Topological analysis of discrete scalar data

This thesis presents a novel computational framework that allows for a robust extraction and quantification of the Morse-Smale complex of a scalar field given on a 2- or 3- dimensional manifold. The proposed framework is based on Forman\u27s discrete Morse theory, which guarantees the topological consistency of the computed complex. Using a graph theoretical formulation of this theory, we present an algorithmic library that computes the Morse-Smale complex combinatorially with an optimal complexity of $O(n^2)$ and efficiently creates a multi-level representation of it. We explore the discrete nature of this complex, and relate it to the smooth counterpart. It is often necessary to estimate the feature strength of the individual components of the Morse-Smale complex -- the critical points and separatrices. To do so, we propose a novel output-sensitive strategy to compute the persistence of the critical points. We also extend this wellfounded concept to separatrices by introducing a novel measure of feature strength called separatrix persistence. We evaluate the applicability of our methods in a wide variety of application areas ranging from computer graphics to planetary science to computer and electron tomography.In dieser Dissertation präsentieren wir ein neues System zur robusten Berechnung des Morse-Smale Komplexes auf 2- oder 3-dimensionalen Mannigfaltigkeiten. Das vorgestellte System basiert auf Forman’s diskreter Morsetheorie und garantiert damit die topologische Konsistenz des berechneten Komplexes. Basierend auf einer graphentheoretischer Formulierung präesentieren wir eine Bibliothek von Algorithmen, die es erlaubt, den Morse-Smale Komplex mit einer optimalen Kompliztät von $O(n^2)$ kombinatorisch zu berechnen und effizient eine mehrskalige Repräsentation davon erstellt. Wir untersuchen die diskrete Natur dieses Komplexes und vergleichen ihn zu seinem kontinuierlichen Gegenstück. Es ist häufig notwendig, die Merkmalsstärke einzelner Bestandteile des Komplexes -- der kritischen Punkte und Separatrizen -- abzuschätzen. Hierfür stellen wir eine neue outputsensitive Strategie vor, um die Persistenz von kritischen Punkten zu berechen. Wir erweitern dieses fundierte Konzept auf Separatrizen durch die Einführung des Wichtigkeitsmaßes Separatrixpersistenz. Wir evaluieren die Anwendbarkeit unserer Methoden anhand vielfältiger Anwendungen aus den Gebieten der Computergrafik, Planetologie, Computer- und Elektronentomographie

Conforming Morse-Smale complexes

pre-printMorse-Smale (MS) complexes have been gaining popularity as a tool for feature-driven data analysis and visualization. However, the quality of their geometric embedding and the sole dependence on the input scalar field data can limit their applicability when expressing application-dependent features. In this paper we introduce a new combinatorial technique to compute an MS complex that conforms to both an input scalar field and an additional, prior segmentation of the domain. The segmentation constrains the MS complex computation guaranteeing that boundaries in the segmentation are captured as separatrices of the MS complex. We demonstrate the utility and versatility of our approach with two applications. First, we use streamline integration to determine numerically computed basins/mountains and use the resulting segmentation as an input to our algorithm. This strategy enables the incorporation of prior flow path knowledge, effectively resulting in an MS complex that is as geometrically accurate as the employed numerical integration. Our second use case is motivated by the observation that often the data itself does not explicitly contain features known to be present by a domain expert. We introduce edit operations for MS complexes so that a user can directly modify their features while maintaining all the advantages of a robust topology-based representation

Localized flow, particle tracing, and topological separation analysis for flow visualization

Since the very beginning of the development of computers they have been used to accelerate the knowledge gain in science and research. Today they are a core part of most research facilities. Especially in natural and technical sciences they are used to simulate processes that would be hard to observe in real world experiments. Together with measurements from such experiments, simulations produce huge amounts of data that have to be analyzed by researchers to gain new insights and develop their field of science

COMPUTER GRAPHICS forum Extraction of Dominant Extremal Structures in Volumetric Data using Separatrix Persistence

Extremal lines and surfaces are features of a 3D scalar field where the scalar function becomes minimal or maximal with respect to a local neighborhood. These features are important in many applications, e.g., computer tomography, fluid dynamics, cell biology. We present a novel topological method to extract these features using discrete Morse theory. In particular, we extend the notion of Separatrix Persistence from 2D to 3D, which gives us a robust estimation of the feature strength for extremal lines and surfaces. Not only does it allow us to determine the most important (parts of) extremal lines and surfaces, it also serves as a robust filtering measure of noise-induced structures. Our purely combinatorial method does not require derivatives or any other numerical computations