Type-2 fuzzy Gaussian mixture models
Pattern Recognition
Information Sciences: an International Journal
Cascade Markov random fields for stroke extraction of Chinese characters
Information Sciences: an International Journal
A recurrent self-evolving interval type-2 fuzzy neural network for dynamic system processing
IEEE Transactions on Fuzzy Systems
An interval type-2 fuzzy-neural network with support-vector regression for noisy regression problems
IEEE Transactions on Fuzzy Systems
Review: Industrial applications of type-2 fuzzy sets and systems: A concise review
Computers in Industry
ICCCI'12 Proceedings of the 4th international conference on Computational Collective Intelligence: technologies and applications - Volume Part I
Overview of Type-2 Fuzzy Logic Systems
International Journal of Fuzzy System Applications
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In this paper, we integrate type-2 (T2) fuzzy sets with Markov random fields (MRFs) referred to as T2 FMRFs, which may handle both fuzziness and randomness in the structural pattern representation. On the one hand, the T2 membership function (MF) has a 3-D structure in which the primary MF describes randomness and the secondary MF evaluates the fuzziness of the primary MF. On the other hand, MRFs can represent patterns statistical-structurally in terms of neighborhood system and clique potentials and, thus, have been widely applied to image analysis and computer vision. In the proposed T2 FMRFs, we define the same neighborhood system as that in classical MRFs. To describe uncertain structural information in patterns, we derive the fuzzy likelihood clique potentials from T2 fuzzy Gaussian mixture models. The fuzzy prior clique potentials are penalties for the mismatched structures based on prior knowledge. Because Chinese characters have hierarchical structures, we use T2 FMRFs to model character structures in the handwritten Chinese character recognition system. The overall recognition rate is 99.07%, which confirms the effectiveness of the proposed method.