Bayesian segmentation of AM-FM texture images

We present a fully unsupervised parametric modulation domain technique for segmenting textured images. Textured regions in the image are modeled as multicomponent sums of nonstationary AM-FM functions. The dominant modulations at each pixel are estimated using a technique called DCA and used to cons...

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Bibliographic Details
Main Authors: Yap, T.B., Havlicek, J.P., DeBrunner, V.
Format: Conference Proceeding
Language:English
Subjects:
Bayesian methods
Biological system modeling
Frequency modulation
Gaussian distribution
Gaussian processes
Image segmentation
Machine vision
Maximum likelihood estimation
Power system modeling
Robustness
Online Access:Request full text
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Description
Summary:We present a fully unsupervised parametric modulation domain technique for segmenting textured images. Textured regions in the image are modeled as multicomponent sums of nonstationary AM-FM functions. The dominant modulations at each pixel are estimated using a technique called DCA and used to construct modulation domain feature vectors. The overall feature space is regarded as a mixture of Gaussians, where the modulations within each texture class are modeled by a single multivariate normal distribution. Although this model is somewhat unrealistic, it leads to a robust segmentation algorithm that is able to operate in a fully unsupervised mode. An EM algorithm is used to estimate the parameters of the Gaussian mixture so that approximate maximum-likelihood estimates of the pixel class labels can be obtained. The proposed technique is demonstrated on a variety of images constructed from juxtapositions of Brodatz-like textures.
ISSN:1058-6393
2576-2303
DOI:10.1109/ACSSC.2001.987673