There are 174 Subdivisions of the Hexahedron into Tetrahedra

This article answers an important theoretical question: How many different subdivisions of the hexahedron into tetrahedra are there? It is well known that the cube has five subdivisions into 6 tetrahedra and one subdivision into 5 tetrahedra. However, all hexahedra are not cubes and moving the vertex positions increases the number of subdivisions. Recent hexahedral dominant meshing methods try to take these configurations into account for combining tetrahedra into hexahedra, but fail to enumerate them all: they use only a set of 10 subdivisions among the 174 we found in this article. The enumeration of these 174 subdivisions of the hexahedron into tetrahedra is our combinatorial result. Each of the 174 subdivisions has between 5 and 15 tetrahedra and is actually a class of 2 to 48 equivalent instances which are identical up to vertex relabeling. We further show that exactly 171 of these subdivisions have a geometrical realization, i.e. there exist coordinates of the eight hexahedron vertices in a three-dimensional space such that the geometrical tetrahedral mesh is valid. We exhibit the tetrahedral meshes for these configurations and show in particular subdivisions of hexahedra with 15 tetrahedra that have a strictly positive Jacobian

Topological challenges in multispectral image segmentation

Land cover classification from remote sensing multispectral images has been traditionallyconducted by using mainly spectral information associated with discrete spatial units (i.e. pixels).Geometric and topological characteristics of the spatial context close to every pixel have been either not fully treated or completely ignored.This article provides a review of the strategies used by a number of researchers in order to include spatial and topological properties in image segmentation.­­­It is shown how most of researchers have proposed to perform -previous to classification- a grouping or segmentation of nearby pixels by modeling neighborhood relationships as 4-connected, 8-connected and (a, b) – connected graphs.In this object-oriented approach, however, topological concepts such as neighborhood, contiguity, connectivity and boundary suffer from ambiguity since image elements (pixels) are two-dimensional entities composing a spatially uniform grid cell (i.e. there are not uni-dimensional nor zero-dimensional elements to build boundaries). In order to solve such topological paradoxes, a few proposals have been proposed. This review discusses how the alternative of digital images representation based on Cartesian complexes suggested by Kovalevsky (2008) for image segmentation in computer vision, does not present topological flaws, typical of conventional solutions based on grid cells. However, such a proposal has not been yet applied to multispectral image segmentation in remote sensing. This review is part of the PhD in Engineering research conducted by the first author under guidance of the second one. This review concludes suggesting the need to research on the potential of using Cartesian complexes for multispectral image segmentation

An extensive English language bibliography on graph theory and its applications, supplement 1

Graph theory and its applications - bibliography, supplement

Geometric Reasoning with polymake

The mathematical software system polymake provides a wide range of functions for convex polytopes, simplicial complexes, and other objects. A large part of this paper is dedicated to a tutorial which exemplifies the usage. Later sections include a survey of research results obtained with the help of polymake so far and a short description of the technical background

Stretchability of Star-Like Pseudo-Visibility Graphs

We present advances on the open problem of characterizing vertex-edge visibility graphs (ve-graphs), reduced by results of O\u27Rourke and Streinu to a stretchability question for pseudo-polygons. We introduce star-like pseudo-polygons as a special subclass containing all the known instances of non-stretchable pseudo-polygons. We give a complete combinatorial characterization and a linear-time decision procedure for star-like pseudo-polygon stretchability and star-like ve-graph recognition. To the best of our knowledge, this is the first problem in computational geometry for which a combinatorial characterization was found by first isolating the oriented matroid substructure and then separately solving the stretchability question. It is also the first class (as opposed to isolated examples) of oriented matroids for which an efficient stretchability decision procedure based on combinatorial criteria is given. The difficulty of the general stretchability problem implied by Mnev\u27s Universality Theorem makes this a result of independent interest in the theory of oriented matroids

Stretchability of Star-Like Pseudo-Visibility Graphs

We present advances on the open problem of characterizing vertex-edge visibility graphs (ve-graphs), reduced by results of O\u27Rourke and Streinu to a stretchability question for pseudo-polygons. We introduce star-like pseudo-polygons as a special subclass containing all the known instances of non-stretchable pseudo-polygons. We give a complete combinatorial characterization and a linear-time decision procedure for star-like pseudo-polygon stretchability and star-like ve-graph recognition. To the best of our knowledge, this is the first problem in computational geometry for which a combinatorial characterization was found by first isolating the oriented matroid substructure and then separately solving the stretchability question. It is also the first class (as opposed to isolated examples) of oriented matroids for which an efficient stretchability decision procedure based on combinatorial criteria is given. The difficulty of the general stretchability problem implied by Mnev\u27s Universality Theorem makes this a result of independent interest in the theory of oriented matroids

Multispectral image classification from axiomatic locally finite spaces-based segmentation

