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In this paper, we look at the possibility to implement the algorithm to construct a discrete line studied by the first author in cellular automata. It turns out that such an implementation is feasible. [ABSTRACT FROM AUTHOR]

Abstract: We propose two fast methods for dominant point detection and polygonal representation of noisy and possibly disconnected curves based on a study of the decomposition of the curve into the sequence of maximal blurred segments . Starting from results of discrete geometry , the notion of maximal blurred segment of width has been proposed, well adapted to possibly noisy curves. The first method uses a fixed parameter that is the width of considered maximal blurred segments. The second method is deduced from the first one based on a multi-width approach to obtain a non-parametric method that uses no threshold for working with noisy curves. Comparisons with other methods in the literature prove the efficiency of our approach. Thanks to a recent result concerning the construction of the sequence of maximal blurred segments, the complexity of the proposed methods is O(n log n). An application of vectorization is also given in this paper. [Copyright &y& Elsevier]

We propose a geometric approach to extract local properties of digital curves. This approach uses the notion of blurred segment 1. It is an extension of Reveillès' arithmetical approach 2 on discrete lines, more flexible, to take into account noise in digital images. This notion is also extended to the 3D space. Algorithms are presented to decompose a curve into a sequence of maximal blurred segments. A curvature estimator for 2D and 3D curves is proposed relying on this decomposition as well as a torsion estimator for 3D curves. All these estimators can naturally work with disconnected curves. They prove their efficiency in experiments and in comparisons with some well-known methods of the literature. [ABSTRACT FROM AUTHOR]

Abstract: The concept of blurred segment is introduced, it allows the flexible segmentation of discrete curves, taking into account noise. Relying on an arithmetic definition of discrete lines [J.-P. Reveillès, Géométrie discrète, calculs en nombre entiers et algorithmique, Thèse d’état, Université Louis Pasteur, Strasbourg, 1991], it generalizes such lines, admitting that some points are missing. Thus, a larger class of objects is considered. A very efficient linear detection algorithm for blurred segments and its application to curve segmentation are presented. [Copyright &y& Elsevier]

The convexity of a discrete region is a property used in numerous domains of computational imagery. We study its detection in the particular case of polyominoes. We present a first method, directly relying on its definition. A second method, which is based on techniques for segmentation of curves in discrete lines, leads to a very simple, linear, algorithm whose correctness is proven. Correlatively, we obtain a characterisation of lower and upper frontiers of the convex hull of a discrete line segment. Finally, we evoke some applications of these results to the problem of discrete tomography. [Copyright &y& Elsevier]

In this paper, we investigate the problem of dominant point detection on digital curves which consists in finding points with local maximum curvature. Thanks to previous studies of the decomposition of curves into sequence of discrete structures [1–3] , namely maximal blurred segments of width ν [4] , an initial algorithm has been proposed in [5] to detect dominant points. However, an heuristic strategy is used to identify the dominant points. We now propose a modified algorithm without heuristics but a simple measure of angle. In addition, two applications are as well presented: (1) polygonal simplification to reduce the number of detected dominant points by associating a weight to each of them, and (2) classification using the polygon issued from the reduced dominant points. The experimental results demonstrate the efficiency and robustness of the proposed method. [ABSTRACT FROM AUTHOR]

This paper proposes a fully automatic method to segment wood knots from images obtained by an X-ray Computed Tomography scanner. Wood knot segmentation is known to be a difficult problem in the presence of sapwood because of the quite similar density of knots and wet sapwood. Classical segmentation techniques produce unsatisfactory results due to the very weak distinction between these two intensities. To overcome this limitation caused by physical characteristics, we propose to exploit the geometric properties of both the knot shapes and knot-sapwood interface. Based on previous work related to automatic knot detection, a new segmentation algorithm is proposed that uses a robust curvature estimation of 2D digital contours. The segmentation process is fast, easily parallelizable and produces better segmentation results than other state-of-the-art algorithms. It may be reproduced from the precise description given here or from source code available online. [ABSTRACT FROM AUTHOR]

Background: Many programs have been developed to identify transcription factor binding sites. However, most of them are not able to infer two-word motifs with variable spacer lengths. This case is encountered for RNA polymerase Sigma ( s) Factor Binding Sites (SFBSs) usually composed of two boxes, called -35 and -10 in reference to the transcription initiation point. Our goal is to design an algorithm detecting SFBS by using combinational and statistical constraints deduced from biological observations. Results: We describe a new approach to identify SFBSs by comparing two related bacterial genomes. The method, named SIGffRid (SIGma Factor binding sites Finder using R'MES to select Input Data), performs a simultaneous analysis of pairs of promoter regions of orthologous genes. SIGffRid uses a prior identification of over-represented patterns in whole genomes as selection criteria for potential -35 and -10 boxes. These patterns are then grouped using pairs of short seeds (of which one is possibly gapped), allowing a variable-length spacer between them. Next, the motifs are extended guided by statistical considerations, a feature that ensures a selection of motifs with statistically relevant properties. We applied our method to the pair of related bacterial genomes of Streptomyces coelicolor and Streptomyces avermitilis. Cross-check with the well-defined SFBSs of the SigR regulon in S. coelicolor is detailed, validating the algorithm. SFBSs for HrdB and BldN were also found; and the results suggested some new targets for these s factors. In addition, consensus motifs for BldD and new SFBSs binding sites were defined, overlapping previously proposed consensuses. Relevant tests were carried out also on bacteria with moderate GC content (i.e. Escherichia coli/Salmonella typhimurium and Bacillus subtilis/Bacillus licheniformis pairs). Motifs of house-keeping s factors were found as well as other SFBSs such as that of SigW in Bacillus strains. Conclusion: We demonstrate that our approach combining statistical and biological criteria was successful to predict SFBSs. The method versatility autorizes the recognition of other kinds of two-box regulatory sites. [ABSTRACT FROM AUTHOR]

Highlights: [•] A robust algorithm was proposed for segmenting knots in CT images of softwood logs. [•] The method was applied to 125 knots of five wood species. [•] The RMSE is comparable to RMSD between two repetitions of manual measurements. [•] The method works as well in wet sapwood as in heartwood. [•] The software will be published under the GPL license and available online soon. [ABSTRACT FROM AUTHOR]

• Defects with a diameter from 0.5 cm on tree trunks could be detected in TLS data. • Good accuracy of the defects classification was achieved with an F1 score of 0.86. • The accuracy was similar between tested species with different bark roughness. Three-dimensional data are increasingly prevalent in forestry thanks to terrestrial LiDAR. This work assesses the feasibility for an automated recognition of the type of local defects present on the bark surface. These singularities are frequently external markers of inner defects affecting wood quality, and their type, size, and frequency are major components of grading rules. The proposed approach assigns previously detected abnormalities in the bark roughness to one of the defect types: branches, branch scars, epicormic shoots, burls, and smaller defects. Our machine learning approach is based on random forests using potential defects shape descriptors, including Hu invariant moments, dimensions, and species. The results of our experiments involving different French commercial species, oak, beech, fir, and pine showed that most defects were well classified with an average F 1 score of 0.86. [ABSTRACT FROM AUTHOR]