Automatic interpretation of map visualizations with color-encoded scalar values from bitmap images

Las visualizaciones de mapas son usadas en diferentes a ‘reas para mostrar datos geográficos (por ejemplo, datos climatológicos u oceanográficos, resultados de análisis empresariales, entre otros). Estas visualizaciones se pueden encontrar en artículos de noticias, artículos científicos y en la Web; sin embargo, muchas de ellas están disponibles como imágenes en mapa de bits, lo que dificulta que el computador interprete los datos visualizados para su indexación y reutilización. En este trabajo proponemos una secuencia de pasos para recuperar la codificación visual a partir de imágenes en mapa de bits de mapas geográficos que utilizan el color para codificar los valores de los datos. Nuestros resultados fueron analizados usando mapas extraídos de documentos científicos, logrando una alta precisión en cada paso propuesto. Adicionalmente presentamos a iGeoMap, nuestro sistema web que utiliza la codificación visual extraída para permitir la interacción del usuario sobre imágenes en mapa de bits de visualizaciones de mapas.Trabajo de investigació

Tangible Scalar Fields

Data Visualization is a field that explores how to most efficiently convey information to the user, most often via visual representations like plots, graphs or glyphs. While this field of research has had great growth within the last couple of years, most of the work has been focused on the visual part of the human visual and auditory system - much less visualization work has been done in regards to the visually impaired. In this thesis, we will look at some previous methods and techniques for visualizing scalar fields via the sense of touch, and additionally provide two novel approaches to visualize a two-dimensional scalar field. Our first approach creates passive physicalizations from a scalar field in a semi-automatic pipeline by encoding the scalar value and field coordinates as positions in 3D space, which we use to construct a triangular mesh built from hexagonal pillars that can be printed on a 3D printer. We further enhance our mesh by encoding a directional attribute on the pillars, creating a visual encoding of the model orientation and improving upon a readability issue by mirroring the mesh. Our second approach uses a haptic force-feedback device to simulate the feeling of moving across a surface based on the scalar field by replicating three physical forces: the normal force, the friction force and the gravity force. We also further extend our approach by introducing a local encoding of global information about the scalar field via a volume representation build from the scalar field.Masteroppgave i informatikkINF399MAMN-PROGMAMN-IN

Visual Exploration And Information Analytics Of High-Dimensional Medical Images

Data visualization has transformed how we analyze increasingly large and complex data sets. Advanced visual tools logically represent data in a way that communicates the most important information inherent within it and culminate the analysis with an insightful conclusion. Automated analysis disciplines - such as data mining, machine learning, and statistics - have traditionally been the most dominant fields for data analysis. It has been complemented with a near-ubiquitous adoption of specialized hardware and software environments that handle the storage, retrieval, and pre- and postprocessing of digital data. The addition of interactive visualization tools allows an active human participant in the model creation process. The advantage is a data-driven approach where the constraints and assumptions of the model can be explored and chosen based on human insight and confirmed on demand by the analytic system. This translates to a better understanding of data and a more effective knowledge discovery. This trend has become very popular across various domains, not limited to machine learning, simulation, computer vision, genetics, stock market, data mining, and geography. In this dissertation, we highlight the role of visualization within the context of medical image analysis in the field of neuroimaging. The analysis of brain images has uncovered amazing traits about its underlying dynamics. Multiple image modalities capture qualitatively different internal brain mechanisms and abstract it within the information space of that modality. Computational studies based on these modalities help correlate the high-level brain function measurements with abnormal human behavior. These functional maps are easily projected in the physical space through accurate 3-D brain reconstructions and visualized in excellent detail from different anatomical vantage points. Statistical models built for comparative analysis across subject groups test for significant variance within the features and localize abnormal behaviors contextualizing the high-level brain activity. Currently, the task of identifying the features is based on empirical evidence, and preparing data for testing is time-consuming. Correlations among features are usually ignored due to lack of insight. With a multitude of features available and with new emerging modalities appearing, the process of identifying the salient features and their interdependencies becomes more difficult to perceive. This limits the analysis only to certain discernible features, thus limiting human judgments regarding the most important process that governs the symptom and hinders prediction. These shortcomings can be addressed using an analytical system that leverages data-driven techniques for guiding the user toward discovering relevant hypotheses. The research contributions within this dissertation encompass multidisciplinary fields of study not limited to geometry processing, computer vision, and 3-D visualization. However, the principal achievement of this research is the design and development of an interactive system for multimodality integration of medical images. The research proceeds in various stages, which are important to reach the desired goal. The different stages are briefly described as follows: First, we develop a rigorous geometry computation framework for brain surface matching. The brain is a highly convoluted structure of closed topology. Surface parameterization explicitly captures the non-Euclidean geometry of the cortical surface and helps derive a more accurate registration of brain surfaces. We describe a technique based on conformal parameterization that creates a bijective mapping to the canonical domain, where surface operations can be performed with improved efficiency and feasibility. Subdividing the brain into a finite set of anatomical elements provides the structural basis for a categorical division of anatomical view points and a spatial context for statistical analysis. We present statistically significant results of our analysis into functional and morphological features for a variety of brain disorders. Second, we design and develop an intelligent and interactive system for visual analysis of brain disorders by utilizing the complete feature space across all modalities. Each subdivided anatomical unit is specialized by a vector of features that overlap within that element. The analytical framework provides the necessary interactivity for exploration of salient features and discovering relevant hypotheses. It provides visualization tools for confirming model results and an easy-to-use interface for manipulating parameters for feature selection and filtering. It provides coordinated display views for visualizing multiple features across multiple subject groups, visual representations for highlighting interdependencies and correlations between features, and an efficient data-management solution for maintaining provenance and issuing formal data queries to the back end

