Showing papers on "Graphics published in 2016"

Handbook of Data Visualization

Abstract: Visualizing the data is an essential part of any data analysis. Modern computing developments have led to big improvements in graphic capabilities and there are many new possibilities for data displays. This book gives an overview of modern data visualization methods, both in theory and practice. It details modern graphical tools such as mosaic plots, parallel coordinate plots, and linked views. Coverage also examines graphical methodology for particular areas of statistics, for example Bayesian analysis, genomic data and cluster analysis, as well software for graphics.

Does animation enhance learning? A meta-analysis

Abstract: This meta-analysis investigated whether animation is beneficial overall for learning compared to static graphics, while also identifying moderator factors affecting the global effect. A systematic search was conducted for experimental studies comparing the impact of animated vs. static graphics displays in the context of knowledge acquisition. A total of 50 papers were considered, and consecutively 61 primary studies (N = 7036), yielding 140 pair-wise comparisons of animated vs. static graphic visualizations in multimedia instructional material were analyzed using a random-effects model. An overall positive effect of animation over static graphics was found, with a Hedges’s g effect size of 0.226 (95% confidence interval = 0.12–0.33). Additional moderator analyses indicated substantial effect sizes when the animation was system-paced (g = 0.309), when it was coupled with auditory commentary (g = 0.336) or when the instruction did not include any accompanying text (g = 0.883).

Directional field synthesis, design, and processing

Abstract: Direction fields and vector fields play an increasingly important role in computer graphics and geometry processing. The synthesis of directional fields on surfaces, or other spatial domains, is a fundamental step in numerous applications, such as mesh generation, deformation, texture mapping, and many more. The wide range of applications resulted in definitions for many types of directional fields: from vector and tensor fields, over line and cross fields, to frame and vector-set fields. Depending on the application at hand, researchers have used various notions of objectives and constraints to synthesize such fields. These notions are defined in terms of fairness, feature alignment, symmetry, or field topology, to mention just a few. To facilitate these objectives, various representations, discretizations, and optimization strategies have been developed. These choices come with varying strengths and weaknesses. This report provides a systematic overview of directional field synthesis for graphics applications, the challenges it poses, and the methods developed in recent years to address these challenges.

3DMatch: Learning the Matching of Local 3D Geometry in Range Scans.

Abstract: Establishing correspondences between 3D geometries is essential to a large variety of graphics and vision applications, including 3D reconstruction, localization, and shape matching. Despite significant progress, geometric matching on real-world 3D data is still a challenging task due to the noisy, low-resolution, and incomplete nature of scanning data. These difficulties limit the performance of current state-of-art methods which are typically based on histograms over geometric properties. In this paper, we introduce 3DMatch, a data-driven local feature learner that jointly learns a geometric feature representation and an associated metric function from a large collection of real-world scanning data. We represent 3D geometry using accumulated distance fields around key-point locations. This representation is suited to handle noisy and partial scanning data, and concurrently supports deep learning with convolutional neural networks directly in 3D. To train the networks, we propose a way to automatically generate correspondence labels for deep learning by leveraging existing RGB-D reconstruction algorithms. In our results, we demonstrate that we are able to outperform state-of-the-art approaches by a significant margin. In addition, we show the robustness of our descriptor in a purely geometric sparse bundle adjustment pipeline for 3D reconstruction.

Directional field synthesis, design, and processing

Abstract: Direction fields and vector fields play an increasingly important role in computer graphics and geometry processing. The synthesis of directional fields on surfaces, or other spatial domains, is a fundamental step in numerous applications, such as mesh generation, deformation, texture mapping, and many more. The wide range of applications resulted in definitions for many types of directional fields: from vector and tensor fields, over line and cross fields, to frame and vector-set fields. Depending on the application at hand, researchers have used various notions of objectives and constraints to synthesize such fields. These notions are defined in terms of fairness, feature alignment, symmetry, or field topology, to mention just a few. To facilitate these objectives, various representations, discretizations, and optimization strategies have been developed. These choices come with varying strengths and weaknesses. This course provides a systematic overview of directional field synthesis for graphics applications, the challenges it poses, and the methods developed in recent years to address these challenges.

