25 October 1993 Three-dimensional pattern recognition using an optoelectronic inner product complex neural network
Author Affiliations +
Proceedings Volume 1959, Optical Pattern Recognition IV; (1993); doi: 10.1117/12.160315
Event: Optical Engineering and Photonics in Aerospace Sensing, 1993, Orlando, FL, United States
A complex associative memory model based on a neural network architecture is proposed for recognizing three-dimensional objects acquired from a dynamic environment. The storage representation of the complex associative memory model is based on an efficient amplitude modulated phase-only matched filter. The input to the memory is derived from the discrete Fourier transform of the edge coordinates of the to-be-recognized moving object, where the edges are obtained through motion-based segmentation of the image scene. An adaptive threshold is used during the decision making process to indicate a match or identify a mismatch. Computer simulation on real world data proves the effectiveness of the proposed model. The proposed scheme is readily amenable to opto-electronic implementation.
© (1993) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Abdul Ahad Sami Awwal, Gregory J. Power, "Three-dimensional pattern recognition using an optoelectronic inner product complex neural network", Proc. SPIE 1959, Optical Pattern Recognition IV, (25 October 1993); doi: 10.1117/12.160315; https://doi.org/10.1117/12.160315

Content addressable memory

Image segmentation

Optical pattern recognition

Neural networks

3D modeling

Fourier transforms

Pattern recognition

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