Document Detail


Probabilistic 3D object recognition and pose estimation using multiple interpretations generation.
MedLine Citation:
PMID:  22193274     Owner:  NLM     Status:  In-Data-Review    
Abstract/OtherAbstract:
This paper presents a probabilistic object recognition and pose estimation method using multiple interpretation generation in cluttered indoor environments. How to handle pose ambiguity and uncertainty is the main challenge in most recognition systems. In order to solve this problem, we approach it in a probabilistic manner. First, given a three-dimensional (3D) polyhedral object model, the parallel and perpendicular line pairs, which are detected from stereo images and 3D point clouds, generate pose hypotheses as multiple interpretations, with ambiguity from partial occlusion and fragmentation of 3D lines especially taken into account. Different from the previous methods, each pose interpretation is represented as a region instead of a point in pose space reflecting the measurement uncertainty. Then, for each pose interpretation, more features around the estimated pose are further utilized as additional evidence for computing the probability using the Bayesian principle in terms of likelihood and unlikelihood. Finally, fusion strategy is applied to the top ranked interpretations with high probabilities, which are further verified and refined to give a more accurate pose estimation in real time. The experimental results show the performance and potential of the proposed approach in real cluttered domestic environments.
Authors:
Zhaojin Lu; Sukhan Lee
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Publication Detail:
Type:  Journal Article    
Journal Detail:
Title:  Journal of the Optical Society of America. A, Optics, image science, and vision     Volume:  28     ISSN:  1520-8532     ISO Abbreviation:  J Opt Soc Am A Opt Image Sci Vis     Publication Date:  2011 Dec 
Date Detail:
Created Date:  2011-12-23     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  9800943     Medline TA:  J Opt Soc Am A Opt Image Sci Vis     Country:  United States    
Other Details:
Languages:  eng     Pagination:  2607-18     Citation Subset:  IM    
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