Hidden Markov Models: Applications In Computer Vision
Will be published approx. on 7. June 2001
Book
Hardback
244 pages
978-981-02-4564-1 (ISBN)
Description
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).
More details
Series
Language
English
Place of publication
Singapore
Singapore
Target group
College/higher education
Professional and scholarly
Product notice
Unsewn / adhesive bound
Paper over boards
Dimensions
Height: 252 mm
Width: 166 mm
Thickness: 20 mm
Weight
535 gr
ISBN-13
978-981-02-4564-1 (9789810245641)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Persons
Content
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 and H. Bunke; a 2-D HMM method for offline handwritten character recognition, H.-S. Park et al; data-driven design for HMM topology for online handwriting recognition, J.J. Lee et al; hidden Markov models for modelling and recognizing gesture under variation, A.D. Wilson and 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 colour-based image retrieval of rotated objects using hidden Markov models, S. Muller et al.