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Please use this identifier to cite or link to this item: http://hdl.handle.net/10204/5488

Title: Improved neural network modeling of inverse lens distortion
Authors: De Villiers, JP
Cronje, J
Nicolls, FC
Keywords: Neural networks
Lens distortion
Inverse distortion corrections
Radar
Issue Date: Apr-2011
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
Series/Report no.: Workflow request;7182
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.
Description: Defense, Security, and Sensing (DSS11), Orlando World Center Marriott Resort & Convention Centre, Orlando, Florida, USA, 26-28 April 2011
URI: http://hdl.handle.net/10204/5488
Appears in Collections:Optronic sensor systems
Advanced mathematical modelling and simulation
Digital intelligence
General science, engineering & technology

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