By Topic

Impulse restoration-based template-matching using the expectation-maximization algorithm

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

3 Author(s)
Abu-Naser, A. ; Dept. of Electr. & Comput. Eng., Illinois Inst. of Technol., Chicago, IL, USA ; Galatsanos, N.P. ; Wernick, M.N.

It has been demonstrated that object recognition can be formulated as an image-restoration problem. In this approach, which we term impulse restoration, the objective is to restore a delta function indicating the object's location. We develop solutions based on impulse restoration for the Gaussian noise case. We propose a new iterative approach, based on the expectation-maximization (EM) algorithm, that simultaneously estimates the background statistics and restores a delta function at the location of the template. We use localization-receiver-operating characteristics (LROC) curves to evaluate quantitatively the performance of this approach and compare it with existing methods. We present experimental results that demonstrate that impulse restoration is a powerful approach for detecting known objects in images severely degraded by noise. Our experiments demonstrate that accurate modeling and estimation of the background and noise statistics are essential to successful object detection

Published in:

Image Processing, 1997. Proceedings., International Conference on  (Volume:3 )

Date of Conference:

26-29 Oct 1997