EXTRACTING FLOW FEATURES USING BAG-OF-FEATURES AND SUPERVISED LEARNING TECHNIQUES

Measuring the similarity between two streamlines is fundamental to many important flow data analysis and visualization tasks such as feature detection, pattern querying and streamline clustering. This dissertation presents a novel streamline similarity measure inspired by the bag-of-features concept from computer vision. Different from other streamline similarity measures, the proposed one considers both the distribution of and the distances among features along a streamline. The proposed measure is tested in two common tasks in vector field exploration: streamline similarity query and streamline clustering. Compared with a recent streamline similarity measure, the proposed measure allows users to see the interesting features more clearly in a complicated vector field. In addition to focusing on similar streamlines through streamline similarity query or clustering, users sometimes want to group and see similar features from different streamlines. For example, it is useful to find all the spirals contained in different streamlines and present them to users. To this end, this dissertation proposes to segment each streamline into different features. This problem has not been studied extensively in flow visualization. For instance, many flow feature extraction techniques segment streamline based on simple heuristics such as accumulative curvature or arc length, and, as a result, the segments they found usually do not directly correspond to complete flow features. This dissertation proposes a machine learning-based streamline segmentation algorithm to segment each streamline into distinct features. It is shown that the proposed method can locate interesting features (e.g., a spiral in a streamline) more accurately than some other flow feature extraction methods. Since streamlines are space curves, the proposed method also serves as a general curve segmentation method and may be applied in other fields such as computer vision. Besides flow visualization, a pedagogical visualization tool DTEvisual for teaching access control is also discussed in this dissertation. Domain Type Enforcement (DTE) is a powerful abstraction for teaching students about modern models of access control in operating systems. With DTEvisual, students have an environment for visualizing a DTE-based policy using graphs, visually modifying the policy, and animating the common DTE queries in real time. A user study of DTEvisual suggests that the tool is helpful for students to understand DTE

ENABLING TECHNIQUES FOR EXPRESSIVE FLOW FIELD VISUALIZATION AND EXPLORATION

Flow visualization plays an important role in many scientific and engineering disciplines such as climate modeling, turbulent combustion, and automobile design. The most common method for flow visualization is to display integral flow lines such as streamlines computed from particle tracing. Effective streamline visualization should capture flow patterns and display them with appropriate density, so that critical flow information can be visually acquired. In this dissertation, we present several approaches that facilitate expressive flow field visualization and exploration. First, we design a unified information-theoretic framework to model streamline selection and viewpoint selection as symmetric problems. Two interrelated information channels are constructed between a pool of candidate streamlines and a set of sample viewpoints. Based on these information channels, we define streamline information and viewpoint information to select best streamlines and viewpoints, respectively. Second, we present a focus+context framework to magnify small features and reduce occlusion around them while compacting the context region in a full view. This framework parititions the volume into blocks and deforms them to guide streamline repositioning. The desired deformation is formulated into energy terms and achieved by minimizing the energy function. Third, measuring the similarity of integral curves is fundamental to many tasks such as feature detection, pattern querying, streamline clustering and hierarchical exploration. We introduce FlowString that extracts shape invariant features from streamlines to form an alphabet of characters, and encodes each streamline into a string. The similarity of two streamline segments then becomes a specially designed edit distance between two strings. Leveraging the suffix tree, FlowString provides a string-based method for exploratory streamline analysis and visualization. A universal alphabet is learned from multiple data sets to capture basic flow patterns that exist in a variety of flow fields. This allows easy comparison and efficient query across data sets. Fourth, for exploration of vascular data sets, which contain a series of vector fields together with multiple scalar fields, we design a web-based approach for users to investigate the relationship among different properties guided by histograms. The vessel structure is mapped from the 3D volume space to a 2D graph, which allow more efficient interaction and effective visualization on websites. A segmentation scheme is proposed to divide the vessel structure based on a user specified property to further explore the distribution of that property over space

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

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