Edge detector design based on LS-SVR
type: Item
instance of: scholarly article
title: Edge detector design based on LS-SVR
zbMATH Open document ID: 1407.94053
DOI: 10.1155/2014/419792
author: Xianqiang Yang
published in: Mathematical Problems in Engineering
publication date: 2019-02-08T00:00:00Z
review text: Summary: For locating inaccurate problem of the discrete localization criterion proposed by Demigny, a new criterion expression of ``good localization'' is proposed. Firstly, a discrete expression of good detection and good localization criterion of two dimension edge detection operator is employed, and then an experiment to measure optimal parameters of two dimension Canny's edge detection operator is introduced after. Moreover, a detailed performance comparison and analysis of two dimension optimal filter obtained via utilizing tensor product for one dimension optimal filter are provided which can prove that least square support vector regression (LS-SVR) is a smoothness filter and give the construct method of the derivate operator. This paper uses LS-SVR as the object function constructor and then realizes the approximation of two dimension optimal edge detection operator. This paper proposes the utility method of using singleness operator to realize multiscale edge detection by referencing the multiscale analysis technology of the wavelets theory. Experiment shows that the method has utility and efficiency.
Mathematics Subject Classification ID: 94A13
zbMATH DE Number: 7016399
Wikidata QID: Q59066987
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2014/419792
OpenAlex ID: W2071768623
cites work: Data-driven adaptive observer for fault diagnosis
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cites work: Asymptotic Behaviors of Support Vector Machines with Gaussian Kernel
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description: scientific article; zbMATH DE number 7016399