Geodesic distance framework for contour-based consistent stereo image segmentation

Xujie Li; Hui Huang; Yandan Wang; Mingxiao Hu

doi:10.1117/1.JEI.27.5.053047

29 October 2018 Geodesic distance framework for contour-based consistent stereo image segmentation

Xujie Li, Hui Huang, Yandan Wang, Mingxiao Hu

Author Affiliations +

Journal of Electronic Imaging, Vol. 27, Issue 5, 053047 (October 2018). https://doi.org/10.1117/1.JEI.27.5.053047

Abstract

This study concentrates on consistent object contour extraction method for stereo image segmentation after the object regions in the left image have been obtained. By taking advantage of the epipolar geometry, our approach introduces an energy optimization framework that incorporates both the stereo correspondence term and patch-based object contour probability term. The contour map is generated by integrating the terms of stereo correspondence and patch-based object contour probability; then, the optimal contours are obtained using geodesic distance technology. The core of the proposed method is to build upon an energy optimization framework with two key contributions: first, it incorporates the patch-based object contour probability term that introduces two search strategies to efficiently find the joint nearest neighbor patch pairs for the stereo image pair. The patch-based object contour probability term provides consistent and reliable priors for the contour extraction. Second, previous methods encounter missing pixels in the extracted contour in the occluded regions. Our approach overcomes this limit by introducing the geodesic distance technology to search the optimal contours. Experimental evaluation on Middlebury dataset and Adobe open dataset indicates that the results of our stereo image segmentation are comparable with or of higher quality than state-of-the-art methods.

Citation Download Citation

Xujie Li, Hui Huang, Yandan Wang, and Mingxiao Hu "Geodesic distance framework for contour-based consistent stereo image segmentation," Journal of Electronic Imaging 27(5), 053047 (29 October 2018). https://doi.org/10.1117/1.JEI.27.5.053047

Received: 13 June 2018; Accepted: 11 October 2018; Published: 29 October 2018

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $24.00

Non-members: $28.00 ADD TO CART

JOURNAL ARTICLE
12 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Image segmentation

Image processing algorithms and systems

Image quality

Video

Image processing

3D displays

Feature extraction

Show All Keywords

Keywords/Phrases

Search In:

Publication Years