Object recognition using appearance representations derived from solid models of objects

Michael A. Sipe; David P. Casasent

doi:10.1117/12.316398

6 July 1998 Object recognition using appearance representations derived from solid models of objects

Michael A. Sipe, David P. Casasent

Proceedings Volume 3387, Visual Information Processing VII; (1998) https://doi.org/10.1117/12.316398
Event: Aerospace/Defense Sensing and Controls, 1998, Orlando, FL, United States

Abstract

We advance active computer vision algorithms for flexible manufacturing systems that classify objects and estimate their pose from intensity images. Our algorithms automatically reposition the sensor if the class or pose of an object is ambiguous in a given image and incorporate data from multiple object views in determining the final object classification. A feature space trajectory (FST) in a global eigenfeature space is used to represent 3-D distorted views of an object. Bayesian methods are used to derive the class hypothesis, pose estimate, confidence measures, and the sensor position that best resolves ambiguity. FSTs constructed using images rendered from solid models of objects are used to process real image data.

Citation Download Citation

Michael A. Sipe and David P. Casasent "Object recognition using appearance representations derived from solid models of objects", Proc. SPIE 3387, Visual Information Processing VII, (6 July 1998); https://doi.org/10.1117/12.316398

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