Application of stochastic counterpart optimization to contrast-detection autofocusing

Author

Sliwinski, Przemyslaw ; Wachel, Pawel

Author_Institution

Inst. of Comput. Eng., Control & Robot., Wroclaw Univ. of Technol., Wroclaw, Poland

fYear

2013

Firstpage

333

Lastpage

337

Abstract

A simple model for the contrast-detection autofocusing problem is considered. The variance of the image is examined as a focus function. We prove that the standard convergence rate of the variance estimate (empirical focus function) of order O(T^-1), where T is a (relative) sensor size, allows direct application of the golden-section search algorithm to the empirical focus function.

Keywords

computer vision; convergence; focusing; image sensors; optimisation; search problems; stochastic processes; contrast-detection autofocusing problem; convergence rate; empirical focus function; golden-section search algorithm; image variance; sensor size; stochastic counterpart optimization; variance estimate; Approximation algorithms; Correlation; Lenses; Optimization; Reactive power; Splines (mathematics); Stochastic processes; Focus function; convergence; empirical focus function; golden-section search; image variance; variance estimation;

fLanguage

English

Publisher

ieee

Conference_Titel

Advances in Computing, Communications and Informatics (ICACCI), 2013 International Conference on

Conference_Location

Mysore

Print_ISBN

978-1-4799-2432-5

Type

conf

DOI

10.1109/ICACCI.2013.6637193

Filename

6637193

Link To Document

https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=1639895