edited by Horst Bunke (University of Bern, Switzerland) & Terry Caelli (University of Alberta, Canada)

Hidden Markov models (HMMs) originally emerged in the domain of speech recognition. In recent years, they have attracted growing interest in the area of computer vision as well. This book is a collection of articles on new developments in the theory of HMMs and their application in computer vision. It addresses topics such as handwriting recognition, shape recognition, face and gesture recognition, tracking, and image database retrieval.

This book is also published as a special issue of the International Journal of Pattern Recognition and Artificial Intelligence (February 2001).

• Introduction: A Simple Complex in Artificial Intelligence and Machine Learning (B H Juang)
• An Introduction to Hidden Markov Models and Bayesian Networks (Z Chahramani)
• Multi-Lingual Machine Printed OCR (P Natarajan et al.)
• Using a Statistical Language Model to Improve the Performance of an HMM-Based Cursive Handwriting Recognition System (U-V Marti & H Bunke)
• A 2-D HMM Method for Offline Handwritten Character Recognition (H-S Park et al.)
• Data-Driven Design of HMM Topology for Online Handwriting Recognition (J J Lee et al.)
• Hidden Markov Models for Modeling and Recognizing Gesture Under Variation (A D Wilson & A F Bobick)
• Sentence Lipreading Using Hidden Markov Model with Integrated Grammar (K Yu et al.)
• Tracking and Surveillance in Wide-Area Spatial Environments Using the Abstract Hidden Markov Model (H H Bui et al.)
• Shape Tracking and Production Using Hidden Markov Models (T Caelli et al.)
• An Integrated Approach to Shape and Color-Based Image Retrieval of Rotated Objects Using Hidden Markov Models (S Müller et al.)