Object Detection Using a Combination of Multiple 3D Feature Descriptors

This paper presents an approach for object pose estimation using a combination of multiple feature descriptors. We propose to use a combination of three feature descriptors, capturing both surface and edge information. Those descriptors individually perform well for different object classes. We use...

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Bibliographic Details
Published inComputer Vision Systems pp. 343 - 353
Main Authors Kiforenko, Lilita, Buch, Anders Glent, Krüger, Norbert
Format Book Chapter
LanguageEnglish
Published Cham Springer International Publishing
SeriesLecture Notes in Computer Science
Subjects
Feature combination
Object detection
Pose estimation
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Summary:This paper presents an approach for object pose estimation using a combination of multiple feature descriptors. We propose to use a combination of three feature descriptors, capturing both surface and edge information. Those descriptors individually perform well for different object classes. We use scenes from an established RGB-D dataset and our own recorded scenes to justify the claim that by combining multiple features, we in general achieve better performance. We present quantitative results for descriptor matching and object detection for both datasets.
ISBN:9783319209036
3319209035
ISSN:0302-9743
1611-3349
DOI:10.1007/978-3-319-20904-3_31