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

Cristian Grava, Alexandru Gacsádi, Ioan Buciu


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.


cellular neural networks, motion estimation, Horn & Schunck method

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DOI: https://doi.org/10.15837/ijccc.2010.5.2231

Copyright (c) 2017 Cristian Grava, Alexandru Gacsádi, Ioan Buciu

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