Analysis of cluttered scenes using an elastic matching approach for stereo images.

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 Comput

journal_title

Neural computation

authors

Eckes C,Triesch J,von der Malsburg C

doi

10.1162/neco.2006.18.6.1441

subject

Has Abstract

pub_date

2006-06-01 00:00:00

pages

1441-71

issue

6

eissn

0899-7667

issn

1530-888X

journal_volume

18

pub_type

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