Single view vs. multiple views scatterplots

Among all the available visualization tools, the scatterplot has been deeply analyzed through the years and many researchers investigated how to improve this tool to face new challenges. The scatterplot visualization diagram is considered one of the most functional among the variety of data visual representations, due to its relative simplicity compared to other multivariable visualization techniques. Even so, one of the most significant and unsolved challenge in data visualization consists in effectively displaying datasets with many attributes or dimensions, such as multidimensional or multivariate ones. The focus of this research is to compare the single view and the multiple views visualization paradigms for displaying multivariable dataset using scatterplots. A multivariable scatterplot has been developed as a web application to provide the single view tool, whereas for the multiple views visualization, the ScatterDice web app has been slightly modified and adopted as a traditional, yet interactive, scatterplot matrix. Finally, a taxonomy of tasks for visualization tools has been chosen to define the use case and the tests to compare the two paradigms

Motion Segmentation Aided Super Resolution Image Reconstruction

This dissertation addresses Super Resolution (SR) Image Reconstruction focusing on motion segmentation. The main thrust is Information Complexity guided Gaussian Mixture Models (GMMs) for Statistical Background Modeling. In the process of developing our framework we also focus on two other topics; motion trajectories estimation toward global and local scene change detections and image reconstruction to have high resolution (HR) representations of the moving regions. Such a framework is used for dynamic scene understanding and recognition of individuals and threats with the help of the image sequences recorded with either stationary or non-stationary camera systems. We introduce a new technique called Information Complexity guided Statistical Background Modeling. Thus, we successfully employ GMMs, which are optimal with respect to information complexity criteria. Moving objects are segmented out through background subtraction which utilizes the computed background model. This technique produces superior results to competing background modeling strategies. The state-of-the-art SR Image Reconstruction studies combine the information from a set of unremarkably different low resolution (LR) images of static scene to construct an HR representation. The crucial challenge not handled in these studies is accumulating the corresponding information from highly displaced moving objects. In this aspect, a framework of SR Image Reconstruction of the moving objects with such high level of displacements is developed. Our assumption is that LR images are different from each other due to local motion of the objects and the global motion of the scene imposed by non-stationary imaging system. Contrary to traditional SR approaches, we employed several steps. These steps are; the suppression of the global motion, motion segmentation accompanied by background subtraction to extract moving objects, suppression of the local motion of the segmented out regions, and super-resolving accumulated information coming from moving objects rather than the whole scene. This results in a reliable offline SR Image Reconstruction tool which handles several types of dynamic scene changes, compensates the impacts of camera systems, and provides data redundancy through removing the background. The framework proved to be superior to the state-of-the-art algorithms which put no significant effort toward dynamic scene representation of non-stationary camera systems

DataLev: Mid-air Data Physicalisation Using Acoustic Levitation

Data physicalisation is a technique that encodes data through the geometric and material properties of an artefact, allowing users to engage with data in a more immersive and multi-sensory way. However, current methods of data physicalisation are limited in terms of their reconfgurability and the types of materials that can be used. Acoustophoresis—a method of suspending and manipulating materials using sound waves—ofers a promising solution to these challenges. In this paper, we present DataLev, a design space and platform for creating reconfgurable, multimodal data physicalisations with enriched materiality using acoustophoresis. We demonstrate the capabilities of DataLev through eight examples and evaluate its performance in terms of reconfgurability and materiality. Our work ofers a new approach to data physicalisation, enabling designers to create more dynamic, engaging, and expressive artefacts

Remote refocusing light-sheet fluorescence microscopy for high-speed 2D and 3D imaging of calcium dynamics in cardiomyocytes

The high prevalence and poor prognosis of heart failure are two key drivers for research into cardiac electrophysiology and regeneration. Dyssynchrony in calcium release and loss of structural organization within individual cardiomyocytes (CM) has been linked to reduced contractile strength and arrhythmia. Correlating calcium dynamics and cell microstructure requires multidimensional imaging with high spatiotemporal resolution. In light-sheet fluorescence microscopy (LSFM), selective plane illumination enables fast optically sectioned imaging with lower phototoxicity, making it suitable for imaging subcellular dynamics. In this work, a custom remote refocusing LSFM system is applied to studying calcium dynamics in isolated CM, cardiac cell cultures and tissue slices. The spatial resolution of the LSFM system was modelled and experimentally characterized. Simulation of the illumination path in Zemax was used to estimate the light-sheet beam waist and confocal parameter. Automated MATLAB-based image analysis was used to quantify the optical sectioning and the 3D point spread function using Gaussian fitting of bead image intensity distributions. The results demonstrated improved and more uniform axial resolution and optical sectioning with the tighter focused beam used for axially swept light-sheet microscopy. High-speed dual-channel LSFM was used for 2D imaging of calcium dynamics in correlation with the t-tubule structure in left and right ventricle cardiomyocytes at 395 fps. The high spatio-temporal resolution enabled the characterization of calcium sparks. The use of para-nitro-blebbistatin (NBleb), a non-phototoxic, low fluorescence contraction uncoupler, allowed 2D-mapping of the spatial dyssynchrony of calcium transient development across the cell. Finally, aberration-free remote refocusing was used for high-speed volumetric imaging of calcium dynamics in human induced pluripotent stem-cell derived cardiomyocytes (hiPSC-CM) and their co-culture with adult-CM. 3D-imaging at up to 8 Hz demonstrated the synchronization of calcium transients in co-culture, with increased coupling with longer co-culture duration, uninhibited by motion uncoupling with NBleb.Open Acces

