New forms of shape invariants from elliptic Fourier descriptors
Pattern Recognition
Fundamentals of speech recognition
Fundamentals of speech recognition
Factorial Hidden Markov Models
Machine Learning - Special issue on learning with probabilistic representations
The Hierarchical Hidden Markov Model: Analysis and Applications
Machine Learning
The FERET Evaluation Methodology for Face-Recognition Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
IEEE Transactions on Pattern Analysis and Machine Intelligence
Structural Hidden Markov Models Using a Relation of Equivalence: Application to Automotive Designs
Data Mining and Knowledge Discovery
Structural hidden Markov models: An application to handwritten numeral recognition
Intelligent Data Analysis
An MCMC sampling approach to estimation of nonstationary hiddenMarkov models
IEEE Transactions on Signal Processing
Input-output HMMs for sequence processing
IEEE Transactions on Neural Networks
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Hidden Markov models (HMMs) and their variants are capable to classify complex and structured objects. However, one of their major restrictions is their inability to cope with shape or conformation intrinsically: HMM-based techniques have difficulty predicting the n-dimensional shape formed by the symbols of the visible observation (VO) sequence. In order to fulfill this crucial need, we propose a novel paradigm that we named conformation-based hidden Markov models (COHMMs). This new formalism classifies VO sequences by embedding the nodes of an HMM state transition graph in a Euclidean vector space. This is accomplished by modeling the noise contained in the shape composed by the VO sequence. We cover the one-level as well as the multilevel COHMMs. Five problems are assigned to a multilevel COHMM: 1) sequence probability evaluation, 2) statistical decoding, 3) structural decoding, 4) shape decoding, and 5) learning. We have applied the COHMMs formalism to human face identification tested on different benchmarked face databases. The results show that the multilevel COHMMs outperform the embedded HMMs as well as some standard HMM-based models.