Analysis and Structured Representation of the Theory of Abstract Cell Complexes Applied to Digital Topology and Digital Geometry
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1 1. Introduction Methods of Digital Topology Are Widely Used in Various
1. Introduction Methods of digital topology are widely used in various image processing operations including topology- preserving thinning, skeletonization, simplification, border and surface tracing and region filling and growing. Usually, transformations of digital objects preserve topological properties. One of the ways to do this is to use simple points, edges and cliques: loosely speaking, a point or an edge of a digital object is called simple if it can be deleted from this object without altering topology. The detection of simple points, edges and cliques is extremely important in image thinning, where a digital image of an object gets reduced to its skeleton with the same topological features. The notion of a simple point was introduced by Rosenfeld [14]. Since then due to its importance, characterizations of simple points in two, three, and four dimensions and algorithms for their detection have been studied in the framework of digital topology by many researchers (see, e.g., [1, 5, 12-13]). Local characterizations of simple points in three dimensions and efficient detection algorithms are particularly essential in such areas as medical image processing [2, 7-8, 15], where the shape correctness is required on the one hand and the image acquisition process is sensitive to the errors produced by the image noise, geometric distortions in the images, subject motion, etc, on the other hand. It has to be noticed that in this paper we use an approach that was developed in [3]. Digital spaces and manifolds are defined axiomatically as specialization graphs with the structure defined by the construction of the nearest neighborhood of every point of the graph. -
Equivalence Between Digital Well-Composedness and Well-Composedness in the Sense of Alexandrov on N-D Cubical Grids Nicolas Boutry, Laurent Najman, Thierry Géraud
Equivalence between Digital Well-Composedness and Well-Composedness in the Sense of Alexandrov on n-D Cubical Grids Nicolas Boutry, Laurent Najman, Thierry Géraud To cite this version: Nicolas Boutry, Laurent Najman, Thierry Géraud. Equivalence between Digital Well-Composedness and Well-Composedness in the Sense of Alexandrov on n-D Cubical Grids. Journal of Mathematical Imaging and Vision, Springer Verlag, 2020, 62 (9), pp.1285-1333. 10.1007/s10851-020-00988-z. hal- 02990817 HAL Id: hal-02990817 https://hal.archives-ouvertes.fr/hal-02990817 Submitted on 5 Nov 2020 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. JMIV manuscript No. (will be inserted by the editor) Equivalence between Digital Well-Composedness and Well-Composedness in the sense of Alexandrov on n-D Cubical Grids Nicolas Boutry · Laurent Najman · Thierry G´eraud Received: date / Accepted: date Abstract Among the different flavors of well-compo- 1 Introduction sednesses on cubical grids, two of them, called respec- tively digital well-composedness (DWCness) and well- composedness in the sense of Alexandrov (AWCness), are known to be equivalent in 2D and in 3D. The for- mer means that a cubical set does not contain critical configurations when the latter means that the boundary of a cubical set is made of a disjoint union of discrete surfaces. -
Digital Topology
Digital Topology Ulrich Eckhardt Department of Applied Mathematics University of Hamburg Bundesstraße 55 D–20 146 Hamburg, Germany and Longin Latecki Department of Computer Science University of Hamburg Vogt–K¨olln–Straße 30 D–22 527 Hamburg February 26, 2008 To appear in Trends in Pattern Recognition, Council of Scientific Information, Vilayil Gardens, Trivandrum, India. Hamburger Beitr¨agezur Angewandten Mathematik Reihe A, Preprint 89 October 1994 Contents 1 Introduction 1 1.1 Motivation and Scope . 1 1.2 Historical Remarks . 3 2 The Digital Plane 3 2.1 Basic Definitions . 3 2.2 Jordan’s Curve Theorem . 4 2.3 The graphs of 4– and 8–topologies . 6 3 Embedding the Digital Plane 7 3.1 Line Complexes . 7 3.2 Cellular Topology . 11 4 Axiomatic Digital Topology 12 4.1 Definition and Simple Properties . 12 4.2 Connectedness . 14 4.3 Alexandroff Topologies for the Digital Plane . 17 5 Semi–Topology 19 5.1 Motivation . 19 5.2 The Associated Topological Space . 20 5.3 Related Concepts . 21 5.4 Connectedness . 22 5.5 Ordered Sets . 23 6 Applications to Image Processing 24 6.1 Models for Discretization . 24 6.2 Continuity . 24 6.3 Homotopy . 25 6.4 Fuzzy Topology . 25 Abstract to avoid wrong conclusions. For example, if an au- tomatic reasoning system should interpret correctly The aim of this paper is to give an introduction into the sentence “The policemen encircled the house”, it the field of digital topology. This topic of research must be able to understand that it is not possible to arose in connection with image processing. -
A Tutorial on Well-Composedness Nicolas Boutry, Thierry Géraud, Laurent Najman
A Tutorial on Well-Composedness Nicolas Boutry, Thierry Géraud, Laurent Najman To cite this version: Nicolas Boutry, Thierry Géraud, Laurent Najman. A Tutorial on Well-Composedness. Journal of Mathematical Imaging and Vision, Springer Verlag, 2018, 60 (3), pp.443-478. 10.1007/s10851-017- 0769-6. hal-01609892v2 HAL Id: hal-01609892 https://hal.archives-ouvertes.fr/hal-01609892v2 Submitted on 12 Oct 2017 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. JMIV manuscript No. (will be inserted by the editor) A Tutorial on Well-Composedness Nicolas Boutry · Thierry G´eraud · Laurent Najman Received: date / Accepted: date Abstract Due to digitization, usual discrete signals 1 Introduction generally present topological paradoxes, such as the connectivity paradoxes of Rosenfeld. To get rid of those In 1979, Rosenfeld [148] studied basic topological pro- paradoxes, and to restore some topological properties to perties of digital images that he called digital topology. the objects contained in the image, like manifoldness, This work was completed in [90] by Kong and himself. Latecki proposed a new class of images, called well- However, Rosenfeld’s framework needs to use a dual composed images, with no topological issues. -
Digital Geometry, a Survey
Digital Geometry, a Survey Li Chen David Coeurjolly University of the District of Columbia Université de Lyon, CNRS, LIRIS ABSTRACT used directly. This paper provides an overview of modern digital geometry Image processing and computer graphics are two main and topology through mathematical principles, algorithms, reasons why digital geometry was developed. First, a dig- and measurements. It also covers recent developments in the ital image is stored in a digital array, and the image must applications of digital geometry and topology including im- be processed using some geometric properties of this type age processing, computer vision, and data science. Recent of data|digital arrays. In computer graphics, the internal research strongly showed that digital geometry has made representation of the data is usually in the form of triangu- considerable contributions to modelings and algorithms in lation or meshes. However, when the image is displayed on image segmentation, algorithmic analysis, and BigData an- the screen, it must be digitized since the screen is a digital alytics. array. Two popular examples can provide explanations as to why digital geometry is necessary. First, most image process- Keywords ing and computer vision problems require extracting cer- Digital geometry, Digital topology, image processing tain objects from the image. This process is called image segmentation, and it usually involves separating meaningful objects from the background. Even though the boundary 1. INTRODUCTION TO DIGITAL GEOM- of an object appears to be a continuous curve, it is actually ETRY a sequence of digital points. Identification, measurement, Digital geometry is the study of the geometric properties and extraction are all related to digital geometry.