DocumentCode
327696
Title
Texture segmentation using zero crossings information
Author
Smith, Guy ; Longstaff, Dennis
Author_Institution
Co-operative Res. Centre for Sensor, Signal & Inf. Process., Queensland Univ., Australia
Volume
1
fYear
1998
fDate
16-20 Aug 1998
Firstpage
262
Abstract
Image texture can be defined as a local two-dimensional random field. The Gauss Markov random field (GMRF) and grey level co-occurrence (GLC) algorithms compute features from models of this random field. However, the GMRF and GLC algorithms capture only second-order interactions between pixels. We describe an algorithm which models texture as a local two-dimensional random field and captures high-order interactions
Keywords
Markov processes; image coding; image segmentation; image texture; quantisation (signal); random processes; 2D random field; Gauss Markov random field; grey level cooccurrence; high-order interactions; image coding; image segmentation; image texture; quantisation; zero crossings; Convolution; Filtering theory; Histograms; Image segmentation; Image sensors; Image texture; Lab-on-a-chip; Laplace equations; Quantization; Shape;
fLanguage
English
Publisher
ieee
Conference_Titel
Pattern Recognition, 1998. Proceedings. Fourteenth International Conference on
Conference_Location
Brisbane, Qld.
ISSN
1051-4651
Print_ISBN
0-8186-8512-3
Type
conf
DOI
10.1109/ICPR.1998.711131
Filename
711131
Link To Document