利用香农最大互信息标准合成和优化图像注册随机算法

IF 0.5 Q4 PHYSICS, MULTIDISCIPLINARY Optoelectronics Instrumentation and Data Processing Pub Date : 2024-07-29 DOI:10.3103/s8756699024700213

A. G. Tashlinskii, R. M. Ibragimov, G. L. Safina

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引用次数: 0

摘要

摘要在随机梯度适应数学方法的基础上，提出了一种利用香农最大互信息准则估计图像配准参数的算法。得到了互信息梯度的表达式。同时，使用 Parzen 窗口法求得图像熵相对于估计参数的导数。对合成算法进行了优化，以提高性能。

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Synthesis and Optimization of a Stochastic Algorithm for Image Registration Using Shannon’s Maximum Mutual Information Criterion

Abstract

An algorithm for estimating image registration parameters using Shannon’s maximum mutual information criterion is synthesized on the basis of the mathematical methods of stochastic gradient adaptation. Expressions for the gradient of mutual information are obtained. At the same time, the derivatives of the entropy of images with respect to the estimated parameters are found using the Parzen window method. The synthesized algorithm is optimized to improve performance.

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来源期刊

Optoelectronics Instrumentation and Data Processing PHYSICS, MULTIDISCIPLINARY-

CiteScore

1.00

自引率

50.00%

发文量

期刊介绍： The scope of Optoelectronics, Instrumentation and Data Processing encompasses, but is not restricted to, the following areas: analysis and synthesis of signals and images; artificial intelligence methods; automated measurement systems; physicotechnical foundations of micro- and optoelectronics; optical information technologies; systems and components; modelling in physicotechnical research; laser physics applications; computer networks and data transmission systems. The journal publishes original papers, reviews, and short communications in order to provide the widest possible coverage of latest research and development in its chosen field.