Robust Hypothesis Verification : Application to Model Based Object Recognition

Frédéric Jurie 1
1 image
LASMEA - Laboratoire des sciences et matériaux pour l'électronique et d'automatique
Abstract : The use of hypothesis verification is recurrent in the model based recognition literature. Small sets of features forming salient groups are paired with model features. Pose can be hypothesised from this small set of correspondences. Verification of the pose consists in measuring how much model features transformed by the computed pose coincide with image features. When data involved in the initial pairing are noisy the pose is inaccurate and verification is a difficult problem. In this paper we propose to use a robust hypothesis verification algorithm to perform object recognition. We explain how to integrate it in two different recognition schemes (2D and 3D recognition). After describing these applications we present numerous experimental results proving the robustness and the efficiency of these algorithms.
Document type :
Journal articles
Complete list of metadatas

https://hal.inria.fr/inria-00548321
Contributor : Thoth Team <>
Submitted on : Monday, December 20, 2010 - 8:43:19 AM
Last modification on : Tuesday, June 5, 2018 - 6:00:02 PM

Links full text

Identifiers

Citation

Frédéric Jurie. Robust Hypothesis Verification : Application to Model Based Object Recognition. Pattern Recognition, Elsevier, 1999, 32 (6), pp.1069--1081. ⟨10.1016/S0031-3203(98)00126-5⟩. ⟨inria-00548321⟩

Share

Metrics

Record views

82