Parallel implementation of elastic grid matching using cellular neural networks

  • Authors:

  • Krzysztof Slot;Piotr Korbel;Hyongsuk Kim;Malrey Lee;Suhong Ko

  • Affiliations:

  • Institute of Electronics, Technical University of Lodz, Lodz and Academy of Humanities and Economics, Lodz, Poland;Institute of Electronics, Technical University of Lodz, Poland;Division of Electronics and Information Engineering, Chonbuk National University, Chonju, Republic of Korea;Research Center of Industrial Technology, School of Electronics and Information Engineering, Chonbuk National University, Republic of Korea;Division of Electronics and Information Engineering, Chonbuk National University, Chonju, Republic of Korea

  • Venue:
  • MIRAGE'07 Proceedings of the 3rd international conference on Computer vision/computer graphics collaboration techniques
  • Year:
  • 2007

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Abstract

The following paper presents a method that allows for a parallel implementation of the most computationally expensive element of the deformable template paradigm, which is a grid-matching procedure. Cellular Neural Network Universal Machine has been selected as a framework for the task realization. A basic idea of deformable grid matching is to guide node location updates in a way that minimizes dissimilarity between an image and grid-recorded information, and that ensures minimum grid deformations. The proposed method provides a parallel implementation of this general concept and includes a novel approach to grid's elasticity modeling. The method has been experimentally verified using two different analog hardware environments, yielding high execution speeds and satisfactory processing accuracy.