Texture recognition using a superfast cellular neural network VLSI chip in a real experimental environment

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7 Citations (Scopus)

Abstract

We have developed a new single-chip texture classifier smart-sensor system. Its main part is a programmable cellular neural network (CNN) VLSI chip with optical input and an execution time of a few microseconds. This chip contains a dynamic and locally interconnected 2D cell array. It executes a theoretically new method for texture classification, compared to the other convolution-based feature extraction methods, since here we have feedback convolution as well. Depending on the kernel parameters, this array can execute filtering, moving, linear and nonlinear effects at the same time. The parameters of the feedback and feed-forward convolutions are optimized through a genetic learning using the 22 * 20 CNN chip itself. This chip has a simplified architecture with binary input/output, but it gives good recognition results: this CNN chip with a simple 3 * 3 kernel can reliably classify 5 natural textures. Using more templates for decision-making, more textures can be separated and a classification error of less than 1% has been achieved.

Original languageEnglish
Pages (from-to)1329-1334
Number of pages6
JournalPattern Recognition Letters
Volume18
Issue number11-13
DOIs
Publication statusPublished - Nov 1997

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Keywords

  • Cellular neural network
  • Deconvolution
  • Genetic algorithm
  • Smart sensors
  • Texture analysis

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence

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