On the relationship of the Markov mesh to the NSHP Markov chain
Pattern Recognition Letters
An Empirical Study of the Simulation of Various Models used for Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Alignment by maximization of mutual information
Alignment by maximization of mutual information
CRV '05 Proceedings of the 2nd Canadian conference on Computer and Robot Vision
Stationary Markov random fields on a finite rectangular lattice
IEEE Transactions on Information Theory
Fast computation methods for estimation of image spatial entropy
Journal of Real-Time Image Processing
Bilateral Markov mesh random field and its application to image restoration
Journal of Visual Communication and Image Representation
Evaluating similarity measures for brain image registration
Journal of Visual Communication and Image Representation
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Shannon entropy is a powerful tool in image analysis, but its reliable computation from image data faces an inherent dimensionality problem that calls for a low-dimensional and closed form model for the pixel value distributions. The most promising such models are Markovian, however, the conventional Markov random field is hampered by noncausality and its causal versions are also not free of difficulties. For example, the Markov mesh random field has its own limitations due to the strong diagonal dependency in its local neighboring system. A new model, named quadrilateral Markov random field (QMRF) is introduced in this paper in order to overcome these limitations. A property of QMRF with neighboring size of 2 is then used to decompose an image prior into a product of 2-D joint pdfs in which they are estimated using a joint histogram under the homogeneity assumption. In addition, the paper includes an extension of the introduced method to the computation of image spatial mutual information. Comparisons on synthesized images as well as two applications with real images are presented to motivate the developments in this paper and demonstrate the advantages in the performance of the introduced method over the existing ones.