Deep Active Contours

Abstract: We propose a method for interactive boundary extraction which combines a deep, patch-based representation with an active contour framework. We train a class-specific convolutional neural network which predicts a vector pointing from the respective point on the evolving contour towards the closest point on the boundary of the object of interest. These predictions form a vector field which is then used for evolving the contour by the Sobolev active contour framework proposed by Sundaramoorthi et al. The resulting interactive segmentation method is very efficient in terms of required computational resources and can even be trained on comparatively small graphics cards. We evaluate the potential of the proposed method on both medical and non-medical challenge data sets, such as the STACOM data set and the PASCAL VOC 2012 data set.

From Static to Interactive: Transforming Data Visualization to Improve Transparency.

Abstract: Data presentation for scientific publications in small sample size studies has not changed substantially in decades. It relies on static figures and tables that may not provide sufficient information for critical evaluation, particularly of the results from small sample size studies. Interactive graphics have the potential to transform scientific publications from static reports of experiments into interactive datasets. We designed an interactive line graph that demonstrates how dynamic alternatives to static graphics for small sample size studies allow for additional exploration of empirical datasets. This simple, free, web-based tool (http://statistika.mfub.bg.ac.rs/interactive-graph/) demonstrates the overall concept and may promote widespread use of interactive graphics.

Teaching and Learning Data Visualization: Ideas and Assignments

Abstract: This article discusses how to make statistical graphics a more prominent element of the undergraduate statistics curricula The focus is on several different types of assignments that exemplify how to incorporate graphics into a course in a pedagogically meaningful way These assignments include having students deconstruct and reconstruct plots, copy masterful graphs, create one-minute visual revelations, convert tables into “pictures,” and develop interactive visualizations, for example, with the virtual earth as a plotting canvas In addition to describing the goals and details of each assignment, we also discuss the broader topic of graphics and key concepts that we think warrant inclusion in the statistics curricula We advocate that more attention needs to be paid to this fundamental field of statistics at all levels, from introductory undergraduate through graduate level courses With the rapid rise of tools to visualize data, for example, Google trends, GapMinder, ManyEyes, and Tableau, and

Recent Advances in Transient Imaging: A Computer Graphics and Vision Perspective

Abstract: Transient imaging has recently made a huge impact in the computer graphics and computer vision fields. By capturing, reconstructing, or simulating light transport at extreme temporal resolutions, researchers have proposed novel techniques to show movies of light in motion, see around corners, detect objects in highly-scattering media, or infer material properties from a distance, to name a few. The key idea is to leverage the wealth of information in the temporal domain at the pico or nanosecond resolution, information usually lost during the capture-time temporal integration. This paper presents recent advances in this field of transient imaging from a graphics and vision perspective, including capture techniques, analysis, applications and simulation.

A Dataset for Visual Navigation with Neuromorphic Methods

Abstract: Standardized benchmarks in Computer Vision have greatly contributed to the advance of approaches to many problems in the field. If we want to enhance the visibility of event-driven vision and increase its impact, we will need benchmarks that allow comparison among different neuromorphic methods as well as comparison to Computer Vision conventional approaches. We present datasets to evaluate the accuracy of frame-free and frame-based approaches for tasks of visual navigation. Similar to conventional Computer Vision datasets, we provide synthetic and real scenes, with the synthetic data created with graphics packages, and the real data recorded using a mobile robotic platform carrying a dynamic and active pixel vision sensor (DAVIS) and an RGB+Depth sensor. For both datasets the cameras move with a rigid motion in a static scene, and the data includes the images, events, optic flow, 3D camera motion, and the depth of the scene, along with calibration procedures. Finally, we also provide simulated event data generated synthetically from well-known frame-based optical flow datasets.

GUIdock: Using Docker Containers with a Common Graphics User Interface to Address the Reproducibility of Research.

Abstract: Reproducibility is vital in science. For complex computational methods, it is often necessary, not just to recreate the code, but also the software and hardware environment to reproduce results. Virtual machines, and container software such as Docker, make it possible to reproduce the exact environment regardless of the underlying hardware and operating system. However, workflows that use Graphical User Interfaces (GUIs) remain difficult to replicate on different host systems as there is no high level graphical software layer common to all platforms. GUIdock allows for the facile distribution of a systems biology application along with its graphics environment. Complex graphics based workflows, ubiquitous in systems biology, can now be easily exported and reproduced on many different platforms. GUIdock uses Docker, an open source project that provides a container with only the absolutely necessary software dependencies and configures a common X Windows (X11) graphic interface on Linux, Macintosh and Windows platforms. As proof of concept, we present a Docker package that contains a Bioconductor application written in R and C++ called networkBMA for gene network inference. Our package also includes Cytoscape, a java-based platform with a graphical user interface for visualizing and analyzing gene networks, and the CyNetworkBMA app, a Cytoscape app that allows the use of networkBMA via the user-friendly Cytoscape interface.

