Shape tracking and production using hidden Markov models

Caelli, Terry, Mccabe, Andrew and Briscoe, Garry 2001, Shape tracking and production using hidden Markov models, International Journal of Pattern Recognition and Artificial Intelligence, vol. 15, no. 1, pp. 197-221, doi: 10.1142/S0218001401000794.


Title Shape tracking and production using hidden Markov models
Author(s) Caelli, TerryORCID iD for Caelli, Terry orcid.org/0000-0001-9281-2556
Mccabe, Andrew
Briscoe, Garry
Journal name International Journal of Pattern Recognition and Artificial Intelligence
Volume number 15
Issue number 1
Start page 197
End page 221
Total pages 25
Publisher WORLD SCIENTIFIC PUBL CO PTE LTD
Publication date 2001-02-01
ISSN 0218-0014
1793-6381
Keyword(s) Science & Technology
Technology
Computer Science, Artificial Intelligence
Computer Science
feature extraction
pattern recognition
scene understanding
tracking human performance
learning
Hidden Markov Models
RECOGNITION
Summary This paper is concerned with an application of Hidden Markov Models (HMMs) to the generation of shape boundaries from image features. In the proposed model, shape classes are defined by sequences of "shape states" each of which has a probability distribution of expected image feature types (feature "symbols").The tracking procedure uses a generalization of the well-known Viterbi method by replacing its search by a type of "beam-search" so allowing the procedure, at any time, to consider less likely features (symbols) as well the search for an instantiable optimal state sequences. We have evaluated the model's performance on a variety of image and shape types and have also developed a new performance measure defined by an expected Hamming distance between predicted and observed symbol sequences. Results point to the use of this type of model for the depiction of shape boundaries when it is necessary to have accurate boundary annotations as, for example, occurs in Cartography.
Language eng
DOI 10.1142/S0218001401000794
Indigenous content off
Field of Research 0801 Artificial Intelligence and Image Processing
1702 Cognitive Sciences
HERDC Research category C1 Refereed article in a scholarly journal
Persistent URL http://hdl.handle.net/10536/DRO/DU:30138460

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