NDLI: Shape matching of repeatable interest segments in 3D point clouds

Content Provider	IEEE Xplore Digital Library
Author	Lam, J. Greenspan, M.
Copyright Year	2012
Description	Author affiliation: Dept. Electrical & Computer Engineering, Queen's University, Kingston, Canada (Lam, J.; Greenspan, M.)
Abstract	A novel approach to object recognition based on shape matching of repeatable segments is presented. The motivation is to increase the recognition system robustness in handling problems such as noise corruption at a local level, featureless surfaces, and variations in 3D data sources. Inspired by the detection of repeatable interest points, interest segments were extracted through region growing and the reconstruction of piece-wise boundary curves from connected interest points. An object pose is automatically estimated if only one of the repeatable scene segments can be matched and aligned correctly with a model segment. To demonstrate this capability, shape matching of selected segments, filtered by size, were registered using the 4 points congruent sets (4PCS) algorithm and compared with an overlap metric. Three different free-form objects were evaluated against nine different occluded and cluttered 2.5D scenes. It was found that on average 1.4 ± 0.8 scene segments can be matched correctly to a model segment in the database, indicating that a highly robust object recognition system will result.
Starting Page	25
Ending Page	32
File Size	916741
Page Count	8
File Format	PDF
ISBN	9781467316118
ISSN	21607508
e-ISBN	9781467316125
e-ISBN	9781467316101
DOI	10.1109/CVPRW.2012.6238911
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-06-16
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Solid modeling Image segmentation Shape Databases Robustness Data models Object recognition
Content Type	Text
Resource Type	Article

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