Geographical object-based image analysis (GEOBIA) usually starts defining coarse geometric space elements, i.e. image-objects, by grouping near pixels based on (a, b)-connected graphs as neighbourhood definitions. In such an approach, however, topological axioms needed to ensure a correct representation of connectedness relationships can not be satisfied. Thus, conventional image-object boundaries definition presents ambiguities because one-dimensional contours are represented by two-dimensional pixels. In this paper, segmentation is conducted using a novel approach based on axiomatic locally finite spaces (provided by Cartesian complexes) and their linked oriented matroids. For the test, the ALFS-based image segments were classified using the support vector machine (SVM) algorithm using directional filter response as an additional channel. The proposed approach uses a multi-scale approach for the segmentation, which includes multi-scale texture and spectral affinity analysis in boundary definition. The proposed approach was evaluated comparatively with conventional pixel representation on a small subset of GEOBIA2016 benchmark dataset. Results show that classification accuracy is increased in comparison to a conventional pixel segmentation.El análisis de imagenes basado en objetos geográficos (GEOBIA por su sigla en inglés) comienza generalmente definiendo elementos más gruesos del espacio geométrico u objetos de imagen, agrupando píxeles cercanos con base en grafos (a, b)-conectados como definiciones de vecindario. En este enfoque, sin embargo, pueden no cumplirse algunos axiomas topológicos requeridos para garantizar una correcta representación de las relaciones de conexión. Por lo tanto, la definición convencional de límites de objetos de imagen, presenta ambigüedades debido a que los contornos unidimensionales están representados por píxeles bidimensionales. En este trabajo, la segmentación se lleva a cabo mediante un nuevo enfoque basado en espacios axiomáticos localmente finitos (proporcionados por complejos cartesianos) y sus matroides orientados asociados. Para probar el enfoque propuesto, los segmentos de la imagen basada en ALFS fueron clasificados usando el algoritmo de máquina de soporte vectorial (SVM por su sigla en inglés) usando la respuesta a filtros direccionales como un canal adicional. El enfoque propuesto utiliza un enfoque multiescala para la segmentación, que incluye análisis de textura y de afinidad espectral en la definición de límite. La propuesta se evaluó comparativamente con la representación de píxeles convencionales en un pequeño subconjunto del conjunto de datos de referencia GEOBIA2016. Los resultados muestran que la exactitud de la clasificación se incrementa en comparación con la segmentación convencional de pixeles

Discrete Geometry

A number of important recent developments in various branches of discrete geometry were presented at the workshop. The presentations illustrated both the diversity of the area and its strong connections to other fields of mathematics such as topology, combinatorics or algebraic geometry. The open questions abound and many of the results presented were obtained by young researchers, confirming the great vitality of discrete geometry

Retos topológicos en la segmentación de imágenes multiespectrales

Land cover classification from remote sensing multispectral images has been traditionallyconducted by using mainly spectral information associated with discrete spatial units (i.e. pixels).Geometric and topological characteristics of the spatial context close to every pixel have been either not fully treated or completely ignored.This article provides a review of the strategies used by a number of researchers in order to include spatial and topological properties in image segmentation.­­­It is shown how most of researchers have proposed to perform -previous to classification- a grouping or segmentation of nearby pixels by modeling neighborhood relationships as 4-connected, 8-connected and (a, b) – connected graphs.In this object-oriented approach, however, topological concepts such as neighborhood, contiguity, connectivity and boundary suffer from ambiguity since image elements (pixels) are two-dimensional entities composing a spatially uniform grid cell (i.e. there are not uni-dimensional nor zero-dimensional elements to build boundaries). In order to solve such topological paradoxes, a few proposals have been proposed. This review discusses how the alternative of digital images representation based on Cartesian complexes suggested by Kovalevsky (2008) for image segmentation in computer vision, does not present topological flaws, typical of conventional solutions based on grid cells. However, such a proposal has not been yet applied to multispectral image segmentation in remote sensing.  This review is part of the PhD in Engineering research conducted by the first author under guidance of the second one. This review concludes suggesting the need to research on the potential of using Cartesian complexes for multispectral image segmentation.La clasificación de la cobertura terrestre a partir de imágenes multiespectrales de teledetección se ha llevado a cabo tradicionalmente utilizando información principalmente espectral asociada a unidades espaciales discretas (es decir, píxeles). Las características geométricas y topológicas del contexto espacial cercanas a cada píxel no se han tratado del todo o se han ignorado por completo. proporciona una revisión de las estrategias utilizadas por un número de investigadores para incluir propiedades espaciales y topológicas en la segmentación de imágenes. Se muestra cómo la mayoría de los investigadores han propuesto realizar, antes de la clasificación, una agrupación o segmentación de píxeles cercanos modelando el vecindario relaciones como 4 conectadas, 8 conectadas y (a, b) conectadas. Sin embargo, en este enfoque orientado a objetos, los conceptos topológicos como vecindad, contigüidad, conectividad y límite sufren de ambigüedad ya que los elementos de imagen (píxeles) son dos entidades tridimensionales que componen una celda de cuadrícula espacialmente uniforme (es decir, no hay uni-di elementos mensionales o de cero dimensiones para construir límites). Para resolver tales paradojas topológicas, se han propuesto algunas propuestas. Esta revisión discute cómo la alternativa de representación de imágenes digitales basada en complejos cartesianos sugerida por Kovalevsky (2008) para la segmentación de imágenes en visión artificial, no presenta fallas topológicas, típicas de soluciones convencionales basadas en celdas de grillas. Sin embargo, tal propuesta aún no se ha aplicado a la segmentación de imágenes multiespectrales en teledetección. Esta revisión es parte del doctorado en investigación de ingeniería conducida por el primer autor bajo la dirección del segundo. Esta revisión concluye sugiriendo la necesidad de investigar sobre el potencial del uso de complejos cartesianos para la segmentación de imágenes multiespectrales