Texture segmentation using zero crossings information

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 d...

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Bibliographic Details
Main Authors: Smith, G., Longstaff, D.
Format: Conference Proceeding
Language:English
Subjects:
Convolution
Filtering theory
Histograms
Image segmentation
Image sensors
Image texture
Lab-on-a-chip
Laplace equations
Quantization
Shape
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Description
Summary: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.
ISSN:1051-4651
2831-7475
DOI:10.1109/ICPR.1998.711131