New forms of shape invariants from elliptic Fourier descriptors
Pattern Recognition
The State of the Art in Online Handwriting Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fundamentals of speech recognition
Fundamentals of speech recognition
Factorial Hidden Markov Models
Machine Learning - Special issue on learning with probabilistic representations
Postprocessing of Recognized Strings Using Nonstationary Markovian Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Hierarchical Hidden Markov Model: Analysis and Applications
Machine Learning
On the approximation of curves by line segments using dynamic programming
Communications of the ACM
Pattern Recognition and Image Preprocessing
Pattern Recognition and Image Preprocessing
Coupled hidden Markov models for complex action recognition
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Feature Extraction of Protein Folds Based on Secondary Structure Transformation
INTSYS '98 Proceedings of the IEEE International Joint Symposia on Intelligence and Systems
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
A Theoretical and Experimental Analysis of Linear Combiners for Multiple Classifier Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Semi-supervised protein classification using cluster kernels
Bioinformatics
Structural Hidden Markov Models Using a Relation of Equivalence: Application to Automotive Designs
Data Mining and Knowledge Discovery
Adaptive online multi-stroke sketch recognition based on hidden markov model
ICMLC'05 Proceedings of the 4th international conference on Advances in Machine Learning and Cybernetics
Image classification by a two-dimensional hidden Markov model
IEEE Transactions on Signal Processing
Improved hidden Markov models in the wavelet-domain
IEEE Transactions on Signal Processing
Hidden Markov model state estimation with randomly delayedobservations
IEEE Transactions on Signal Processing
Input-output HMMs for sequence processing
IEEE Transactions on Neural Networks
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Current extensions of hidden Markov models such as structural, hierarchical, coupled, and others have the power to classify complex and highly organized patterns. However, one of their major limitations is the inability to cope with topology: When applied to a visible observation (VO) sequence, the traditional HMM-based techniques have difficulty predicting the n-dimensional shape formed by the symbols of the VO sequence. To fulfill this need, we propose a novel paradigm named ''topological hidden Markov models'' (THMMs) that classifies VO sequences by embedding the nodes of an HMM state transition graph in a Euclidean space. This is achieved by modeling the noise embedded in the shape generated by the VO sequence. We cover the first and second level topological HMMs. We describe five basic problems that are assigned to a second level topological hidden Markov model: (1) sequence probability evaluation, (2) statistical decoding, (3) structural decoding, (4) topological decoding, and (5) learning. To show the significance of this research, we have applied the concept of THMMs to: (i) predict the ASCII class assigned to a handwritten numeral, and (ii) map protein primary structures to their 3D folds. The results show that the second level THMMs outperform the SHMMs and the multi-class SVM classifiers significantly.