Probabilistic view clustering in object recognition

Octavia I. Camps; Douglas W. Christoffel; Anjali Pathak

doi:10.1117/12.131657

1 November 1992 Probabilistic view clustering in object recognition

Octavia I. Camps, Douglas W. Christoffel, Anjali Pathak

Proceedings Volume 1828, Sensor Fusion V; (1992) https://doi.org/10.1117/12.131657
Event: Applications in Optical Science and Engineering, 1992, Boston, MA, United States

Abstract

To recognize objects and to determine their poses in a scene we need to find correspondences between the features extracted from the image and those of the object models. Models are commonly represented by describing a few characteristic views of the object representing groups of views with similar properties. Most feature-based matching schemes assume that all the features that are potentially visible in a view will appear with equal probability, and the resulting matching algorithms have to allow for 'errors' without really understanding what they mean. PREMIO is an object recognition system that uses CAD models of 3D objects and knowledge of surface reflectance properties, light sources, sensor characteristics, and feature detector algorithms to estimate the probability of the features being detectable and correctly matched. The purpose of this paper is to describe the predictions generated by PREMIO, how they are combined into a single probabilistic model, and illustrative examples showing its use in object recognition.

Citation Download Citation

Octavia I. Camps, Douglas W. Christoffel, and Anjali Pathak "Probabilistic view clustering in object recognition", Proc. SPIE 1828, Sensor Fusion V, (1 November 1992); https://doi.org/10.1117/12.131657

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