Object Recognition within Cluttered Scenes Employing a Hybrid Optical Neural Network Filter

Kypraios, Ioannis I, Young, Rupert, Birch, Philip and Chatwin, Chris (2004) Object Recognition within Cluttered Scenes Employing a Hybrid Optical Neural Network Filter. Optical Engineering, 43 (8). pp. 1839-1850. ISSN 0091-3286

Full text not available from this repository.

Abstract

We propose a hybrid filter, which we call the hybrid optical neural network (HONN) filter. This filter combines the optical implementation and shift invariance of correlator-type filters with the nonlinear superposition capabilities of artificial neural network methods. The filter demonstrates good performance in maintaining high-quality correlation responses and resistance to clutter to nontraining in-class images at orientations intermediate to the training set poses. We present the design and implementation of the HONN filter architecture and assess its object recognition performance in clutter.

Item Type: Article
Additional Information: The article details a non-linear extension to existing linear filters for explicit optical implementation within our coherent optical pattern recognition hardware. The research demonstrated improved intra-class tolerance of the non-linear filters, as compared to the linear counterparts, whilst maintaining interclass discrimination and tolerance to heavy background clutter. The non-linear filters developed in this research have been incorporated into our hybrid digital/optical correlator hardware architecture and in this have demonstrated improved performance over existing linear filters. The NASA Jet Propulsion Laboratory, Pasadena, California Institute of Technology, is interested in our correlator hardware and filter research (contact: Tien-Hsin.Chao@jpl.nasa.gov,Tel: 001-818-354-8614)
Schools and Departments: School of Engineering and Informatics > Engineering and Design
Depositing User: Rupert Young
Date Deposited: 06 Feb 2012 20:08
Last Modified: 30 Mar 2012 14:25
URI: http://sro.sussex.ac.uk/id/eprint/24272
📧 Request an update