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ACM is one of the popular models for image segmentation.
Broadly speaking, there exists a continuum of contextual models for image region classification.
Moreover, there are other variational models for image segmentation [6 8].
Models for image representation generally involve minimum distances between the pixels and borders of components, pixels closer to a given set of pixels than to another set.
One of the widely used variational models for image segmentation is the Potts model [11] which was proposed by Potts in 1952.
Among them, the Mumford-Shah (M-S) model [3] is one of the most widely studied mathematical models for image analysis.
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The concept of aspect models for images has been recently applied to scene [8, 15, 21] and object [40, 41] categorization tasks, using the estimated distribution over aspects as a feature extraction process, or directly as a classifier.
In Section 3, we propose a new hybrid model for image smoothing.
A multi-threshold level set model for image segmentations is presented in the paper.
Fractional Brownian motion (fBm) model for image texture is used to derive the required Fisher information.
CE contributed towards the general framework of the proposed prediction based model for image compression.
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