Shape Descriptors for Maximally Stable Extremal Regions
Per-Erik Forssén, David Lowe
ICCV07, Rio de Janeiro, Brazil
IEEE International Conference on Computer Vision
Volume CFP07198-CDR
October 2007
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.
Full Paper
Portable document format file PDF
Bibtex entry
@InProceedings{fl07,
author = {Per-Erik Forss{\'e}n and David Lowe},
title = {Shape Descriptors for Maximally Stable Extremal Regions},
OPTcrossref = {},
OPTkey = {},
booktitle = {{IEEE} International Conference on Computer Vision},
OPTpages = {},
year = {2007},
OPTeditor = {},
volume = {CFP07198-CDR},
OPTnumber = {},
OPTseries = {},
address = {Rio de Janeiro, Brazil},
month = {October},
publisher = {{IEEE} Computer Society}
}
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