By Topic

Object detection by two-dimensional linear prediction

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
$33 $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

1 Author(s)
T. Quatieri ; MIT, Lincoln Laboratory, Lexington, Massachusetts

This paper addresses the problem of detecting small areas of textured images which differ from their immediate surroundings. A significance test is described which adapts itself to the generally changing background statistics so that a constant false alarm rate is maintained. A detection algorithm is derived from the fact that this significance test can be expressed in terms of the error residuals of an adaptive two-dimensional linear predictor whose coefficients are estimated from the background. The algorithm has been successfully demonstrated with both synthetic and real-world images.

Published in:

Acoustics, Speech, and Signal Processing, IEEE International Conference on ICASSP '83.  (Volume:8 )

Date of Conference:

Apr 1983