Continuous quantification of uniqueness and stereoscopic vision

Val Petran; Frank Merat

doi:10.1117/12.883620

3 June 2011 Continuous quantification of uniqueness and stereoscopic vision

Val Petran, Frank Merat

Proceedings Volume 8056, Visual Information Processing XX; 80560E (2011) https://doi.org/10.1117/12.883620
Event: SPIE Defense, Security, and Sensing, 2011, Orlando, Florida, United States

Abstract

In this paper we introduce the concept of continuous quantification of uniqueness. Our approach is to construct an algorithm that computes a fuzzy set membership function, which given any inter-object dissimilarity metric and it's variability, measures the probability that an entity of interest will not be confused with other similar entities in a search space. We demonstrate use of this algorithm by applying it to stereoscopic computer vision, in order to identify which of several sub-problems pertaining to solution of the classic stereoscopic correspondence problem are least likely to be solved incorrectly, and hence are most well suited to greatest confidence first approaches.

Citation Download Citation

Val Petran and Frank Merat "Continuous quantification of uniqueness and stereoscopic vision", Proc. SPIE 8056, Visual Information Processing XX, 80560E (3 June 2011); https://doi.org/10.1117/12.883620

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