Sunday, 27 June 2010

Wseas Transactions

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Transactions: WSEAS TRANSACTIONS ON COMPUTERS
Transactions ID Number: 88-123
Full Name: Seoksoo Kim
Position: Professor
Age: ON
Sex: Male
Address: 133 Ojeong-dong, Daedeok-gu, Daejeon-city
Country: KOREA
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E-mail address: sskim0123@naver.com
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Title of the Paper: A Study on Multiple Objects Detection, Loading and Control in Video for Augmented Reality
Authors as they appear in the Paper: Sungmo Jung, Jae-gu Song, Seoksoo Kim
Email addresses of all the authors: sungmoj@gmail.com, bhas9@paran.com, sskim0123@naver.com
Number of paper pages: 11
Abstract: Since the researches on augmented reality have recently received attention, M2M market started to be activated and there are numerous efforts to apply this to the real life in all sectors of society. However, with the existing marker-based augmented reality technology, a designated object only can be loaded from one marker and one marker has to be added to load the same object additionally. To solve this problem, the relevant marker should be extracted and printed in screen so that loading of the multiple objects is enabled. However, since the distance between markers will not be measured in the process of detecting and copying markers, the markers can be overlapped and thus the objects would not be augmented. To solve this problem, a circle having the longest radius needs to be created from a focal point of a marker to be copied, so that no object is copied within the confines of the circle. Therefore, in this paper, multiple objects detection and loading using PP!
HT has been developed. And markers overlapping control according to multiple objects control has been studied using Bresenham and Mean Shift algorithm.
Keywords: Multiple Objects Detection, Multiple Objects Loading, Multiple Objects Control, Computer Vision, PPHT, Bresenham Algorithm, Mean Shift Algorithm, Augmented Reality
EXTENSION of the file: .pdf
Special (Invited) Session: New Aspects of Automatic Control, Modelling & Simulation
Organizer of the Session: 645-443
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IP ADDRESS: 203.247.63.125