Automatic graphic logo detection via Fast Region-based Convolutional Networks

Abstract: Brand recognition is a very challenging topic with many useful applications in localization recognition, advertisement and marketing. In this paper we present an automatic graphic logo detection system that robustly handles unconstrained imaging conditions. Our approach is based on Fast Region-based Convolutional Networks (FRCN) proposed by Ross Girshick, which have shown state-of-the-art performance in several generic object recognition tasks (PASCAL Visual Object Classes challenges). In particular, we use two CNN models pretrained with the ILSVRC ImageNet dataset and we look at the selective search of windows ‘proposals’ in the pre-processing stage and data augmentation to enhance the logo recognition rate. The novelty lies in the use of transfer learning to leverage powerful Convolutional Neural Network models trained with large-scale datasets and repurpose them in the context of graphic logo detection. Another benefit of this framework is that it allows for multiple detections of graphic logos using regions that are likely to have an object. Experimental results with the FlickrLogos-32 dataset show not only the promising performance of our developed models with respect to noise and other transformations a graphic logo can be subject to, but also its superiority over state-of-the-art systems with hand-crafted models and features.

Getting Started with ggplot2

Abstract: The goal of this chapter is to teach you how to produce useful graphics with ggplot2 as quickly as possible. You’ll learn the basics of ggplot() along with some useful “recipes” to make the most important plots. ggplot() allows you to make complex plots with just a few lines of code because it’s based on a rich underlying theory, the grammar of graphics. Here we’ll skip the theory and focus on the practice, and in later chapters you’ll learn how to use the full expressive power of the grammar.

Unsupervised texture transfer from images to model collections

Abstract: Large 3D model repositories of common objects are now ubiquitous and are increasingly being used in computer graphics and computer vision for both analysis and synthesis tasks. However, images of objects in the real world have a richness of appearance that these repositories do not capture, largely because most existing 3D models are untextured. In this work we develop an automated pipeline capable of transporting texture information from images of real objects to 3D models of similar objects. This is a challenging problem, as an object's texture as seen in a photograph is distorted by many factors, including pose, geometry, and illumination. These geometric and photometric distortions must be undone in order to transfer the pure underlying texture to a new object --- the 3D model. Instead of using problematic dense correspondences, we factorize the problem into the reconstruction of a set of base textures (materials) and an illumination model for the object in the image. By exploiting the geometry of the similar 3D model, we reconstruct certain reliable texture regions and correct for the illumination, from which a full texture map can be recovered and applied to the model. Our method allows for large-scale unsupervised production of richly textured 3D models directly from image data, providing high quality virtual objects for 3D scene design or photo editing applications, as well as a wealth of data for training machine learning algorithms for various inference tasks in graphics and vision.

Gaze and saccade based graphical manipulation

Abstract: According to the invention, a system for presenting graphics on a display device is disclosed. The system may include an eye tracking device and a graphics processing device. The eye tracking device may be for determining a gaze point of a user on a display device. The graphics processing device may be for causing graphics to be displayed on the display device. The graphics displayed on the display device may be modified such that the graphics are of higher quality in an area including the gaze point of the user, than outside the area.

LiveRender: A Cloud Gaming System Based on Compressed Graphics Streaming

Abstract: In cloud gaming systems, the game program runs at servers in the cloud, while clients access game services by sending input events to the servers and receiving game scenes via video streaming. In this paradigm, servers are responsible for all performance-intensive operations, and thus suffer from poor scalability. An alternative paradigm is called graphics streaming, in which graphics commands and data are offloaded to the clients for local rendering, thereby mitigating the server’s burden and allowing more concurrent game sessions. Unfortunately, this approach is bandwidth-consuming, due to large amounts of graphic commands and geometry data. In this paper, we present LiveRender, an open-source gaming system that remedies the problem by implementing a suite of bandwidth optimization techniques including intraframe compression, interframe compression, and caching, establishing what we call compressed graphics streaming. Experiments results show that the new approach is able to reduce bandwidth consumption by 52%–73% compared to raw graphics streaming, with no perceptible difference in video quality and reduced response delay. Compared to the video streaming approach, LiveRender achieves a traffic reduction of 40%–90% with even improved video quality and substantially smaller response delay, while enabling higher concurrency at the server.

