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Author Hyeongyong Jeon ♦ Jaekyong Jeong ♦ Joonwoon Bang ♦ Chijung Hwang
Source IEEE Xplore Digital Library
Content type Text
Publisher Institute of Electrical and Electronics Engineers, Inc. (IEEE)
File Format PDF
Copyright Year ©2008
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Special computer methods
Subject Keyword Karhunen-Loeve transforms ♦ Linear systems ♦ Optical distortion ♦ Layout ♦ Artificial intelligence ♦ Computer vision ♦ Optical computing ♦ Image motion analysis ♦ Newton method ♦ Parameter estimation ♦ RST ♦ Optical flow ♦ object tracking ♦ KLT
Abstract In computer vision, optical flow is very useful in tracking objects from frame to frame. The well-known KLT (Kanade-Lucas-Tomasi) of the differential method can trace local features through a generalized affine transform explained by six parameters. Since most of the distortion of the local patch between frames can be explained by four factors (horizontal and vertical translation, scaling and rotation), we present a tracking method with four parameters in this paper. The advantages of our method are that it has lower complexity than KLT and calculates the values of the four parameters directly. In this experiment, we show the four estimated factors in a virtual scene and track local features in a real scene. As a result, despite reducing the parameters, the proposed method performs very well for feature tracking and can track local features using only four parameters.
Description Author affiliation: Comput. Eng. Dept., Chungnam Nat. Univ., Daejeon (Hyeongyong Jeon; Jaekyong Jeong; Joonwoon Bang; Chijung Hwang)
ISBN 9780769534404
ISSN 10823409
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research ♦ Reading
Education Level UG and PG
Learning Resource Type Article
Publisher Date 2008-11-03
Publisher Place USA
Rights Holder Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Size (in Bytes) 571.33 kB
Page Count 4
Starting Page 241
Ending Page 244

Source: IEEE Xplore Digital Library