18,243 research outputs found

    Pattern search for the visualization of scalar, vector, and line fields

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    The main topic of this thesis is pattern search in data sets for the purpose of visual data analysis. By giving a reference pattern, pattern search aims to discover similar occurrences in a data set with invariance to translation, rotation and scaling. To address this problem, we developed algorithms dealing with different types of data: scalar fields, vector fields, and line fields. For scalar fields, we use the SIFT algorithm (Scale-Invariant Feature Transform) to find a sparse sampling of prominent features in the data with invariance to translation, rotation, and scaling. Then, the user can define a pattern as a set of SIFT features by e.g. brushing a region of interest. Finally, we locate and rank matching patterns in the entire data set. Due to the sparsity and accuracy of SIFT features, we achieve fast and memory-saving pattern query in large scale scalar fields. For vector fields, we propose a hashing strategy in scale space to accelerate the convolution-based pattern query. We encode the local flow behavior in scale space using a sequence of hierarchical base descriptors, which are pre-computed and hashed into a number of hash tables. This ensures a fast fetching of similar occurrences in the flow and requires only a constant number of table lookups. For line fields, we present a stream line segmentation algorithm to split long stream lines into globally-consistent segments, which provides similar segmentations for similar flow structures. It gives the benefit of isolating a pattern from long and dense stream lines, so that our patterns can be defined sparsely and have a significant extent, i.e., they are integration-based and not local. This allows for a greater flexibility in defining features of interest. For user-defined patterns of curve segments, our algorithm finds similar ones that are invariant to similarity transformations. Additionally, we present a method for shape recovery from multiple views. This semi-automatic method fits a template mesh to high-resolution normal data. In contrast to existing 3D reconstruction approaches, we accelerate the data acquisition time by omitting the structured light scanning step of obtaining low frequency 3D information.Das Hauptthema dieser Arbeit ist die Mustersuche in Datensätzen zur visuellen Datenanalyse. Durch die Vorgabe eines Referenzmusters versucht die Mustersuche ähnliche Vorkommen in einem Datensatz mit Translations-, Rotations- und Skalierungsinvarianz zu entdecken. In diesem Zusammenhang haben wir Algorithmen entwickelt, die sich mit verschiedenen Arten von Daten befassen: Skalarfelder, Vektorfelder und Linienfelder. Bei Skalarfeldern benutzen wir den SIFT-Algorithmus (Scale-Invariant Feature Transform), um ein spärliches Abtasten von markanten Merkmalen in Daten mit Translations-, Rotations- und Skalierungsinvarianz zu finden. Danach kann der Benutzer ein Muster als Menge von SIFT-Merkmalspunkten definieren, zum Beispiel durch Markieren einer interessierenden Region. Schließlich lokalisieren wir passende Muster im gesamten Datensatz und stufen sie ein. Aufgrund der spärlichen Verteilung und der Genauigkeit von SIFT-Merkmalspunkten erreichen wir eine schnelle und speichersparende Musterabfrage in großen Skalarfeldern. Für Vektorfelder schlagen wir eine Hashing-Strategie zur Beschleunigung der faltungsbasierten Musterabfrage im Skalenraum vor. Wir kodieren das lokale Flussverhalten im Skalenraum durch eine Sequenz von hierarchischen Basisdeskriptoren, welche vorberechnet und als Zahlen in einer Hashtabelle gespeichert sind. Dies stellt eine schnelle Abfrage von ähnlichen Vorkommen im Fluss sicher und benötigt lediglich eine konstante Anzahl von Nachschlageoperationen in der Tabelle. Für Linienfelder präsentieren wir einen Algorithmus zur Segmentierung von Stromlinien, um lange Stromlinen in global konsistente Segmente aufzuteilen. Dies erlaubt eine größere Flexibilität bei der Definition von Mustern. Für vom Benutzer definierte Muster von Kurvensegmenten findet unser Algorithmus ähnliche Kurvensegmente, die unter Ähnlichkeitstransformationen invariant sind. Zusätzlich präsentieren wir eine Methode zur Rekonstruktion von Formen aus mehreren Ansichten. Diese halbautomatische Methode passt ein Template an hochauflösendeNormalendatenan. Im Gegensatz zu existierenden 3D-Rekonstruktionsverfahren beschleunigen wir die Datenaufnahme, indem wir auf die Streifenprojektion verzichten, um niederfrequente 3D Informationen zu gewinnen

    Visualizing Magnitude and Direction in Flow Fields

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    In weather visualizations, it is common to see vector data represented by glyphs placed on grids. The glyphs either do not encode magnitude in readable steps, or have designs that interfere with the data. The grids form strong but irrelevant patterns. Directional, quantitative glyphs bent along streamlines are more effective for visualizing flow patterns. With the goal of improving the perception of flow patterns in weather forecasts, we designed and evaluated two variations on a glyph commonly used to encode wind speed and direction in weather visualizations. We tested the ability of subjects to determine wind direction and speed: the results show the new designs are superior to the traditional. In a second study we designed and evaluated new methods for representing modeled wave data using similar streamline-based designs. We asked subjects to rate the marine weather visualizations: the results revealed a preference for some of the new designs

    The Data Big Bang and the Expanding Digital Universe: High-Dimensional, Complex and Massive Data Sets in an Inflationary Epoch

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    Recent and forthcoming advances in instrumentation, and giant new surveys, are creating astronomical data sets that are not amenable to the methods of analysis familiar to astronomers. Traditional methods are often inadequate not merely because of the size in bytes of the data sets, but also because of the complexity of modern data sets. Mathematical limitations of familiar algorithms and techniques in dealing with such data sets create a critical need for new paradigms for the representation, analysis and scientific visualization (as opposed to illustrative visualization) of heterogeneous, multiresolution data across application domains. Some of the problems presented by the new data sets have been addressed by other disciplines such as applied mathematics, statistics and machine learning and have been utilized by other sciences such as space-based geosciences. Unfortunately, valuable results pertaining to these problems are mostly to be found only in publications outside of astronomy. Here we offer brief overviews of a number of concepts, techniques and developments, some "old" and some new. These are generally unknown to most of the astronomical community, but are vital to the analysis and visualization of complex datasets and images. In order for astronomers to take advantage of the richness and complexity of the new era of data, and to be able to identify, adopt, and apply new solutions, the astronomical community needs a certain degree of awareness and understanding of the new concepts. One of the goals of this paper is to help bridge the gap between applied mathematics, artificial intelligence and computer science on the one side and astronomy on the other.Comment: 24 pages, 8 Figures, 1 Table. Accepted for publication: "Advances in Astronomy, special issue "Robotic Astronomy
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