Automatic tuning for the segmentation of infrared images considering uncertain ground truth

Rubén Usamentiaga; Daniel Fernando Garcia; Julio Molleda

doi:10.1117/1.3059579

1 January 2009 Automatic tuning for the segmentation of infrared images considering uncertain ground truth

Rubén Usamentiaga, Daniel Fernando Garcia, Julio Molleda

Author Affiliations +

Journal of Electronic Imaging, Vol. 18, Issue 1, 013001 (January 2009). https://doi.org/10.1117/1.3059579

Abstract

Image segmentation is one of the most important tasks of image processing, as it provides information used to interpret and analyze image contents. The tuning of the parameters of the segmentation method can be considered an optimization problem by defining an objective function based on the similarity of the segmented image and the ground truth. The problem becomes harder to solve when the ground truth is known only under uncertainty. A solution is proposed for the design and the automatic tuning of a real-time segmentation method for infrared images where the ground truth is uncertain. The proposed solution consists of three steps: the proposal of a segmentation method adapted for the considered images, the definition of an objective function that takes the uncertainty of the ground truth into account, and the automatic tuning of the segmentation method by means of genetic algorithms.

©(2009) Society of Photo-Optical Instrumentation Engineers (SPIE)

Citation Download Citation

Rubén Usamentiaga, Daniel Fernando Garcia, and Julio Molleda "Automatic tuning for the segmentation of infrared images considering uncertain ground truth," Journal of Electronic Imaging 18(1), 013001 (1 January 2009). https://doi.org/10.1117/1.3059579

Published: 1 January 2009

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