Robust curvature extrema detection based on new numerical derivation

Cédric Join 1, 2 Salvatore Tabbone 3
2 ALIEN - Algebra for Digital Identification and Estimation
Inria Lille - Nord Europe, Inria Saclay - Ile de France, Ecole Centrale de Lille, X - École polytechnique, CNRS - Centre National de la Recherche Scientifique : UMR8146
3 QGAR - Querying Graphics through Analysis and Recognition
INRIA Lorraine, LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications
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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Communication dans un congrès
Advanced Concepts for Intelligent Vision Systems, ACIVS 2008, Oct 2008, Juan-les-Pins, France. Springer, 2008
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Cédric Join, Salvatore Tabbone. Robust curvature extrema detection based on new numerical derivation. Advanced Concepts for Intelligent Vision Systems, ACIVS 2008, Oct 2008, Juan-les-Pins, France. Springer, 2008. 〈inria-00300799〉

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