By Topic

Analysis of feature extraction criteria for vector field visualization

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Harikumar, G. ; Coordinated Sci. Lab., Illinois Univ., Urbana, IL, USA ; Bresler, Y.

This paper addresses the problem of the visualization of vector-valued images. It is attempted to synthesize a display matched to the capabilities of a human observer. The problem is reduced to the extraction of the best linear feature of the vector field. Several new nonparametric feature extraction criteria (projection indices) that make use of both the spatial and multivariate structures of the data have been recently proposed and demonstrated. A theoretical analysis of these projection indices and a Monte-Carlo study of their effectiveness is presented here. The study uses performance measures derived from a decision-theoretic model of the human observer

Published in:

Pattern Recognition, 1994. Vol. 3 - Conference C: Signal Processing, Proceedings of the 12th IAPR International Conference on

Date of Conference:

9-13 Oct 1994