A Nonlinear Wave Metric and its CNN Implementation for Object Classification

Authors:
Istvá/n Szatmá/ri;Csaba Rekeczky;Tamá/s Roska
Affiliations:
Analogical and Neural Computing Laboratory, Computer and Automation Institute, Hungarian Academy of Sciences, Kende u. 13-17, H-1111 Budapest, Hungary;Analogical and Neural Computing Laboratory, Computer and Automation Institute, Hungarian Academy of Sciences, Kende u. 13-17, H-1111 Budapest, Hungary;Analogical and Neural Computing Laboratory, Computer and Automation Institute, Hungarian Academy of Sciences, Kende u. 13-17, H-1111 Budapest, Hungary&semi/ Electronics Research Laboratory, Coll ...
Venue:
Journal of VLSI Signal Processing Systems - Special issue on spatiotemporal signal processing with analog CNN visual microprocessors
Year:
1999

Citing 2
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Abstract

In this paper a nonlinear wave metric is introduced for object classification. It is shown that the choice of a metric is a nontrivial problem since it is easy to give examples when well-known distance measures, such as Hamming, Hausdorff, and Nonlinear Hausdorff metrics are completely inadequate for this classification. As an alternative a generalized theorem is proposed that includes the previous metrics as special cases. It is based on nonlinear wave propagation and defines a computational framework that is well-suited for parallel array processors. In this study we investigate different Cellular Neural Network (CNN) architectures and solutions for the proposed metric and analyze its VLSI implementation complexity.