Axodraw Version 2

Abstract: We present version two of the Latex graphical style file Axodraw. It has a number of new drawing primitives and many extra options, and it can now work with \program{pdflatex} to directly produce output in PDF file format (but with the aid of an auxiliary program).

Deepstereo: learning to predict new views from real world imagery

Abstract: A system and method of deep learning using deep networks to predict new views from existing images may generate and improve models and representations from large-scale data. This system and method of deep learning may employ a deep architecture performing new view synthesis directly from pixels, trained from large numbers of posed image sets. A system employing this type of deep network may produce pixels of an unseen view based on pixels of neighboring views, lending itself to applications in graphics generation.

Proxy-guided image-based rendering for mobile devices

Abstract: VR headsets and hand-held devices are not powerful enough to render complex scenes in real-time. A server can take on the rendering task, but network latency prohibits a good user experience. We present a new image-based rendering (IBR) architecture for masking the latency. It runs in real-time even on very weak mobile devices, supports modern game engine graphics, and maintains high visual quality even for large view displacements. We propose a novel server-side dual-view representation that leverages an optimally-placed extra view and depth peeling to provide the client with coverage for filling disocclusion holes. This representation is directly rendered in a novel wide-angle projection with favorable directional parameterization. A new client-side IBR algorithm uses a pre-transmitted level-of-detail proxy with an encaging simplification and depth-carving to maintain highly complex geometric detail. We demonstrate our approach with typical VR / mobile gaming applications running on mobile hardware. Our technique compares favorably to competing approaches according to perceptual and numerical comparisons.

gScale: scaling up GPU virtualization with dynamic sharing of graphics memory space

Abstract: With increasing GPU-intensive workloads deployed on cloud, the cloud service providers are seeking for practical and efficient GPU virtualization solutions. However, the cutting-edge GPU virtualization techniques such as gVirt still suffer from the restriction of scalability, which constrains the number of guest virtual GPU instances. This paper introduces gScale, a scalable GPU virtualization solution. By taking advantage of the GPU programming model, gScale presents a dynamic sharing mechanism which combines partition and sharing together to break the hardware limitation of global graphics memory space. Particularly, we propose three approaches for gScale: (1) the private shadow graphics translation table, which enables global graphics memory space sharing among virtual GPU instances, (2) ladder mapping and fence memory space pool, which allows the CPU to access host physical memory space (serving the graphics memory) bypassing global graphics memory space, (3) slot sharing, which improves the performance of vGPU under a high density of instances. The evaluation shows that gScale scales up to 15 guest virtual GPU instances in Linux or 12 guest virtual GPU instances in Windows, which is 5x and 4x scalability, respectively, compared to gVirt. At the same time, gScale incurs a slight runtime overhead on the performance of gVirt when hosting multiple virtual GPU instances.

Device, system, and method for hybrid media content distribution

Abstract: A device, system, and method synthesizes personalized linear television experiences from on-demand assets, live event video, streaming graphics, and dynamic ad insertion, where portions of the broadcast day are scheduled for globally shared viewing events, where other portions are scheduled for distinct programming for various audience segments. Vector graphics are streamed independently of the video and rasterized locally to improve quality and contextualization of the graphics layer while gaining the efficiency of leveraging the same cached video assets for linear and on-demand applications. The system includes origination services transforming and publishing linear television schedules, video, and graphics to a distribution cache, a live event server streaming live performances, an advertising server providing targeted advertising, a personalization server, and assembly services stitching the elements to create a continuous experience of video and graphics for a media player to receive and render for each channel selected by the viewer.

VennDiagramWeb: a web application for the generation of highly customizable Venn and Euler diagrams.

