Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
10359910 | Computer Vision and Image Understanding | 2005 | 18 Pages |
Abstract
This paper presents a method for object recognition once parts have been detected. The recognition task is formulated as a graph problem searching for the characteristic geographical arrangements of (possibly missing) parts. The objective function is Bayesian maximum a posteriori estimation, integrating the image likelihood as a posteriori probability of the part detectors. The variability in the arrangement of object parts is captured by a Gaussian distribution after translation normalization. By employing two special properties of a Gaussian distribution, we are able to deal with missing parts situation where the chosen origin is not detected. We use an Aâ algorithm to find the optimal solution for the graph search problem. Experiments are performed on both synthetic and real data to demonstrate good results and fast performance of the recognition.
Keywords
Related Topics
Physical Sciences and Engineering
Computer Science
Computer Vision and Pattern Recognition
Authors
Thang V. Pham, Arnold W.M. Smeulders,