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Article

Obtain the depth of field based on two-view

Published: 06 March 2010 Publication History

Abstract

this paper attempts to propose a method that uses images taken in different locations by a single camera or from a video stream interception to restore depth information of the scene. At first, the SIFT algorithm is used to process two images, to find the feature points and match them into pairs, then these pairs are used to calculate the corresponding fundamental matrix. From the fundamental matrix the epipolar can be got. The feature points are matched with the epipolar constraint to improve accuracy. With these matched points, the binocular stereo vision method is used to find the depth information. For a visual representation, the coordinates of the points are input to Matlab to show the result. Experiments show that the method can get information of depth of the field without a known position relationship between images.

References

[1]
Lowe D G. Distinctive image features from scale- invariant keypoints {J}. International Journal of Computer Vision, 2004, 60(2): 91-110.
[2]
Z Zhang A flexible new technique for camera calibration 2000 (11)
[3]
Qiang Tong Research on Algorithms for Images Matching Based on Two Views 2006
[4]
YUAN Si-cong, LIU Jin-song, ZHANG Qing-yang, et al. Research on image matching method in binocular stereo vision Computer Engineering and Applications, 2008, 44(8): 75-77.
[5]
Carsten Steger, Markus Ulrich, Christian Wiedemann Machine Vision Algorithms and Applications 2007
[6]
Zhi-gang Zheng Study on High-precision Camera Calibration and Robust Stereo Matching 2008

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        Published In

        cover image Guide Proceedings
        CAR'10: Proceedings of the 2nd international Asia conference on Informatics in control, automation and robotics - Volume 1
        March 2010
        519 pages
        ISBN:9781424451920

        Publisher

        IEEE Press

        Publication History

        Published: 06 March 2010

        Author Tags

        1. SIFT
        2. binocular stereo vision
        3. fundamental matrix
        4. image matching

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