Hierarchical neural network architectures for vision system

Jin Kun Lin, Gee Gwo Mei, Wentai Liu, Su shing Chen

Research output: Contribution to journalConference articlepeer-review

Abstract

Using hierarchical neural network architectures (HNN), the authors study the implementations of several vision tasks, including contour finding, motion detection, and line detection (Hough transform). Several models are used. They are homogeneous and heterogeneous Boltzmann machines, homogeneous and heterogeneous Hopfield models, homogeneous winner-take-all model, and the models with and without limited displacement. Simulations have shown that the homogeneous system is capable of handling the desired vision tasks and thus simplifies the implementation.

Original languageEnglish
Pages (from-to)511
Number of pages1
JournalNeural Networks
Volume1
Issue number1 SUPPL
DOIs
Publication statusPublished - 1988
EventInternational Neural Network Society 1988 First Annual Meeting - Boston, MA, USA
Duration: 1988 Sept 61988 Sept 10

All Science Journal Classification (ASJC) codes

  • Cognitive Neuroscience
  • Artificial Intelligence

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