Computational Topology in Image Context: 7th International Workshop, CTIC 2019, M�laga, Spain, January 24-25, 2019, Proceedings

by Rebeca Marfil (Editor), Mariletty Calderïn (Editor), Fernando Dïaz del Rïo (Editor), Pedro Real (Editor), Antonio Bandera (Editor)

Overview

This book constitutes the proceedings of the 7th International Workshop on Computational Topology in Image Context, CTIC 2019, held in Málaga, Spain, in January 2019.

The 14 papers presented in this volume were carefully reviewed and selected from 21 submissions. Papers deal with theoretical issues but most of them put the attention on the applicability of concepts and algorithms. These were designed to deal with objects and images, but also with the speech signal. The final application must be for instance in the medical domain or in the robotics one.

Product Details

ISBN-13:	9783030108274
Publisher:	Springer International Publishing
Publication date:	12/22/2018
Series:	Lecture Notes in Computer Science , #11382
Edition description:	1st ed. 2019
Pages:	191
Product dimensions:	6.10(w) x 9.25(h) x (d)

Table of Contents

Discrete analog of the Jacobi set for vector fields.- Computing and reducing slope complexes.- Persistent Homology Computation Using Combinatorial Map Simplification.- Inferring Underlying Manifold of Data by the Use of Persistent Homology Analysis.- Gaps and Well-Composed Objects in the Triangular Grid.- Generating Second Order (Co)homological Information within AT-Model Context.- Computing the Component-Labeling and the Adjacency Tree of a Binary Digital Image in Near Logarithmic-time.- Towards Emotion Recognition: A Persistent Entropy Application.- Harmonic holes as the submodules of brain network and network dissimilarity.- Persistent 1-Cycles: Definition, Computation, and Its Application.- A persistence-based approach to automatic detection of line segments in images.- On the Nonlinear Statistics of Optical Flow.- Topological Homogeneity for Electron Microscopy Images.- Characterizing epithelial tissues using persistent entropy.