Title :
Integrating multiple model views for object recognition
Author :
Ferrari, Vittorio ; Tuytelaars, Tinne ; Van Gool, Luc
Author_Institution :
Comput. Vision Group, ETH, Zurich, Switzerland
fDate :
27 June-2 July 2004
Abstract :
We present a new approach to appearance-based object recognition, which captures the relationships between multiple model views and exploits them to improve recognition performance. The basic building block is local, viewpoint invariant regions. We propose an efficient algorithm for partitioning a set of region matches into groups lying on smooth surfaces (GAMs). During modeling, the model views are connected by a large number of region-tracks, each aggregating image regions of a single physical region across the views. At recognition time, GAMs are constructed matching a test image to each model view. The consistency of configurations of GAMs is measured by exploiting the model connections. A genetic algorithm finds covering the object as completely as possible the most consistent configuration. Introducing GAMs as an intermediate grouping level facilitates decision-making and improves discriminative power. As a complementary application, we introduce a novel GAM-based two-view filter and demonstrate its effectiveness in recovering correct matches in the presence of up to 96% mismatches.
Keywords :
genetic algorithms; image matching; object detection; genetic algorithm; group aggregated matches; multiple model views integration; object recognition; test image matching; Computer vision; Decision making; Genetic algorithms; Image recognition; Matched filters; Object recognition; Partitioning algorithms; Power system modeling; Testing; Voting;
Conference_Titel :
Computer Vision and Pattern Recognition, 2004. CVPR 2004. Proceedings of the 2004 IEEE Computer Society Conference on
Print_ISBN :
0-7695-2158-4
DOI :
10.1109/CVPR.2004.1315151