Search results for: G. Sfikas
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Lecture Notes in Computer Science > Artificial Neural Networks: Formal Models and Their Applications – ICANN 2005 > Special Session: Intelligent Multimedia and Semantics Organizers and Chairs: Y. Avrithis, S. Kollias > 835-840
In this paper we propose a new distance metric for probability density functions (PDF). The main advantage of this metric is that unlike the popular Kullback-Liebler (KL) divergence it can be computed in closed form when the PDFs are modeled as Gaussian Mixtures (GM). The application in mind for this metric is histogram based image retrieval. We experimentally show that in an image retrieval scenario...
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In medical imaging, constructing an atlas and bringing an image set in a single common reference frame may easily lead the analysis to erroneous conclusions, especially when the population under study is heterogeneous. In this paper, we propose a framework based on spectral clustering that is capable of partitioning an image population into sets that require a separate atlas, and identifying the most...
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We propose a new approach for the restoration of polarimetric Stokes images, capable of simultaneously segmenting and restoring the images. In order to easily handle the admissibility constraints inherent to Stokes images, a proper transformation of the images is introduced. This transformation exploits the correspondence between any Stokes vector and the covariance matrix of the two components of...
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In the context of image segmentation, Markov random fields (MRF) are extensively used. However solution of MRF-based models is heavily dependent on how successfully the MRF energy minimization is performed. In this framework, two methodologies, complementary to each other, are proposed for random field optimization. We address the special class of models comprising a random field imposed on the probabilities...
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A new hierarchical Bayesian model is proposed for image segmentation based on Gaussian mixture models (GMM) with a prior enforcing spatial smoothness. According to this prior, the local differences of the contextual mixing proportions (i.e. the probabilities of class labels) are Studentpsilas t-distributed. The generative properties of the Student's t-pdf allow this prior to impose smoothness and...
