Parallel data fusion on a hypercube multiprocessor

Paul B. Davis; D. Cate; Mongi A. Abidi

doi:10.1117/12.25291

1 April 1991 Parallel data fusion on a hypercube multiprocessor

Paul B. Davis, D. Cate, Mongi A. Abidi

Proceedings Volume 1383, Sensor Fusion III: 3D Perception and Recognition; (1991) https://doi.org/10.1117/12.25291
Event: Advances in Intelligent Robotics Systems, 1990, Boston, MA, United States

Abstract

A new parallel analytic data fusion method has been developed and tested on real image pairs. This fusion algorithm is based on the interaction between two analytically formulated constraints: (1) the principle of Knowledge Source Aggregation, and (2) the principle of Belief Enhancement/Withdrawal. In this paper, we discuss ways in which a message-passing multiprocessor employing the hypercube interconnection topology is exploited in order to achieve optimal speed-up in the parallel data fusion algorithm. Image parallelism is optimized by having multiple processors execute the same task but operate on different subsets of the data. Two numerical methods used to solve a system of partial differential equations resulting from the use of the Euler-Lagrange equation for the fusion process are compared. Tests conducted on an NCUBE/4 parallel computer have resulted in an effective implementation of the complete fusion process.

Citation Download Citation

Paul B. Davis, D. Cate, and Mongi A. Abidi "Parallel data fusion on a hypercube multiprocessor", Proc. SPIE 1383, Sensor Fusion III: 3D Perception and Recognition, (1 April 1991); https://doi.org/10.1117/12.25291

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