dc.contributor.author |
Govender, Natasha
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|
dc.contributor.author |
Warrell, J
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|
dc.contributor.author |
Torr, P
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dc.contributor.author |
Nicolls, F
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|
dc.date.accessioned |
2014-10-09T12:05:39Z |
|
dc.date.available |
2014-10-09T12:05:39Z |
|
dc.date.issued |
2014-08 |
|
dc.identifier.citation |
Govender, N., Warrell, J., Torr, P. and Nicolls, F. 2014. Probabilistic models for 2D active shape recognition using Fourier descriptors and mutual information. In: Advances in Computer Vision, Istanbul, 22-23 August 2014 |
en_US |
dc.identifier.uri |
http://www.robots.ox.ac.uk/~tvg/publications/2014/shape_recongition.pdf
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|
dc.identifier.uri |
http://hdl.handle.net/10204/7718
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|
dc.description |
Advances in Computer Vision, Istanbul, 22-23 August 2014. Abstract only added. |
en_US |
dc.description.abstract |
Shape recognition is essential for robots to perform tasks in both human and industrial environments. Many algorithms have been developed for shape recognition with varying results. However, few of the proposed methods actively look for additional information to improve the initial shape recognition results. We propose an initial system which performs shape recognition using the euclidean distances of Fourier descriptors. To improve upon these results we build multinomial and Gaussian probabilistic models using the extracted Fourier descriptors and show how actively looking for cues using mutual information can improve the overall results. These probabilistic models achieve excellent results while significantly improving on the initial system. |
en_US |
dc.language.iso |
en |
en_US |
dc.relation.ispartofseries |
Workflow;13476 |
|
dc.subject |
Shape recognition |
en_US |
dc.subject |
Human and industrial environments |
en_US |
dc.subject |
Gaussian probabilistic models |
en_US |
dc.subject |
Fourier descriptors |
en_US |
dc.title |
Probabilistic models for 2D active shape recognition using Fourier descriptors and mutual information |
en_US |
dc.type |
Conference Presentation |
en_US |
dc.identifier.apacitation |
Govender, N., Warrell, J., Torr, P., & Nicolls, F. (2014). Probabilistic models for 2D active shape recognition using Fourier descriptors and mutual information. http://hdl.handle.net/10204/7718 |
en_ZA |
dc.identifier.chicagocitation |
Govender, Natasha, J Warrell, P Torr, and F Nicolls. "Probabilistic models for 2D active shape recognition using Fourier descriptors and mutual information." (2014): http://hdl.handle.net/10204/7718 |
en_ZA |
dc.identifier.vancouvercitation |
Govender N, Warrell J, Torr P, Nicolls F, Probabilistic models for 2D active shape recognition using Fourier descriptors and mutual information; 2014. http://hdl.handle.net/10204/7718 . |
en_ZA |
dc.identifier.ris |
TY - Conference Presentation
AU - Govender, Natasha
AU - Warrell, J
AU - Torr, P
AU - Nicolls, F
AB - Shape recognition is essential for robots to perform tasks in both human and industrial environments. Many algorithms have been developed for shape recognition with varying results. However, few of the proposed methods actively look for additional information to improve the initial shape recognition results. We propose an initial system which performs shape recognition using the euclidean distances of Fourier descriptors. To improve upon these results we build multinomial and Gaussian probabilistic models using the extracted Fourier descriptors and show how actively looking for cues using mutual information can improve the overall results. These probabilistic models achieve excellent results while significantly improving on the initial system.
DA - 2014-08
DB - ResearchSpace
DP - CSIR
KW - Shape recognition
KW - Human and industrial environments
KW - Gaussian probabilistic models
KW - Fourier descriptors
LK - https://researchspace.csir.co.za
PY - 2014
T1 - Probabilistic models for 2D active shape recognition using Fourier descriptors and mutual information
TI - Probabilistic models for 2D active shape recognition using Fourier descriptors and mutual information
UR - http://hdl.handle.net/10204/7718
ER -
|
en_ZA |