A Homogeneous Algorithm for Motion Estimation and Compensation by Using Cellular Neural Networks

  • Cristian Grava University of Oradea Faculty of Electrical Engineering and Information Technology Oradea, Romania
  • Alexandru Gacsádi University of Oradea Faculty of Electrical Engineering and Information Technology Oradea, Romania
  • Ioan Buciu University of Oradea Faculty of Electrical Engineering and Information Technology Oradea, Romania

Abstract

In this paper we present an original implementation of a homogeneous algorithm for motion estimation and compensation in image sequences, by using Cellular Neural Networks (CNN). The CNN has been proven their efficiency in real-time image processing, because they can be implemented on a CNN chip or they can be emulated on Field Programmable Gate Array (FPGA). The motion information is obtained by using a CNN implementation of the well-known Horn & Schunck method. This information is further used in a CNN implementation of a motion-compensation method. Through our algorithm we obtain a homogeneous implementation for real-time applications in artificial vision or medical imaging. The algorithm is illustrated on some classical sequences and the results confirm the validity of our algorithm.

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Published
2010-12-01
How to Cite
GRAVA, Cristian; GACSÁDI, Alexandru; BUCIU, Ioan. A Homogeneous Algorithm for Motion Estimation and Compensation by Using Cellular Neural Networks. INTERNATIONAL JOURNAL OF COMPUTERS COMMUNICATIONS & CONTROL, [S.l.], v. 5, n. 5, p. 719-726, dec. 2010. ISSN 1841-9844. Available at: <http://univagora.ro/jour/index.php/ijccc/article/view/2231>. Date accessed: 30 nov. 2021.

Keywords

cellular neural networks, motion estimation, Horn & Schunck method