A Neural Computation Model Based on nCRF and Reverse Control Mechanism

  • Hui Wei
  • Xiao-Mei Wang
Part of the Studies in Computational Intelligence book series (SCI, volume 363)


Previous nCRF models are mainly based on fixed RF whose dynamic characteristics are not taken into account. In this paper, we establish a multilayer neural computation model with feedback for the basic structure of nCRF. In our model, GC’s RF can dynamically and self-adaptively adjust its size according to stimulus properties. RF becomes smaller in local areas where the image details need distinguishing and larger where the image information have no obvious difference. The experimental results fully reflect the dynamic characteristics of GC’s RF. Among adjacent areas in an image, similar ones are integrated together and represented by a larger RF, while dissimilar ones are separated and represented by several smaller RFs. Such a biology-inspired neural computation model is a reliable approach for image segmentation and clustering integration.


Non-classical receptive field Neural network Image representation 


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© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Hui Wei
    • 1
  • Xiao-Mei Wang
    • 1
  1. 1.Department of Computer Science, Laboratory of Cognitive Model and Algorithm, Brain Science Research CenterFudan UniversityChina

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