On learning texture edge detectors
Will, S.
Hermes, L.
Buhmann, J.M.
Puzicha, J.
Inst. fur Inf. III, Bonn Univ.;
This paper appears in: Image Processing, 2000. Proceedings. 2000 International Conference on
Publication Date: 2000
Volume: 3,
On page(s): 877-880 vol.3
Meeting Date: 09/10/2000 - 09/13/2000
Location: Vancouver, BC, Canada
ISBN: 0-7803-6297-7
References Cited: 10
INSPEC Accession Number: 7011527
Digital Object Identifier: 10.1109/ICIP.2000.899596
Current Version Published: 2002-08-06
Abstract
Texture is an inherently non-local image property. All common
texture descriptors, therefore, have a significant spatial support which
renders classical edge detection schemes inadequate for the detection of
texture boundaries. In this paper we propose a novel scheme to learn
filters for texture edge detection. Textures are defined by the
statistical distribution of Gabor filter responses. Optimality criteria
for detection reliability and localization accuracy are suggested in the
spirit of Canny's edge detector. Texture edges are determined as zero
crossings of the difference of the two a posteriori class distributions.
An optimization algorithm is designed to determine the best filter
kernel according to the underlying quality measure. The effectiveness of
the approach is demonstrated on texture mondrians composed from the
Brodatz album and a series of synthetic aperture radar (SAR) imagery.
Moreover, we indicate how the proposed scheme can be combined with
snake-type algorithms for prior-knowledge driven boundary refinement and
interactive annotation
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