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ACIVS
2008
Springer

Robust Curvature Extrema Detection Based on New Numerical Derivation

13 years 12 months ago
Robust Curvature Extrema Detection Based on New Numerical Derivation
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 shapebased 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-tovector-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.
Cédric Join, Salvatore Tabbone
Added 01 Jun 2010
Updated 01 Jun 2010
Type Conference
Year 2008
Where ACIVS
Authors Cédric Join, Salvatore Tabbone
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