Geometric Hashing with Local Affine Frames
Chum, Ondřej, Matas, Jiří
CVPR, Los Alamitos, USA
Proc. of Conference on Computer Vision and Pattern Recognition (CVPR)
Volume 1, Pages 879-884
June, 2006
Abstract
We propose a novel representation of local image structure and a
matching scheme that are insensitive to a wide range of appearance
changes. The representation is a collection of local affine frames
that are constructed on outer boundaries of maximally stable extremal
regions (MSERs) in an affine-covariant way. Each local affine frame is
described by a relative location of other local affine frames in its
neighborhood. The image is thus represented by quantities that
depend only on the location of the boundaries of MSERs. Inter-image
correspondences between local affine frames are formed in constant
time by geometric hashing. Direct detection of local affine frames
removes the requirement of point-based hashing to establish
reference frames in a combinatorial way, which has in the case of
affine transform complexity that is cubic in the number of points.
Local affine frames, which are also the quantities represented in the
hash table, occupy a 6D space and hence data collisions are less
likely compared with 2D point hashing. Experimentally, the robustness
of the method and its insensitivity to photometric changes is
demonstrated on images from different spectral bands of satellite
sensor, on images of a transparent object and on images of an object
taken during day and night.
Keywords
MSER, hashing, two-view matching, wide-baseline stereo
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Bibtex entry
@InProceedings{chum-hashing-cvpr06,
author = {Chum, Ond{\vr}ej and Matas, Ji{\vr}{\'i}},
title = {Geometric Hashing with Local Affine Frames},
booktitle = {Proc. of Conference on Computer Vision and
Pattern Recognition (CVPR)},
address = {Los Alamitos, USA} ,
year = {2006},
month = {June},
day = {17--22},
isbn = {0-7695-2597-0},
publisher = {IEEE Computer Society},
book_pages = {1313},
pages = {879--884},
psurl = {[pdf]},
annote = { We propose a novel representation of local image
structure and a matching scheme that are insensitive to a wide
range of appearance changes. The representation is a collection of
local affine frames that are constructed on outer boundaries of
maximally stable extremal regions (MSERs) in an affine-covariant
way. Each local affine frame is de- scribed by a relative location
of other local affine frames in its neighborhood. The image is
thus represented by quan- tities that depend only on the location
of the boundaries of MSERs. Inter-image correspondences between
local affine frames are formed in constant time by geometric
hashing. Direct detection of local affine frames removes the
require- ment of point-based hashing to establish reference frames
in a combinatorial way, which has in the case of affine trans-
form complexity that is cubic in the number of points. Local
affine frames, which are also the quantities represented in the
hash table, occupy a 6D space and hence data collisions are less
likely compared with 2D point hashing. Experimentally, the
robustness of the method and its in- sensitivity to photometric
changes is demonstrated on im- ages from different spectral bands
of satellite sensor, on images of a transparent object and on
images of an object taken during day and night. },
keywords = {MSER, hashing, two-view matching, wide-baseline stereo},
editor = {Fitzgibbon, Andrew and Taylor, Camillo and LeCun , Yan},
venue = {New York , USA },
volume = { 1 },
}