dc.contributor.author |
De Villiers, JP
|
|
dc.contributor.author |
Cronje, J
|
|
dc.contributor.author |
Nicolls, FC
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|
dc.date.accessioned |
2012-01-16T10:55:47Z |
|
dc.date.available |
2012-01-16T10:55:47Z |
|
dc.date.issued |
2011-04 |
|
dc.identifier.citation |
De Villiers, JP, Cronje, J and Nicolls, FC. 2011. Improved neural network modeling of inverse lens distortion. Defense, Security, and Sensing (DSS11), Orlando World Center Marriott Resort & Convention Centre, Orlando, Florida, USA, 26-28 April 2011, 9 pp |
en_US |
dc.identifier.uri |
http://hdl.handle.net/10204/5488
|
|
dc.description |
Defense, Security, and Sensing (DSS11), Orlando World Center Marriott Resort & Convention Centre, Orlando, Florida, USA, 26-28 April 2011 |
en_US |
dc.description.abstract |
Inverse lens distortion modelling allows one to find the pixel in a distorted image which corresponds to a known point in object space, such as may be produced by a RADAR. This paper extends recent work using neural networks as a compromise between processing complexity, memory usage and accuracy. The already encouraging results are further enhanced by considering different neuron activation functions, architectures, scaling methodologies and training techniques. The errors are given in terms of microns on the detector to facilitate fair comparison between different resolutions and pixel sizes. |
en_US |
dc.language.iso |
en |
en_US |
dc.relation.ispartofseries |
Workflow request;7182 |
|
dc.subject |
Neural networks |
en_US |
dc.subject |
Lens distortion |
en_US |
dc.subject |
Inverse distortion corrections |
en_US |
dc.subject |
Radar |
en_US |
dc.title |
Improved neural network modeling of inverse lens distortion |
en_US |
dc.type |
Conference Presentation |
en_US |
dc.identifier.apacitation |
De Villiers, J., Cronje, J., & Nicolls, F. (2011). Improved neural network modeling of inverse lens distortion. http://hdl.handle.net/10204/5488 |
en_ZA |
dc.identifier.chicagocitation |
De Villiers, JP, J Cronje, and FC Nicolls. "Improved neural network modeling of inverse lens distortion." (2011): http://hdl.handle.net/10204/5488 |
en_ZA |
dc.identifier.vancouvercitation |
De Villiers J, Cronje J, Nicolls F, Improved neural network modeling of inverse lens distortion; 2011. http://hdl.handle.net/10204/5488 . |
en_ZA |
dc.identifier.ris |
TY - Conference Presentation
AU - De Villiers, JP
AU - Cronje, J
AU - Nicolls, FC
AB - Inverse lens distortion modelling allows one to find the pixel in a distorted image which corresponds to a known point in object space, such as may be produced by a RADAR. This paper extends recent work using neural networks as a compromise between processing complexity, memory usage and accuracy. The already encouraging results are further enhanced by considering different neuron activation functions, architectures, scaling methodologies and training techniques. The errors are given in terms of microns on the detector to facilitate fair comparison between different resolutions and pixel sizes.
DA - 2011-04
DB - ResearchSpace
DP - CSIR
KW - Neural networks
KW - Lens distortion
KW - Inverse distortion corrections
KW - Radar
LK - https://researchspace.csir.co.za
PY - 2011
T1 - Improved neural network modeling of inverse lens distortion
TI - Improved neural network modeling of inverse lens distortion
UR - http://hdl.handle.net/10204/5488
ER -
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en_ZA |