Báo cáo hóa học: "Design and implementation of a real time and train less eye state recognition system"

Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Design and implementation of a real time and train less eye state recognition system | EURASIP Journal on Advances in Signal Processing SpringerOpen0 This Provisional PDF corresponds to the article as it appeared upon acceptance. Fully formatted PDF and full text HTML versions will be made available soon. Design and implementation of a real time and train less eye state recognition system EURASIP Journal on Advances in Signal Processing 2012 2012 30 doi 1687-6180-2012-30 Mohammad Dehnavi Mohammad Eshghi m-eshghi@ ISSN 1687-6180 Article type Research Submission date 22 May 2011 Acceptance date 15 February 2012 Publication date 15 February 2012 Article URL http content 2012 1 30 This peer-reviewed article was published immediately upon acceptance. It can be downloaded printed and distributed freely for any purposes see copyright notice below . For information about publishing your research in EURASIP Journal on Advances in Signal Processing go to http authors instructions For information about other SpringerOpen publications go to http 2012 Dehnavi and Eshghi licensee Springer. This is an open access article distributed under the terms of the Creative Commons Attribution License http licenses by which permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited. Design and implementation of a real time and train less eye state recognition system Mohammad Dehnavi 1 and Mohammad Eshghi1 1ECE Department Shahid Beheshti University Tehran Iran Corresponding author @ Email address MD ME m-eshghi@ Abstract Eye state recognition is one of the main stages of many image processing systems such as driver drowsiness detection system and closed-eye photo correction. Driver drowsiness is one of the main causes in the road accidents around the world. In these circumstances a fast and accurate driver drowsiness detection .

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