SlicerAstro: a 3-D interactive visual analytics tool for HI data

SKA precursors are capable of detecting hundreds of galaxies in HI in a single 12 hours pointing. In deeper surveys one will probe more easily faint HI structures, typically located in the vicinity of galaxies, such as tails, filaments, and extraplanar gas. The importance of interactive visualization has proven to be fundamental for the exploration of such data as it helps users to receive immediate feedback when manipulating the data. We have developed SlicerAstro, a 3-D interactive viewer with new analysis capabilities, based on traditional 2-D input/output hardware. These capabilities enhance the data inspection, allowing faster analysis of complex sources than with traditional tools. SlicerAstro is an open-source extension of 3DSlicer, a multi-platform open source software package for visualization and medical image processing. We demonstrate the capabilities of the current stable binary release of SlicerAstro, which offers the following features: i) handling of FITS files and astronomical coordinate systems; ii) coupled 2-D/3-D visualization; iii) interactive filtering; iv) interactive 3-D masking; v) and interactive 3-D modeling. In addition, SlicerAstro has been designed with a strong, stable and modular C++ core, and its classes are also accessible via Python scripting, allowing great flexibility for user-customized visualization and analysis tasks.Comment: 18 pages, 11 figures, Accepted by Astronomy and Computing. SlicerAstro link: https://github.com/Punzo/SlicerAstro/wiki#get-slicerastr

Learning Equivariant Representations

State-of-the-art deep learning systems often require large amounts of data and computation. For this reason, leveraging known or unknown structure of the data is paramount. Convolutional neural networks (CNNs) are successful examples of this principle, their defining characteristic being the shift-equivariance. By sliding a filter over the input, when the input shifts, the response shifts by the same amount, exploiting the structure of natural images where semantic content is independent of absolute pixel positions. This property is essential to the success of CNNs in audio, image and video recognition tasks. In this thesis, we extend equivariance to other kinds of transformations, such as rotation and scaling. We propose equivariant models for different transformations defined by groups of symmetries. The main contributions are (i) polar transformer networks, achieving equivariance to the group of similarities on the plane, (ii) equivariant multi-view networks, achieving equivariance to the group of symmetries of the icosahedron, (iii) spherical CNNs, achieving equivariance to the continuous 3D rotation group, (iv) cross-domain image embeddings, achieving equivariance to 3D rotations for 2D inputs, and (v) spin-weighted spherical CNNs, generalizing the spherical CNNs and achieving equivariance to 3D rotations for spherical vector fields. Applications include image classification, 3D shape classification and retrieval, panoramic image classification and segmentation, shape alignment and pose estimation. What these models have in common is that they leverage symmetries in the data to reduce sample and model complexity and improve generalization performance. The advantages are more significant on (but not limited to) challenging tasks where data is limited or input perturbations such as arbitrary rotations are present

Interactive multiview information visualization in tablet-sized touch devices for scientific data

In this thesis we describe an exploratory visualization system for scientific data using multiple views in tablet-sized touch devices. The increasing ubiquity of mobile devices and vast amount of data generation create a need for such exploration environment. We make the data analysis available anywhere at anytime with our approach. This thesis includes an interaction framework for scientific data and a spatial selection technique based on transfer functions. We then combine the interaction and selection with multiple information visualization views to make the data exploration possible. The proposed approach is one of the first multiview exploratory visualizations in tablet devices

Data Visualization on Interactive Surfaces: A Research Agenda

International audienceInteractive tabletops and surfaces provide rich opportunities for data visualization and analysis, and consequently are used increasingly in such settings. In this article we discuss the potential benefits of using interactive surface platforms for visualization applications and present a research agenda of some of the most pressing research challenges in this space. The agenda emerged from discussions with researchers and practitioners in human-computer interaction, computer-supported collaborative work, and a wide variety of visualization fields at the DEXIS 2011 workshop on "Data Exploration for Interactive Surface

Interactive Visual Analytics for Large-scale Particle Simulations

Particle based model simulations are widely used in scientific visualization. In cosmology, particles are used to simulate the evolution of dark matter in the universe. Clusters of particles (that have special statistical properties) are called halos. From a visualization point of view, halos are clusters of particles, each having a position, mass and velocity in three dimensional space, and they can be represented as point clouds that contain various structures of geometric interest such as filaments, membranes, satellite of points, clusters, and cluster of clusters. The thesis investigates methods for interacting with large scale data-sets represented as point clouds. The work mostly aims at the interactive visualization of cosmological simulation based on large particle systems. The study consists of three components: a) two human factors experiments into the perceptual factors that make it possible to see features in point clouds; b) the design and implementation of a user interface making it possible to rapidly navigate through and visualize features in the point cloud, c) software development and integration to support visualization

Eliciting Multi-touch Selection Gestures for Interactive Data Graphics

International audienceWe report the results of a study in which we elicited selection gestures for multi-touch data graphics. The selection of data items is a common and extremely important form of interaction with data graphics, and serves as the basis for many other data interaction techniques. However, interactive charting tools for multi-touch displays typically only provide dedicated multi-touch gestures for single-point selection or zooming. Our study used gesture elicitation to explore a wider range of possible selection interactions for multi-touch data graphics. The results show a strong preference for simple, one-handed selection gestures. They also show that users tend to interact with chart axes and make figurative selection gestures outside the chart, rather than interact with the visual marks themselves. Finally, we found strong consensus around several unique selection gestures related to visual chart features