Road extraction from multi-temporal satellite images by an evidential reasoning approach
Pattern Recognition Letters
Machine Vision and Applications
Active fusion—a new method applied to remote sensing image interpretation
Pattern Recognition Letters - Special issue on non-conventional pattern analysis in remote sensing
Postprocessing of Recognized Strings Using Nonstationary Markovian Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Segmentation of Color Textures
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mixture model for face-color modeling and segmentation
Pattern Recognition Letters
Off-Line Handwritten Word Recognition Using a Hidden Markov Model Type Stochastic Network
IEEE Transactions on Pattern Analysis and Machine Intelligence
A region growing and merging algorithm to color segmentation
Pattern Recognition
Vector order statistics operators as color edge detectors
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Multi-resolution screening of paper formation variations on production line
Expert Systems with Applications: An International Journal
Screening paper formation variations on production line
IEA/AIE'07 Proceedings of the 20th international conference on Industrial, engineering, and other applications of applied intelligent systems
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The problem of post-processing of a classified image is addressed from the point of view of the Dempster-Shafer theory of evidence. Each neighbour of a pixel being analyzed is considered as an item of evidence supporting particular hypotheses regarding the class label of that pixel. The strength of support is defined as a function of the degree of uncertainty in class label of the neighbour, and the distance between the neighbour and the pixel being considered. A post-processing window defines the neighbours. Basic belief masses are obtained for each of the neighbours and aggregated according to the rule of orthogonal sum. The final label of the pixel is chosen according to the maximum of the belief function.