Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
A Markov Random Field Model-Based Approach to Image Interpretation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Tree Approximations to Markov Random Fields
IEEE Transactions on Pattern Analysis and Machine Intelligence
The nature of statistical learning theory
The nature of statistical learning theory
Bellman Functions on Trees for Segmentation, Generalized Smoothing, Matching Multi-Alignment in Massive Data Sets
Massive Data Set Analysis in Seismic Explorations for Oil and Gas in Crystalline Basement Interval
Massive Data Set Analysis in Seismic Explorations for Oil and Gas in Crystalline Basement Interval
ICPR '96 Proceedings of the 13th International Conference on Pattern Recognition - Volume 2
A Problem of Pattern Recognition in Arrays of Interrelated Objects. Recognition Algorithm
Automation and Remote Control
Recognition of dependent objects based on acyclic Markov models
Pattern Recognition and Image Analysis
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In the classical pattern recognition problem, consideration is given to individual objects, each of which actually belongs to one of the finite number of classes and is presented for the recognition irrespective of other objects. Recognition objects often form a single interconnected array determined by the nature of the event involved, namely, its natural extent in time or in space along one or a few coordinates. As a consequence, the need arises to take consistent decisions about the classes for all elements of the array. The prior assumption consisting in the fact that neighboring objects more often belong to one class than to different classes will permit one to improve the recognition quality in comparison with the classical case of the independence of classes of separate objects.