Recognition using specular highlights

Aaron Netz, Margarita Osadchy

Research output: Contribution to journalArticlepeer-review

Abstract

We present a novel approach to pose estimation and model-based recognition of specular objects in difficult viewing conditions, such as low illumination, cluttered background, large highlights, and shadows that appear on the object of interest. In such challenging conditions, conventional features are unreliable. We show that under the assumption of a dominant light source, specular highlights produced by a known object can be used to establish correspondence between its image and the 3D model, and to verify the hypothesized pose and the identity of the object. Previous methods that use highlights for recognition make limiting assumptions such as known pose, scene-dependent calibration, simple shape, etc. The proposed method can efficiently recognize free-form specular objects in arbitrary pose and under unknown lighting direction. It uses only a single image of the object as its input and outputs object identity and the full pose. We have performed extensive experiments for both recognition and pose estimation accuracy on synthetic images and on real indoor and outdoor images.

Original languageEnglish
Article number6212513
Pages (from-to)639-652
Number of pages14
JournalIEEE Transactions on Pattern Analysis and Machine Intelligence
Volume35
Issue number3
DOIs
StatePublished - 2013

Keywords

  • Object recognition
  • invariants
  • pose estimation
  • specularities
  • varying illumination

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence
  • Applied Mathematics
  • Computer Vision and Pattern Recognition
  • Computational Theory and Mathematics

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