Segmentation improvement through a robust segment grouping-and-matching algorithm in stereovision

Max Buvry; Charlie J. Krey

doi:10.1117/12.177529

1 July 1994 Segmentation improvement through a robust segment grouping-and-matching algorithm in stereovision

Max Buvry, Charlie J. Krey

Author Affiliations +

Journal of Electronic Imaging, Vol. 3, Issue 3, (July 1994). https://doi.org/10.1117/12.177529

Abstract

An original algorithm is presented that matches the segments of a pair of stereoscopic images and then improves the edge segmentation of images to make the interpretation process easier. The method does not consider each segment independently to perform the matching, rather it groups and matches the segments simultaneously. First, the linked segments are grouped and matched. Then, the same process is repeated while considering the segments more and more distant. At each step, different criteria such as segment connection type and relative position of the segments are used to select the correct matchings among ail the matching hypotheses. The segmentation improvement takes into account the geometric and topologic properties of objects-the computed sets of segments are considered the projection of the faces of these objects. The segmentation improvement method relies on the comparison of the matched sets obtained previously.

Citation Download Citation

Max Buvry and Charlie J. Krey "Segmentation improvement through a robust segment grouping-and-matching algorithm in stereovision," Journal of Electronic Imaging 3(3), (1 July 1994). https://doi.org/10.1117/12.177529

Published: 1 July 1994

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