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dc.contributor.authorLin, Chin-Tengen_US
dc.contributor.authorChen, Shi-Anen_US
dc.contributor.authorCheng, Ying-Changen_US
dc.contributor.authorChung, Jen-Fengen_US
dc.date.accessioned2014-12-08T15:24:49Z-
dc.date.available2014-12-08T15:24:49Z-
dc.date.issued2006en_US
dc.identifier.isbn978-0-7803-9389-9en_US
dc.identifier.issn0271-4302en_US
dc.identifier.urihttp://hdl.handle.net/11536/17266-
dc.description.abstractThis 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.en_US
dc.language.isoen_USen_US
dc.titleCNN-based local motion estimation chip for image stabilization processingen_US
dc.typeProceedings Paperen_US
dc.identifier.journal2006 IEEE INTERNATIONAL SYMPOSIUM ON CIRCUITS AND SYSTEMS, VOLS 1-11, PROCEEDINGSen_US
dc.citation.spage2645en_US
dc.citation.epage2648en_US
dc.contributor.department電控工程研究所zh_TW
dc.contributor.departmentInstitute of Electrical and Control Engineeringen_US
dc.identifier.wosnumberWOS:000245413503007-
Appears in Collections:Conferences Paper