This paper introduces a new class of finite mixture models for solving such problems. The proposed non-extensive mixture models have real-valued power-law ...
This paper introduces a new class of finite mixture models for solving such problems. The proposed non-extensive mixture models have real-valued power-law ...
Abstract. We present a novel algorithm for unsupervised segmentation of natural images that harnesses the principle of minimum description length (MDL).
Finite mixture model with symmetric distribution has been widely used for many computer vision and pattern recognition problems.
Apr 17, 2009 · Different texture regions of a natural image admit a mixture model. ... The non-parametric model P is obtained by kernel density estimation.
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Mixture model is a compelling framework for unsupervised image segmentation. A segmented image ... Natural image segmentation with non-extensive mixture models. J ...
[PDF] Flexibly Regularized Mixture Models and Application to Image ...
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To illustrate our approach, we consider unsupervised segmentation of natural images: a paradigmatic application of finite mixture models, in which accounting ...
Missing: extensive | Show results with:extensive
In this work, we propose a new Bayesian model for unsupervised image segmentation based on a combination of the spatially varying finite mixture models (SVFMMs) ...
Sep 4, 2018 · Abstract Gaussian mixture model (GMM) is a flexible tool for image segmen- tation and image classification. However, one main limitation of ...
Abstract. Finite mixture models with symmetric distribution have been widely used for many computer vision and pattern recognition problems. However, in many ...
Missing: Natural extensive