NDLI: Shape Descriptors for Maximally Stable Extremal Regions

Content Provider	IEEE Xplore Digital Library
Author	Forssen, P.-E. Lowe, D.G.
Copyright Year	2007
Description	Author affiliation: Univ. of British Columbia, Vancouver (Forssen, P.-E.; Lowe, D.G.)
Abstract	This paper introduces an affine invariant shape descriptor for maximally stable extremal regions (MSER). Affine invariant feature descriptors are normally computed by sampling the original grey-scale image in an invariant frame defined from each detected feature, but we instead use only the shape of the detected MSER itself. This has the advantage that features can be reliably matched regardless of the appearance of the surroundings of the actual region. The descriptor is computed using the scale invariant feature transform (SIFT), with the resampled MSER binary mask as input. We also show that the original MSER detector can be modified to achieve better scale invariance by detecting MSERs in a scale pyramid. We make extensive comparisons of the proposed feature against a SIFT descriptor computed on grey-scale patches, and also explore the possibility of grouping the shape descriptors into pairs to incorporate more context. While the descriptor does not perform as well on planar scenes, we demonstrate various categories of full 3D scenes where it outperforms the SIFT descriptor computed on grey-scale patches. The shape descriptor is also shown to be more robust to changes in illumination. We show that a system can achieve the best performance under a range of imaging conditions by matching both the texture and shape descriptors.
Starting Page	1
Ending Page	8
File Size	4812087
Page Count	8
File Format	PDF
ISBN	9781424416301
ISSN	15505499
DOI	10.1109/ICCV.2007.4409025
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-10-14
Publisher Place	Brazil
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Shape Layout Lighting Object recognition Computer vision Computer science Image sampling Detectors Robustness Object detection
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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Matching Maximally Stable Extremal Regions Using Edge Information and the Chamfer Distance Function

Shape Guided Maximally Stable Extremal Region (MSER) Tracking

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Scene Text Segmentation with Multi-level Maximally Stable Extremal Regions

Evaluation of features detectors and descriptors based on 3D objects

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Features for recognition: viewpoint invariance for non-planar scenes

Shape Descriptors for Maximally Stable Extremal Regions

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