Guiding belief propagation using domain knowledge for protein-structure determination
Proceedings of the First ACM International Conference on Bioinformatics and Computational Biology
Detection of near-regular object configurations by elastic graph search
ICCVG'10 Proceedings of the 2010 international conference on Computer vision and graphics: Part I
Probabilistic ensembles for improved inference in protein-structure determination
Proceedings of the 2nd ACM Conference on Bioinformatics, Computational Biology and Biomedicine
| Hi-index | 0.00 |
We describe a part-based object-recognition framework, specialized to mining complex 3D objects from detailed 3D images. Objects are modeled as a collection of parts together with a pairwise potential function. An efficient inference algorithm -- based on belief propagation (BP) -- finds the optimal layout of parts, given some input image. We introduce AggBP, a message aggregation scheme for BP, in which groups of messages are approximated as a single message. For objects consisting of N parts, we reduce CPU time and memory requirements from O( N^2 ) to O(N). We apply AggBP on synthetic data as well as a real-world task identifying protein fragments in three-dimensional images. These experiments show that our improvements result in minimal loss in accuracy in significantly less time.