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Proceedings Paper

Logarithmic r-theta mapping for hybrid optical neural network filter for multiple objects recognition within cluttered scenes
Author(s): Ioannis Kypraios; Rupert C. D. Young; Chris R. Chatwin; Phil M. Birch
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Paper Abstract

θThe window unit in the design of the complex logarithmic r-θ mapping for hybrid optical neural network filter can allow multiple objects of the same class to be detected within the input image. Additionally, the architecture of the neural network unit of the complex logarithmic r-θ mapping for hybrid optical neural network filter becomes attractive for accommodating the recognition of multiple objects of different classes within the input image by modifying the output layer of the unit. We test the overall filter for multiple objects of the same and of different classes' recognition within cluttered input images and video sequences of cluttered scenes. Logarithmic r-θ mapping for hybrid optical neural network filter is shown to exhibit with a single pass over the input data simultaneously in-plane rotation, out-of-plane rotation, scale, log r-θ map translation and shift invariance, and good clutter tolerance by recognizing correctly the different objects within the cluttered scenes. We record in our results additional extracted information from the cluttered scenes about the objects' relative position, scale and in-plane rotation.

Paper Details

Date Published: 13 April 2009
PDF: 15 pages
Proc. SPIE 7340, Optical Pattern Recognition XX, 73400N (13 April 2009); doi: 10.1117/12.818789
Show Author Affiliations
Ioannis Kypraios, Univ. of Sussex (United Kingdom)
Rupert C. D. Young, Univ. of Sussex (United Kingdom)
Chris R. Chatwin, Univ. of Sussex (United Kingdom)
Phil M. Birch, Univ. of Sussex (United Kingdom)


Published in SPIE Proceedings Vol. 7340:
Optical Pattern Recognition XX
David P. Casasent; Tien-Hsin Chao, Editor(s)

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