Abstract:
:We present a system for the automatic interpretation of cluttered scenes containing multiple partly occluded objects in front of unknown, complex backgrounds. The system is based on an extended elastic graph matching algorithm that allows the explicit modeling of partial occlusions. Our approach extends an earlier system in two ways. First, we use elastic graph matching in stereo image pairs to increase matching robustness and disambiguate occlusion relations. Second, we use richer feature descriptions in the object models by integrating shape and texture with color features. We demonstrate that the combination of both extensions substantially increases recognition performance. The system learns about new objects in a simple one-shot learning approach. Despite the lack of statistical information in the object models and the lack of an explicit background model, our system performs surprisingly well for this very difficult task. Our results underscore the advantages of view-based feature constellation representations for difficult object recognition problems.
journal_name
Neural Computjournal_title
Neural computationauthors
Eckes C,Triesch J,von der Malsburg Cdoi
10.1162/neco.2006.18.6.1441subject
Has Abstractpub_date
2006-06-01 00:00:00pages
1441-71issue
6eissn
0899-7667issn
1530-888Xjournal_volume
18pub_type
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