Sensor And Information Fusion From Knowledge-Based Constraints

Allen R Hanson; Edward M Riseman; Thornas D Williams

doi:10.1117/12.946667

9 August 1988 Sensor And Information Fusion From Knowledge-Based Constraints

Allen R Hanson, Edward M Riseman, Thornas D Williams

Proceedings Volume 0931, Sensor Fusion; (1988) https://doi.org/10.1117/12.946667
Event: 1988 Technical Symposium on Optics, Electro-Optics, and Sensors, 1988, Orlando, FL, United States

Abstract

A constraint-based approach to uniformly combining information from multiple representations and sources of sensory data is described. The approach is important to research in intermediate grouping, knowledge-based model matching, and information fusion. The techniques presented extend the capabilities of an earlier system that applied constraints to attributes of single types of extracted image events called tokens. Relational measures are defined between symbolic tokens so that sets of tokens across representations can he selected and grouped on the basis of constraint functions applied to these relational measures. Since typical low-level representations involve hundreds or thousands of tokens in each representation, even binary relational measures can involve very large numbers of token pairs. Control strategies for ordering and filtering tokens, based upon constraints on token attributes and token relationships, can be formed to reduce the computation involved in producing token aggregations. The system is demonstrated using region and line data and an associated set of relational measures. The approach can be naturally extended to include tokens extracted from motion, stereo, and range data.

Citation Download Citation

Allen R Hanson, Edward M Riseman, and Thornas D Williams "Sensor And Information Fusion From Knowledge-Based Constraints", Proc. SPIE 0931, Sensor Fusion, (9 August 1988); https://doi.org/10.1117/12.946667

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