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Proceedings Paper

Combining local descriptions with geometric constraints for 3D object recognition in multiple statuses
Author(s): Ying Huang; Xiaoqing Ding; Shengjin Wang
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Paper Abstract

A novel Markov random field (MRF) based framework is developed for the problem of 3D object recognition in multiple statuses. This approach utilizes densely sampled grids to represent the local information of the input images. Markov random field models are then created to model the geometric distribution of the object key points. Flexible matching, which seeks to find an accurate correspondence mapping between the key points of two images, is performed by combining the local similarities with the geometric relations using the highest confidence first (HCF) method. Afterwards, similarities between different images are calculated for object recognition. The algorithm is evaluated using the Coil-100 object database. The excellent recognition rates achieved in all the experiments indicate that our approach is well-suited for appearance-based 3-D object recognition. Comparisons with previous methods show that the proposed one is far more robust in the presence of object zooming, rotation, occlusion, noise, and viewpoint variations.

Paper Details

Date Published: 1 March 2005
PDF: 12 pages
Proc. SPIE 5672, Image Processing: Algorithms and Systems IV, (1 March 2005); doi: 10.1117/12.585757
Show Author Affiliations
Ying Huang, Tsinghua Univ. (China)
Xiaoqing Ding, Tsinghua Univ. (China)
Shengjin Wang, Tsinghua Univ. (China)

Published in SPIE Proceedings Vol. 5672:
Image Processing: Algorithms and Systems IV
Edward R. Dougherty; Jaakko T. Astola; Karen O. Egiazarian, Editor(s)

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