Title :
The implementation of a nonlinear wave metric for image analysis and classification on the 64×64 I/O CNN-UM chip
Author :
Szátmari, István
Author_Institution :
Analogical & Neural Comput. Lab., Hungarian Acad. of Sci., Budapest, Hungary
Abstract :
In this paper the implementation of a nonlinear wave metric on the 64×64 I/O CNN-UM chip and its experimental results are presented. The nonlinear wave metric was designed and introduced as a generalized theorem for object analysis and classification. This proposed metric includes the well-known distance measures such as Hamming, Hausdorff metrics as special cases. The defined computational method is well-suited for cellular neural network (CNN) architecture and the experimental results shows good correlation with theoretic considerations
Keywords :
cellular neural nets; digital signal processing chips; image classification; neural chips; 64×64 I/O CNN-UM chip; Hamming metric; Hausdorff metric; cellular neural network architecture; distance measures; image analysis; image classification; nonlinear wave metric; object analysis; object classification; Automation; Cellular networks; Cellular neural networks; Computer architecture; Computer networks; Hamming distance; Image analysis; Image recognition; Laboratories; Semiconductor device measurement;
Conference_Titel :
Cellular Neural Networks and Their Applications, 2000. (CNNA 2000). Proceedings of the 2000 6th IEEE International Workshop on
Conference_Location :
Catania
Print_ISBN :
0-7803-6344-2
DOI :
10.1109/CNNA.2000.877361
