Markov Random Field Models for Unsupervised Segmentation of Textured Color Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Morphological Scale-Space Representation with Levelings
SCALE-SPACE '99 Proceedings of the Second International Conference on Scale-Space Theories in Computer Vision
A Lattice Approach to Image Segmentation
Journal of Mathematical Imaging and Vision
Elements of Information Theory (Wiley Series in Telecommunications and Signal Processing)
Elements of Information Theory (Wiley Series in Telecommunications and Signal Processing)
On Genuine Connectivity Relations Based on Logical Predicates
ICIAP '07 Proceedings of the 14th International Conference on Image Analysis and Processing
Constrained Connectivity for Hierarchical Image Decomposition and Simplification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Hierarchical Segmentation of Complex Structures
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
Preventing chaining through transitions while favouring it within homogeneous regions
ISMM'11 Proceedings of the 10th international conference on Mathematical morphology and its applications to image and signal processing
Preventing chaining through transitions while favouring it within homogeneous regions
ISMM'11 Proceedings of the 10th international conference on Mathematical morphology and its applications to image and signal processing
Pattern spectra from partition pyramids and hierarchies
ISMM'11 Proceedings of the 10th international conference on Mathematical morphology and its applications to image and signal processing
Local Mutual Information for Dissimilarity-Based Image Segmentation
Journal of Mathematical Imaging and Vision
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A dissimilarity measure between adjacent pixels of an image is usually determined by the intensity values of these pixels and therefore does not depend on statistics computed over the whole image domain. In this paper, new dissimilarity measures exploiting image statistics are proposed. This is achieved by introducing the notion of dissimilarity function defined for every possible pair of intensity values. Necessary conditions for generating a valid dissimilarity function are provided and a series of functions integrating image statistics are presented. For example, the joint probability of adjacent pixel values leads to the notion of frequent connectivity while the notion of dependent connectivity relies on the local mutual information. The usefulness of the proposed approach is demonstrated by a series of experiments on satellite image data.