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Hidden Markov Models for Spatio-Temporal Pattern Recognition and Image Segmentation
Lovell, Brian C. (2003). Hidden Markov Models for Spatio-Temporal Pattern Recognition and Image Segmentation. In: Mukherjee, Dipti Prasad and Pal, Srimanta, Proceedings of the International Conference on Advances in Pattern Recognition. International Conference on Advances in Pattern Recognition, Calcutta, (60-65). 10-13 December.
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| Name |
Description |
MIMEType |
Size |
Downloads |
icapr.pdf
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icapr.pdf |
application/pdf |
403.49KB |
1227 |
| Author(s) |
Lovell, Brian C.
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| Title of paper |
Hidden Markov Models for Spatio-Temporal Pattern Recognition and Image Segmentation
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| Conference name |
International Conference on Advances in Pattern Recognition
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| Conference location |
Calcutta
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| Conference dates |
10-13 December
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| Proceedings title |
Proceedings of the International Conference on Advances in Pattern Recognition
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| Editor(s) |
Mukherjee, Dipti Prasad
Pal, Srimanta
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| Place published |
Kolkata
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| Publisher |
Allied Publishers
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| Publication date |
2003
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| Volume number |
1
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| Issue number |
1
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| ISBN |
81-7764-532-3
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| Start page |
60
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| End page |
65
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| Total pages |
6
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| Language |
eng
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| Abstract/Summary |
Time and again hidden Markov models have been demonstrated to be highly effective in one-dimensional pattern recognition and classification problems such as speech recognition. A great deal of attention is now focussed on 2-D and possibly 3-D applications arising from problems encountered in computer vision in domains such as gesture, face, and handwriting recognition. Despite their widespread usage and numerous successful applications, there are few analytical results which can explain their remarkably good performance and guide researchers in selecting topologies and parameters to improve classification performance.
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| Subjects |
280207 Pattern Recognition
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| Keyword(s) |
iris-research
image segmentation
pattern recognition
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| Additional Notes |
Invited Paper
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