Unsupervised segmentation of textured images by edge detection inmultidimensional feature
Khotanzad, A.
Chen, J.-Y.
Dept. of Electr. Eng., Southern Methodist Univ., Dallas, TX;
This paper appears in: Pattern Analysis and Machine Intelligence, IEEE Transactions on
Publication Date: Apr 1989
Volume: 11,
Issue: 4
On page(s): 414-421
ISSN: 0162-8828
References Cited: 16
CODEN: ITPIDJ
INSPEC Accession Number: 3401991
Digital Object Identifier: 10.1109/34.19038
Current Version Published: 2002-08-06
Abstract
An algorithm for unsupervised texture segmentation is developed
that is based on detecting changes in textural characteristics of small
local regions. Six features derived from two, two-dimensional, noncausal
random field models are used to represent texture. These features
contain information about gray-level-value variations in the eight
principal directions. An algorithm for automatic selection of the size
of the observation windows over which textural activity and change are
measured has been developed. Effects of changes in individual features
are considered simultaneously by constructing a one-dimensional measure
of textural change from them. Edges in this measure correspond to the
sought-after textural edges. Experiments results with images containing
regions of natural texture show that the algorithm performs very well
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