On the beneficial effect of noise in vertex localization

A theoretical and experimental analysis related to the effect of noise in the task of vertex identication in unknown shapes is presented. Shapes are seen as real functions of their closed boundary. An alternative global perspective of curvature is examined providing insight into the process of noise- enabled vertex localization. The analysis reveals that noise facilitates in the localization of certain vertices. The concept of noising is thus considered and a relevant global method for localizing Global Vertices is investigated in relation to local methods under the presence of increasing noise. Theoretical analysis reveals that induced noise can indeed help localizing certain vertices if combined with global descriptors. Experiments with noise and a comparison to localized methods validate the theoretical results

Multi-scale Analysis of Discrete Contours for Unsupervised Noise Detection

International audienceBlurred segments [Debled06] were introduced in discrete geometry to address possible noise along discrete contours. The noise is not really detected but is rather canceled out by thickening digital straight segments. The thickness is tuned by a user and set globally for the contour, which requires both supervision and non-adaptive contour processing. To overcome this issue, we propose an original strategy to detect locally both the amount of noise and the meaningful scales of each point of a digital contour. Based on the asymptotic properties of maximal segments, it also detects curved and flat parts of the contour. From a given maximal observation scale, the proposed approach does not require any parameter tuning and is easy to implement. We demonstrate its effectiveness on several datasets. Its potential applications are numerous, ranging from geometric estimators to contour reconstruction

Blurred Segments in Gray Level Images for Interactive Line Extraction

International audienceAbstract. The recognition of discrete straight segments is a significant topic in the field of discrete geometry and for many applications dealing with geometric feature extraction. It can be performed from noisy binary data using the concept of blurred segments [3,2]. However, to our best knowledge, these algorithms have never been defined to directly extract straight segments in gray level images. This article proposes a solution to extend the recognition by using gray level image information. Although initially intended to be implemented within a semi-automatic line selec- tion tool used in an interactive 3D modeling application, it also meets more general parameter extraction requirements

Greyscale Image Vectorization from Geometric Digital Contour Representations

International audienceIn the field of digital geometry, numerous advances have been recently made to efficiently represent a simple polygonal shape; from dominant points of a curvature-based representation, a binary shape is efficiently represented even in presence of noise. In this article, we exploit recent results of such digital contour representations and propose an image vectorization algorithm allowing a geometric quality control. All the results presented in this paper can also be reproduced online