Detection of abrupt changes: theory and application
Detection of abrupt changes: theory and application
Optimum polygonal approximation of digitized curves
Pattern Recognition Letters
A new split-and-merge technique for polygonal approximation of chain coded curves
Pattern Recognition Letters
Algorithms for straight line fitting using k-means
Pattern Recognition Letters
IEEE Transactions on Pattern Analysis and Machine Intelligence
Exploring the Performance of Genetic Algorithms as Polygonal Approximators
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 3
High-Quality Polygonal Contour Approximation Based on Relaxation
ICDAR '01 Proceedings of the Sixth International Conference on Document Analysis and Recognition
Using penalized contrasts for the change-point problem
Signal Processing
Multivariate numerical differentiation
Journal of Computational and Applied Mathematics
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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.