Robust curvature extrema detection based on new numerical derivation
Identifieur interne : 004248 ( Hal/Curation ); précédent : 004247; suivant : 004249Robust curvature extrema detection based on new numerical derivation
Auteurs : Cédric Join [France] ; Salvatore Tabbone [France]Source :
Abstract
Extrema of curvature are useful key points for different image analysis tasks. Indeed, polygonal approximation or arc decomposition methods used often these points to initialize or to improve their algorithms. Several shape-based image retrieval methods focus also their descriptors on key points. This paper is focused on the detection of extrema of curvature points for a raster-to-vector-conversion framework. We propose an original adaptation of an approach used into nonlinear control for fault-diagnosis and fault-tolerant control based on algebraic derivation and which is robust to noise. The experimental results are promising and show the robustness of the approach when the contours are bathed into a high level speckled noise.
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Indeed, polygonal approximation or arc decomposition methods used often these points to initialize or to improve their algorithms. Several shape-based image retrieval methods focus also their descriptors on key points. This paper is focused on the detection of extrema of curvature points for a raster-to-vector-conversion framework. We propose an original adaptation of an approach used into nonlinear control for fault-diagnosis and fault-tolerant control based on algebraic derivation and which is robust to noise. The experimental results are promising and show the robustness of the approach when the contours are bathed into a high level speckled noise.</div></front></TEI><hal api="V3"><titleStmt><title xml:lang="en">Robust curvature extrema detection based on new numerical derivation</title><author role="aut"><persName><forename type="first">Cédric</forename><surname>Join</surname></persName><email>cedric.join@cran.uhp-nancy.fr</email><idno type="halauthor">132593</idno><affiliation ref="#struct-1001"></affiliation><affiliation ref="#struct-56036"></affiliation></author><author role="aut"><persName><forename type="first">Salvatore</forename><surname>Tabbone</surname></persName><email>Antoine.Tabbone@loria.fr</email><idno type="halauthor">59023</idno><orgName ref="#struct-300292"></orgName><affiliation ref="#struct-2361"></affiliation></author><editor role="depositor"><persName><forename>Cédric</forename><surname>Join</surname></persName><email>cedric.join@cran.uhp-nancy.fr</email></editor></titleStmt><editionStmt><edition n="v1" type="current"><date type="whenSubmitted">2008-07-20 14:02:18</date><date type="whenModified">2016-05-19 01:15:02</date><date type="whenReleased">2008-07-20 21:26:15</date><date type="whenProduced">2008-10-23</date><date type="whenEndEmbargoed">2008-07-20</date><ref type="file" target="https://hal.inria.fr/inria-00300799/document"><date notBefore="2008-07-20"></date></ref><ref type="file" subtype="author" n="1" target="https://hal.inria.fr/inria-00300799/file/acivs_revised_final.pdf"><date notBefore="2008-07-20"></date></ref></edition><respStmt><resp>contributor</resp><name key="114590"><persName><forename>Cédric</forename><surname>Join</surname></persName><email>cedric.join@cran.uhp-nancy.fr</email></name></respStmt></editionStmt><publicationStmt><distributor>CCSD</distributor><idno type="halId">inria-00300799</idno><idno type="halUri">https://hal.inria.fr/inria-00300799</idno><idno type="halBibtex">join:inria-00300799</idno><idno type="halRefHtml">Advanced Concepts for Intelligent Vision Systems, ACIVS 2008, Oct 2008, Juan-les-Pins, France. 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Indeed, polygonal approximation or arc decomposition methods used often these points to initialize or to improve their algorithms. Several shape-based image retrieval methods focus also their descriptors on key points. This paper is focused on the detection of extrema of curvature points for a raster-to-vector-conversion framework. We propose an original adaptation of an approach used into nonlinear control for fault-diagnosis and fault-tolerant control based on algebraic derivation and which is robust to noise. The experimental results are promising and show the robustness of the approach when the contours are bathed into a high level speckled noise.</abstract></profileDesc></hal></record>Pour manipuler ce document sous Unix (Dilib)
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