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Feature matching in stereo images encouraging uniform spatial distribution
The University of New South Wales, Canberra, ACT 2600, Australia.
CSIRO Computational Informatics, Locked Bag 17, North Ryde, NSW 1670, Australia.
CSIRO Agriculture Flagship, Clunies Ross Street, Canberra, ACT 2601, Australia.
CSIRO Agriculture Flagship, Clunies Ross Street, Canberra, ACT 2601, Australia.
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2015 (English)In: Pattern Recognition, ISSN 0031-3203, E-ISSN 1873-5142, Vol. 48, no 8, 2530-2542 p.Article in journal (Refereed) PublishedText
Abstract [en]

Finding feature correspondences between a pair of stereo images is a key step in computer vision for 3D reconstruction and object recognition. In practice, a larger number of correct correspondences and a higher percentage of correct matches are beneficial. Previous researches show that the spatial distribution of correspondences are also very important especially for fundamental matrix estimation. So far, no existing feature matching method considers the spatial distribution of correspondences. In our research, we develop a new algorithm to find good correspondences in all the three aspects mentioned, i.e., larger number of correspondences, higher percentage of correct correspondences, and better spatial distribution of correspondences. Our method consists of two processes: an adaptive disparity smoothing filter to remove false correspondences based on the disparities of neighboring correspondences and a matching exploration algorithm to find more correspondences according to the spatial distribution of correspondences so that the correspondences are as uniformly distributed as possible in the images. To find correspondences correctly and efficiently, we incorporate the cheirality constraint under an epipole polar transformation together with the epipolar constraint to predict the potential location of matching point. Experiments demonstrate that our method performs very well on both the number of correct correspondences and the percentage of correct correspondences; and the obtained correspondences are also well distributed over the image space.

Place, publisher, year, edition, pages
2015. Vol. 48, no 8, 2530-2542 p.
Keyword [en]
3D/stereo scene analysis; Stereo image processing; Feature correspondences
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
URN: urn:nbn:se:liu:diva-128585DOI: 10.1016/j.patcog.2015.02.026OAI: oai:DiVA.org:liu-128585DiVA: diva2:932934
Available from: 2016-06-02 Created: 2016-05-25 Last updated: 2016-06-21

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Pham, Tuan D
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Pattern Recognition
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