparse Linear System 
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 2010

Authors and Affiliations

  • Anne-Laure Chauve
    • 1
  • Jean-Philippe Pons
    • 1
  • Jean-Yves Audibert
    • 1
  • Renaud Keriven
    • 1
  1. 1.IMAGINE, ENPC/CSTB/LIGMUniversité Paris-EstFrance

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