Characterization of the recognition and the identification capabilities of the statistical snake at low resolution and high noise levels in speckled images

Olivier Ruch; Philippe Refregier

doi:10.1117/12.421135

20 March 2001 Characterization of the recognition and the identification capabilities of the statistical snake at low resolution and high noise levels in speckled images

Olivier Ruch, Philippe Refregier

Author Affiliations +

Proceedings Volume 4387, Optical Pattern Recognition XII; (2001) https://doi.org/10.1117/12.421135
Event: Aerospace/Defense Sensing, Simulation, and Controls, 2001, Orlando, FL, United States

Abstract

Target recognition is an important task for many automatic systems based on imagery. The recent technique of active contours (snakes) is well adapted to the segmentation step when the recognition is made from the shape of the target. Classical segmentation strategies are generally edge-based in the sense that the segmentation is driven from an edge map of the scene. Consequently, these methods which are efficient with a certain class of problem could fail in presence of strong noise. We have recently proposed an original approach for the statistical segmentation of an object (statistical snake) for which the image is assumed to be made of two regions (the object and the background) composed of homogeneous intensity random fields. In this article, we characterize the quality of the segmentation as a function of the target resolution and noise level with two similarity measurements based on Hausdorff distance between the exact contour and the result of the segmentation.

Citation Download Citation

Olivier Ruch and Philippe Refregier "Characterization of the recognition and the identification capabilities of the statistical snake at low resolution and high noise levels in speckled images", Proc. SPIE 4387, Optical Pattern Recognition XII, (20 March 2001); https://doi.org/10.1117/12.421135

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