Enhancement of Perceptually Salient Contours using a Parallel Artificial Cortical Network

  • Authors:

  • Vassilios Vonikakis;Antonios Gasteratos;Ioannis Andreadis

  • Affiliations:

  • Democritus University of Thrace, Laboratory of Electronics, Section of Electronics and Information Systems Technology, Department of Electrical and Computer Engineering, 671 00, Xanthi, Greece;Democritus University of Thrace, Laboratory of Robotics and Automation, Section of Production Systems, Department of Production and Management Engineering, 671 00, Xanthi, Greece;Democritus University of Thrace, Laboratory of Electronics, Section of Electronics and Information Systems Technology, Department of Electrical and Computer Engineering, 671 00, Xanthi, Greece

  • Venue:
  • Biological Cybernetics
  • Year:
  • 2006

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Abstract

In this paper we present a parallel artificial cortical network inspired by the Human visual system, which enhances the salient contours of an image. The network consists of independent processing elements, which are organized into hypercolumns. They process concurrently the distinct orientations of all the edges of the image. These processing elements are a new set of orientation kernels appropriate for the discrete lattice of the hypercolumns. The Gestalt laws of proximity and continuity that describe the process of saliency extraction in the human brain are encoded by means of weights. These weights interconnect the kernels according to a novel connection pattern based on co-exponentiality. The output of every kernel is modulated by the outputs of its neighboring kernels, according to a new affinity function. This function takes into account the degree of difference between the facilitation of the two lobes of the kernel. Saliency enhancement results as a consequence of the local interactions between the kernels. The network was tested on real and synthetic images and displays promising results for both. Comparisons with other methods with the same scope, demonstrate that the proposed method performs adequately. Furthermore it exhibits O(N) complexity with execution times that have never been reported by any other method so far, even though it is executed on a conventional PC