Please use this identifier to cite or link to this item: https://doi.org/10.1109/ICIP.2010.5653951
Title: 3D augmented Markov random field for object recognition
Authors: Yu W.
Ashraf A.B.
Chang Y.-J.
Li C.
Chen T. 
Keywords: 3D
Markov random field
Object recognition
Stereo
Issue Date: 2010
Citation: Yu W., Ashraf A.B., Chang Y.-J., Li C., Chen T. (2010). 3D augmented Markov random field for object recognition. Proceedings - International Conference on Image Processing, ICIP : 3889-3892. ScholarBank@NUS Repository. https://doi.org/10.1109/ICIP.2010.5653951
Abstract: In this paper, we propose the use of 3D information to augment the Markov random field (MRF) model for object recognition. Conventional MRF for image-based object recognition usually uses appearance and 2D location as features in the model. We estimate rough 3D information from stereo image pairs, and incorporate this information into node and edge potential models in the conventiona lMRF. Introducing 3D information into the node potential allows to leverage the distribution statistics of 3D location for different classes. We solve the object recognition problem by finding the globally optimal class assignment that minimizes an energy function defined in the augmented MRF. We show that the introduction of 3D distance in the edge potential can help distinguish "true" neighbors from "fake" neighbors in 2D. We demonstrate improved recognition results by using the proposed technique.
Source Title: Proceedings - International Conference on Image Processing, ICIP
URI: http://scholarbank.nus.edu.sg/handle/10635/146170
ISBN: 9781424479948
ISSN: 15224880
DOI: 10.1109/ICIP.2010.5653951
Appears in Collections:Staff Publications

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