CNN-based local motion estimation chip for image stabilization processing

Chin-Teng Lin, Shi-An Chen, Ying-Chang Cheng, Jen-Feng Chung

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

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. The complete design has integrated into the total area of 8.1mm(2) by using TSMC 0.35 mu m mixed-signal process.
Original languageEnglish
Title of host publication2006 IEEE INTERNATIONAL SYMPOSIUM ON CIRCUITS AND SYSTEMS, VOLS 1-11, PROCEEDINGS
PublisherIEEE
Pages2645
ISBN (Print)978-0-7803-9389-9
StatePublished - 2006
EventISCAS 2006: 2006 IEEE International Symposium on Circuits and Systems - Kos, Greece
Duration: 21 May 200624 May 2006

Publication series

NameIEEE International Symposium on Circuits and Systems
PublisherIEEE
ISSN (Print)0271-4302

Conference

ConferenceISCAS 2006: 2006 IEEE International Symposium on Circuits and Systems
CountryGreece
CityKos
Period21/05/0624/05/06

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