Doctor of Philosophy

dissertationCorrelation is a powerful relationship measure used in many fields to estimate trends and make forecasts. When the data are complex, large, and high dimensional, correlation identification is challenging. Several visualization methods have been proposed to solve these problems, but they all have limitations in accuracy, speed, or scalability. In this dissertation, we propose a methodology that provides new visual designs that show details when possible and aggregates when necessary, along with robust interactive mechanisms that together enable quick identification and investigation of meaningful relationships in large and high-dimensional data. We propose four techniques using this methodology. Depending on data size and dimensionality, the most appropriate visualization technique can be provided to optimize the analysis performance. First, to improve correlation identification tasks between two dimensions, we propose a new correlation task-specific visualization method called correlation coordinate plot (CCP). CCP transforms data into a powerful coordinate system for estimating the direction and strength of correlations among dimensions. Next, we propose three visualization designs to optimize correlation identification tasks in large and multidimensional data. The first is snowflake visualization (Snowflake), a focus+context layout for exploring all pairwise correlations. The next proposed design is a new interactive design for representing and exploring data relationships in parallel coordinate plots (PCPs) for large data, called data scalable parallel coordinate plots (DSPCP). Finally, we propose a novel technique for storing and accessing the multiway dependencies through visualization (MultiDepViz). We evaluate these approaches by using various use cases, compare them to prior work, and generate user studies to demonstrate how our proposed approaches help users explore correlation in large data efficiently. Our results confirmed that CCP/Snowflake, DSPCP, and MultiDepViz methods outperform some current visualization techniques such as scatterplots (SCPs), PCPs, SCP matrix, Corrgram, Angular Histogram, and UntangleMap in both accuracy and timing. Finally, these approaches are applied in real-world applications such as a debugging tool, large-scale code performance data, and large-scale climate data

Visual encoding quality and scalability in information visualization

Information visualization seeks to amplify cognition through interactive visual representations of data. It comprises human processes, such as perception and cognition, and computer processes, such as visual encoding. Visual encoding consists in mapping data variables to visual variables, and its quality is critical to the effectiveness of information visualizations. The scalability of a visual encoding is the extent to which its quality is preserved as the parameters of the data grow. Scalable encodings offer good support for basic analytical tasks at scale by carrying design decisions that consider the limits of human perception and cognition. In this thesis, I present three case studies that explore different aspects of visual encoding quality and scalability: information loss, perceptual scalability, and discriminability. In the first study, I leverage information theory to model encoding quality in terms of information content and complexity. I examine how information loss and clutter affect the scalability of hierarchical visualizations and contribute an information-theoretic algorithm for adjusting these factors in visualizations of large datasets. The second study centers on the question of whether a data property (outlierness) can be lost in the visual encoding process due to saliency interference with other visual variables. I designed a controlled experiment to measure the effectiveness of motion outlier detection in complex multivariate scatterplots. The results suggest a saliency deficit effect whereby global saliency undermines support to tasks that rely on local saliency. Finally, I investigate how discriminability, a classic visualization criterion, can explain recent empirical results on encoding effectiveness and provide the foundation for automated evaluation of visual encodings. I propose an approach for discriminability evaluation based on a perceptually motivated image similarity measure

Perception, Cognition, and Effectiveness of Visualizations with Applications in Science and Engineering

Visualization is a powerful tool for data exploration and analysis. With data ever-increasing in quantity and becoming integrated into our daily lives, having effective visualizations is necessary. But how does one design an effective visualization? To answer this question we need to understand how humans perceive, process, and understand visualizations. Through visualization evaluation studies we can gain deeper insight into the basic perception and cognition theory of visualizations, both through domain-specific case studies as well as generalized laboratory experiments.Engineering and Applied Science