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

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...

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Bibliographic Details
Published in:International journal of computers, communications & control communications & control, 2010-12, Vol.5 (5), p.719
Main Authors: Grava, Cristian, Gacsádi, Alexandru, Buciu, Ioan
Format: Article
Language:English
Subjects:
Algorithms
Artificial neural networks
Artificial vision
Cellular communication
Compensation
Field programmable gate arrays
Image processing
Medical imaging
Motion simulation
Neural networks
Real time
Online Access:Get full text
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Summary: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.
ISSN:1841-9836
1841-9844
DOI:10.15837/ijccc.2010.5.2231