Revisiting Digital Straight Segment Recognition

This paper presents new results about digital straight segments, their recognition and related properties. They come from the study of the arithmetically based recognition algorithm proposed by I. Debled-Rennesson and J.-P. Reveill\`es in 1995 [Debled95]. We indeed exhibit the relations describing the possible changes in the parameters of the digital straight segment under investigation. This description is achieved by considering new parameters on digital segments: instead of their arithmetic description, we examine the parameters related to their combinatoric description. As a result we have a better understanding of their evolution during recognition and analytical formulas to compute them. We also show how this evolution can be projected onto the Stern-Brocot tree. These new relations have interesting consequences on the geometry of digital curves. We show how they can for instance be used to bound the slope difference between consecutive maximal segments

Maximal digital straight segments and convergence of discrete geometric estimators

Discrete geometric estimators approach geometric quantities on digitized shapes without any knowledge of the continuous shape. A classical yet difficult problem is to show that an estimator asymptotically converges toward the true geometric quantity as the resolution increases. We study here the convergence of local estimators based on Digital Straight Segment (DSS) recognition. It is closely linked to the asymptotic growth of maximal DSS, for which we show bounds both about their number and sizes. These results not only give better insights about digitized curves but indicate that curvature estimators based on local DSS recognition are not likely to converge. We indeed invalidate an hypothesis which was essential in the only known convergence theorem of a discrete curvature estimator. The proof involves results from arithmetic properties of digital lines, digital convexity, combinatorics, continued fractions and random polytopes

Minimal Decomposition of a Digital Surface into Digital Plane Segments Is NP-Hard

Abstract. This paper deals with the complexity of the decomposition of a digital surface into digital plane segments (DPS for short). We prove that the decision problem (does there exist a decomposition with less than k DPS?) is NP-complete, and thus that the optimisation problem (finding the minimal number of DPS) is NP-hard. The proof is based on a polynomial reduction of any instance of the well-known 3-SAT problem to an instance of the digital surface decomposition problem. A geometric model for the 3-SAT problem is proposed.

Gift-Wrapping based Preimage Computation Algorithm

International audienceBased on a classical convex hull algorithm called Gift-Wrapping, the purpose of the paper is to provide a new algorithm for computing the vertices of a polytope called preimage - roughly the set of naive digital planes containing a finite subset $S$ of $\mathbb{Z}^3$. The vertices of the upper hemisphere, the ones of the lower hemisphere and at last the equatorial vertices are computed independently. The principle of the algorithm is based on duality and especially on the fact that the vertices of the preimage correspond to faces of the input set $S$ or of its chords set $S\ominus S \cup \{(0,0,1)\}$. It allows to go from one vertex to another by gift-wrapping until the whole region of interest has been explored

Gift-Wrapping based Preimage Computation Algorithm

International audienceBased on a classical convex hull algorithm called Gift-Wrapping, the purpose of the paper is to provide a new algorithm for computing the vertices of a polytope called preimage - roughly the set of naive digital planes containing a finite subset $S$ of $\mathbb{Z}^3$. The vertices of the upper hemisphere, the ones of the lower hemisphere and at last the equatorial vertices are computed independently. The principle of the algorithm is based on duality and especially on the fact that the vertices of the preimage correspond to faces of the input set $S$ or of its chords set $S\ominus S \cup \{(0,0,1)\}$. It allows to go from one vertex to another by gift-wrapping until the whole region of interest has been explored

What Does Digital Straightness Tell about Digital Convexity?

International audienceThe paper studies local convexity properties of parts of dig- ital boundaries. An online and linear-time algorithm is introduced for the decomposition of a digital boundary into convex and concave parts. In addition, other data are computed in the same time without any extra cost: the hull of each convex or concave part as well as the Bezout points of each edge of those hulls. The proposed algorithm involves well- understood algorithms: adding a point to the front or removing a point from the back of a digital straight segment and computing the set of maximal segments. The output of the algorithm is useful either for a polygonal representation of digital boundaries or for a segmentation into circular arcs

Multiscale Discrete Geometry

International audienceThis paper presents a first step in analyzing how digital shapes behave with respect to multiresolution. We first present an analysis of the covering of a standard digital straight line by a multi-resolution grid. We then study the multi-resolution of Digital Straight Segments (DSS): we provide a sublinear algorithm computing the exact characteristics of a DSS whenever it is a subset of a known standard line. We finally deduce an algorithm for computing a multiscale representation of a digital shape, based only on a DSS decomposition of its boundary