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Analysis of Noisy Digital Contours with Adaptive Tangential Cover

Abstract : The notion of tangential cover, based on maximal segments, is a well-known tool to study the geometrical characteristics of a discrete curve. However, it is not robust to noise, while extracted contours from digital images typically contain noise and this makes the geometric analysis tasks on such contours difficult. To tackle this issue, we investigate in this paper a discrete structure, named Adaptive Tangential Cover (ATC), which is based on the notion of tangential cover and on a local noise estimator. More specifically, the ATC is composed of maximal segments with different widths deduced from the local noise values estimated at each point of the contour. Furthermore, a parameter-free algorithm is also presented to compute ATC. This study leads to the proposal of several applications of ATC on noisy digital contours: dominant point detection, contour length estimator, tangent/normal estimator, detection of convex and concave parts. An extension of ATC to 3D curves is also proposed in this paper. The experimental results demonstrate the efficiency of this new notion.
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Phuc Ngo, Isabelle Debled-Rennesson, Bertrand Kerautret, Hayat Nasser. Analysis of Noisy Digital Contours with Adaptive Tangential Cover. Journal of Mathematical Imaging and Vision, Springer Verlag, 2017, 59 (1), pp.123-135. ⟨10.1007/s10851-017-0723-7⟩. ⟨hal-01486969⟩

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