NDLI: On stochastic gradient descent and quadratic mutual information for image registration

Content Provider	IEEE Xplore Digital Library
Author	Singh, A. Ahuja, N.
Copyright Year	2013
Description	Author affiliation: Dept. of Electr. & Comput. Eng., Univ. of Illinois at Urbana-Champaign, Urbana, IL, USA (Singh, A.; Ahuja, N.)
Abstract	Mutual information (MI) is quite popular as a cost function for intensity based registration of images due to its ability to handle highly non-linear relationships between intensities of the two images. More recently, quadratic mutual information (QMI) has been proposed as an alternative measure that computes Euclidean distance instead of KL divergence between the joint and the product of the marginal densities of pixel intensities. In this paper, we examine the conditions under which QMI is advantageous over the classical MI measure, for the image registration problem. We show that QMI is a better cost function to use for optimization methods such as stochastic gradient descent. We show that the QMI cost function remains much smoother than the classical MI measure on stochastic subsampling of the image data. As a consequence, QMI has a higher probability of convergence, even for larger degrees of initial misalignment of the images.
Starting Page	1326
Ending Page	1330
File Size	311529
Page Count	5
File Format	PDF
ISBN	9781479923410
DOI	10.1109/ICIP.2013.6738273
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-09-15
Publisher Place	Australia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Cost function Kernel Mutual information Convergence Image registration Joints image registration
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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