Abstract
In this paper, we propose Markov random field models for pattern recognition, which provide a flexible and natural framework for modelling the interactions between spatially related random variables in their neighbourhood systems. The proposed approach is superior to conventional approaches in many aspects. This paper introduces the concept of states into Markov random filed models, presents a theoretic analysis of the approach, discusses issues of designing neighbourhood system and cliques, and analyses properties of the models. We have applied our method to the recognition of unconstrained handwritten numerals. The experimental results show that the proposed approach can achieve high performance. © 2001 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved.
| Original language | English |
|---|---|
| Pages (from-to) | 725-733 |
| Journal | Pattern Recognition |
| Volume | 35 |
| Issue number | 3 |
| DOIs | |
| Publication status | Published - Mar 2002 |
Bibliographical note
Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].Research Keywords
- Handwritten numerical recognition
- Markov random field
- Neighbourhood system
- Pattern recognition
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