Probabilistic approach to 3D inference of geons from a 2D view

Alain Jacot-Descombes; Thierry Pun

doi:10.1117/12.58603

1 March 1992 Probabilistic approach to 3-D inference of geons from a 2-D view

Alain Jacot-Descombes, Thierry Pun

Proceedings Volume 1708, Applications of Artificial Intelligence X: Machine Vision and Robotics; (1992) https://doi.org/10.1117/12.58603
Event: Aerospace Sensing, 1992, Orlando, FL, United States

Abstract

A new, probabilistic approach for inferring 3-D volumetric primitives from a single 2-D view is presented. This recognition relies on the assumption that every object can be decomposed into component parts that belong to a finite set or alphabet of volumetric primitives (geons). For each possible primitive from the permissible set, a conditional probability function is computed. This law specifies the probability of obtaining the primitive given an observable 2- D measure or feature. The distribution functions are determined by simulation, on the basis of a representative number of random projections of the primitives. The measures themselves are chosen in such a way that they can easily be extracted from real images and their discriminative power for the volumetric primitive inference is high. Examples illustrate the proposed approach.

Citation Download Citation

Alain Jacot-Descombes and Thierry Pun "Probabilistic approach to 3-D inference of geons from a 2-D view", Proc. SPIE 1708, Applications of Artificial Intelligence X: Machine Vision and Robotics, (1 March 1992); https://doi.org/10.1117/12.58603

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