CNN-based local motion estimation for image stabilization processing and its implementation

Chin-Teng Lin, Shi-An Chen, Ying-Chang Cheng, Chao Ting Hong

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The objective of this paper is to investigate a novel design for local motion vectors (LMVs) of image sequences, which are often used in a digital image stabilization (IS) system. The IS technique removes unwanted shaking phenomenon in image sequences captured by hand-held camcorders. It includes two main parts such as motion estimation and compensation. Most of computation power occurs in the part of motion estimation. In order to reduce this complexity, an idea, which integrates an adaptive-threshold method and cellular neural networks (CNN) architecture, is designed to improve this problem. The design only implements the most important local motion estimation with the array size of 19x25 pixels. Experimental results with HSPICE simulation and CNNUM are shown that the proposed architecture fast searches the location of possible LVMs and has the capability of real-time operations.
Original languageEnglish
Title of host publication2006 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS, VOLS 1-6, PROCEEDINGS
PublisherIEEE
Pages1816
ISBN (Print)978-1-4244-0099-7
DOIs
StatePublished - 2006
Event2006 IEEE International Conference on Systems, Man and Cybernetics - Taipei, Taiwan
Duration: 8 Oct 200611 Oct 2006

Publication series

NameIEEE International Conference on Systems Man and Cybernetics Conference
PublisherIEEE
ISSN (Print)1062-922X

Conference

Conference2006 IEEE International Conference on Systems, Man and Cybernetics
CountryTaiwan
CityTaipei
Period8/10/0611/10/06

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  • Cite this

    Lin, C-T., Chen, S-A., Cheng, Y-C., & Hong, C. T. (2006). CNN-based local motion estimation for image stabilization processing and its implementation. In 2006 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS, VOLS 1-6, PROCEEDINGS (pp. 1816). (IEEE International Conference on Systems Man and Cybernetics Conference). IEEE. https://doi.org/10.1109/ICSMC.2006.384993