Sub-pattern texture recognition using intelligent focal-plane imaging sensor of small window-size

Tamás Szirányi, Attila Hanis

Research output: Contribution to journalConference article

4 Citations (Scopus)

Abstract

In this paper we demonstrate how to use statistical evaluation for texture recognition in the case of window-size of the imaging focal-plane sensor being smaller than the pattern of the texture. The evaluation method is similar to the sub-pixel pattern recognition developed by the first author. We have reported in an earlier publication on the development of a new single-chip texture classifier smart-sensor system, whose main part is a cellular nonlinear network (CNN) VLSI chip. This architecture is very fast but it has a limited window-size. Now we show that this architecture can effectively recognize textures of periodicity larger than the window-size. As a result, we recognized 15 Brodatz-textures by using a 20×22 CNN chip with a 0.4% error-rate.

Original languageEnglish
Pages (from-to)1133-1139
Number of pages7
JournalPattern Recognition Letters
Volume20
Issue number11-13
DOIs
Publication statusPublished - Nov 1999
EventProceedings of the 1999 Pattern Recognition in Practice (PRP VI) - Vlieland, Neth
Duration: Jun 2 1999Jun 4 1999

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ASJC Scopus subject areas

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

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