Robust curvature extrema detection based on new numerical derivation

Cédric Join (1, 2) , Salvatore Tabbone (3)

1 CRAN - Centre de Recherche en Automatique de Nancy
2 ALIEN - Algebra for Digital Identification and Estimation
3 QGAR - Querying Graphics through Analysis and Recognition

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.

Domaines: Automatique

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. ⟨inria-00300799⟩