Robust and fast Hausdorff distance for image matching

Hu Zhu; Tianxu Zhang; Luxin Yan; Lizhen Deng

doi:10.1117/1.OE.51.1.017203

31 January 2012 Robust and fast Hausdorff distance for image matching

Hu Zhu, Tianxu Zhang, Luxin Yan, Lizhen Deng

Author Affiliations +

Optical Engineering, Vol. 51, Issue 1, 017203 (January 2012). https://doi.org/10.1117/1.OE.51.1.017203

Abstract

A robust and fast Hausdorff distance (HD) method is presented for image matching. Canny edge operator is used for extracting edge points. HD measure is one of efficient measures for comparing two edge images by calculating the interpixel distance between two sets of edge points, and does not require the point-to-point correspondence. However, high computational complexity is a common problem for HD measure because a large number of edge points could be extracted used to calculate HD. Further, a great many incorrect edge points will be extracted under the condition of occlusion and other ill conditions. A gradient orientation selectivity strategy is proposed to not only select available edges, but also reduce the number of edge points. Experimental results show that the proposed method has less computational cost, and has good robustness for object matching, especially under partial occlusion and other ill conditions.

Citation Download Citation

Hu Zhu, Tianxu Zhang, Luxin Yan, and Lizhen Deng "Robust and fast Hausdorff distance for image matching," Optical Engineering 51(1), 017203 (31 January 2012). https://doi.org/10.1117/1.OE.51.1.017203

Published: 31 January 2012

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