NDLI: Finding the pseudogradient of the objective function in procedures for estimation of interframe deformations of images

Content Provider	Springer Nature Link
Author	Horeva, A. M. Tashlinskii, A. G. Birtimirov, L. Sh.
Copyright Year	2013
Abstract	In the recursive estimate of parameters of interframe geometric deformations of digital images, calculating the pseudogradient from a local sample and current estimates of deformation parameters to be measured involves finding derivatives in terms of finite differences. The maximal convergence rate of estimates of the deformation parameters can be achieved when using the most informative readings of images in a local sample. Under this approach, there exist optimal values of increments in the parameters and basic axes, depending on the mismatch between estimates and correlation properties of the image. The performance of the procedures may be increased if the increments are optimized at each iteration. The potential accuracy of estimation of the parameters, as given by the Cramer-Rao inequality, serves as an optimality criterion when calculating the pseudogradient of the objective function. It is shown that the condition of maximum of information can be reduced to the condition of maximization of the ratio of the expectation of the pseudogradient to its mean square deviation. Application of optimized values of increments enables one to reduce considerably (up to several times) the computational cost with the same accuracy of estimation.
Starting Page	440
Ending Page	444
Page Count	5
File Format	PDF
ISSN	10546618
Journal	Pattern Recognition and Image Analysis
Volume Number	23
Issue Number	4
e-ISSN	15556212
Language	English
Publisher	Springer US
Publisher Date	2013-12-19
Publisher Place	Boston
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	image interframe deformations estimation of parameters identification-free adaptation pseudogradient optimization Pattern Recognition Image Processing and Computer Vision
Content Type	Text
Resource Type	Article
Subject	Computer Graphics and Computer-Aided Design Computer Vision and Pattern Recognition

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Pseudogradient optimization of objective function in estimation of geometric interframe image deformations

Convergence of estimates of image interframe deformation parameters in pseudogradient estimation

Analysis of methods of estimating objective function gradient during recurrent measurements of image parameters

Pseudogradient optimization in the estimation of geometric interframe image deformations

Image preprocessing for pseudoogradient evaluation of interframe geometric deformations

Choice of finite differences in finding the goal function gradient in the estimation procedures of interframe image deformations

Choice of the objective function for pseudogradient measurement of image parameters

Analysis of objective functions in the problem of estimating mutual geometric deformations of images

Optimization of mismatch euclidean distance in evaluating interframe deformations of geometrical images

Finding the pseudogradient of the objective function in procedures for estimation of interframe deformations of images

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