Structure-aware image denoising, super-resolution, and enhancement methods

Denoising, super-resolution and structure enhancement are classical image processing applications. The motive behind their existence is to aid our visual analysis of raw digital images. Despite tremendous progress in these fields, certain difficult problems are still open to research. For example, denoising and super-resolution techniques which possess all the following properties, are very scarce: They must preserve critical structures like corners, should be robust to the type of noise distribution, avoid undesirable artefacts, and also be fast. The area of structure enhancement also has an unresolved issue: Very little efforts have been put into designing models that can tackle anisotropic deformations in the image acquisition process. In this thesis, we design novel methods in the form of partial differential equations, patch-based approaches and variational models to overcome the aforementioned obstacles. In most cases, our methods outperform the existing approaches in both quality and speed, despite being applicable to a broader range of practical situations.Entrauschen, Superresolution und Strukturverbesserung sind klassische Anwendungen der Bildverarbeitung. Ihre Existenz bedingt sich in dem Bestreben, die visuelle Begutachtung digitaler Bildrohdaten zu unterstützen. Trotz erheblicher Fortschritte in diesen Feldern bedürfen bestimmte schwierige Probleme noch weiterer Forschung. So sind beispielsweise Entrauschungsund Superresolutionsverfahren, welche alle der folgenden Eingenschaften besitzen, sehr selten: die Erhaltung wichtiger Strukturen wie Ecken, Robustheit bezüglich der Rauschverteilung, Vermeidung unerwünschter Artefakte und niedrige Laufzeit. Auch im Gebiet der Strukturverbesserung liegt ein ungelöstes Problem vor: Bisher wurde nur sehr wenig Forschungsaufwand in die Entwicklung von Modellen investieret, welche anisotrope Deformationen in bildgebenden Verfahren bewältigen können. In dieser Arbeit entwerfen wir neue Methoden in Form von partiellen Differentialgleichungen, patch-basierten Ansätzen und Variationsmodellen um die oben erwähnten Hindernisse zu überwinden. In den meisten Fällen übertreffen unsere Methoden nicht nur qualitativ die bisher verwendeten Ansätze, sondern lösen die gestellten Aufgaben auch schneller. Zudem decken wir mit unseren Modellen einen breiteren Bereich praktischer Fragestellungen ab

Visualising multiple overlapping classification hierarchies.

The revision or reorganisation of hierarchical data sets can result in many possible hierarchical classifications composed of the same or overlapping data sets existing in parallel with each other. These data sets are difficult for people to handle and conceptualise, as they tryto reconcile the different perspectives and structures that such data represents. One area where this situation occurs is the study of botanical taxonomy, essentially the classification and naming of plants. Revisions, new discoveries and new dimensions for classifying plants lead to a proliferation of classifications over the same set of plant data. Taxonomists would like a method of exploring these multiple overlapping hierarchies for interesting information, correlations, or anomalies. The application and extension of Information Visualisation (IV) techniques, the graphical display of abstract information, is put forward as a solution to this problem. Displaying the multiple classification hierarchies in a visually appealing manner along with powerful interaction mechanisms for examination and exploration of the data allows taxonomists to unearth previously hidden information. This visualisation gives detail that previous visualisations and statistical overviews cannot offer. This thesis work has extended previous IV work in several respects to achieve this goal. Compact, yet full and unambiguous, hierarchy visualisations have been developed. Linking and brushing techniques have been extended to work on a higher class of structure, namely overlapping trees and hierarchies. Focus and context techniques have been pushed to achieve new effects across the visually distinct representations of these multiple hierarchies. Other data types, such as multidimensional data and large cluster hierarchies have also been displayed using the final version of the visualisation