Abstract: Visualization of data generated by high-throughput, high-dimensionality experiments is rapidly becoming a rate-limiting step in computational biology. There is an ongoing need to quickly develop high-quality visualizations that can be easily customized or incorporated into automated pipelines. This often requires an interface for manual plot modification, rapid cycles of tweaking visualization parameters, and the generation of graphics code. To facilitate this process for the generation of highly-customizable, high-resolution Venn and Euler diagrams, we introduce VennDiagramWeb: a web application for the widely used VennDiagram R package. VennDiagramWeb is hosted at http://venndiagram.res.oicr.on.ca/ . VennDiagramWeb allows real-time modification of Venn and Euler diagrams, with parameter setting through a web interface and immediate visualization of results. It allows customization of essentially all aspects of figures, but also supports integration into computational pipelines via download of R code. Users can upload data and download figures in a range of formats, and there is exhaustive support documentation. VennDiagramWeb allows the easy creation of Venn and Euler diagrams for computational biologists, and indeed many other fields. Its ability to support real-time graphics changes that are linked to downloadable code that can be integrated into automated pipelines will greatly facilitate the improved visualization of complex datasets. For application support please contact Paul.Boutros@oicr.on.ca.

Magic Touch: Interacting with 3D Printed Graphics

Abstract: Graphics like maps and models are important learning materials. With recently developed projects, we can use 3D printers to make tactile graphics that are more accessible to blind people. However, current 3D printed graphics can only convey limited information through their shapes and textures. We present Magic Touch, a computer vision-based system that augments printed graphics with audio files associated with specific locations, or hotspots, on the model. A user can access an audio file associated with a hotspot by touching it with a pointing gesture. The system detects the user's gesture and determines the hotspot location with computer vision algorithms by comparing a video feed of the user's interaction with the digital representation of the model and its hotspots. To enable MT, a model designer must add a single tracker with fiducial tags to a model. After the tracker is added, MT only requires an RGB camera, so it can be easily deployed on many devices such as mobile phones, laptops and smart glasses.

Numerical Model of Shallow Water: The Use of NVIDIA CUDA Graphics Processors

Abstract: In the paper we discuss the main features of the software package for numerical simulations of the surface water dynamics. We consider an approximation of the shallow water equations together with the parallel technologies for NVIDIA CUDA graphics processors. The numerical hydrodynamic code is based on the combined Lagrangian-Euler method (CSPH-TVD). We focused on the features of the parallel implementation of Tesla line of graphics processors: C2070, K20, K40, K80. By using hierarchical grid systems at different spatial scales we increase the efficiency of the computing resources usage and speed up our simulations of a various flooding problems.

Parallel Implementation of Bias Field Correction Fuzzy C-Means Algorithm for Image Segmentation

Abstract: Image segmentation in the medical field is one of the most important phases to diseases diagnosis. The bias field estimation algorithm is the most interesting techniques to correct the in-homogeneity intensity artifact on the image. However, the use of such technique requires a powerful processing and quite expensive for big size as medical images. Hence the idea of parallelism becomes increasingly required. Several researchers have followed this path mainly in the bioinformatics field where they have suggested different algorithms implementations. In this paper, a novel Single Instruction Multiple Data (SIMD) architecture for bias field estimation and image segmentation algorithm is proposed. In order to accelerate compute-intensive portions of the sequential implementation, we have implemented this algorithm on three different graphics processing units (GPU) cards named GT740m, GTX760 and GTX580 respectively, using Compute Unified Device Architecture (CUDA) software programming tool. Numerical obtained results for the computation speed up, allowed us to conclude on the suitable GPU architecture for this kind of applications and closest ones.

Tactile Graphics with a Voice

Abstract: We discuss the development of Tactile Graphics with a Voice (TGV), a system used to access label information in tactile graphics using QR codes. Blind students often rely on tactile graphics to access textbook images. Many textbook images have a large number of text labels that need to be made accessible. In order to do so, we propose TGV, which uses QR codes to replace the text, as an alternative to Braille. The codes are read with a smartphone application. We evaluated the system with a longitudinal study where 10 blind and low-vision participants completed tasks using three different modes on the smartphone application: (1) no guidance, (2) verbal guidance, and (3) finger-pointing guidance. Our results show that TGV is an effective way to access text in tactile graphics, especially for those blind users who are not fluent in Braille. We also found that preferences varied greatly across the modes, indicating that future work should support multiple modes. We expand upon the algorithms we used to implement the finger pointing, algorithms to automatically place QR codes on documents. We also discuss work we have started on creating a Google Glass version of the application.

Deep Disentangled Representations for Volumetric Reconstruction

Abstract: We introduce a convolutional neural network for inferring a compact disentangled graphical description of objects from 2D images that can be used for volumetric reconstruction. The network comprises an encoder and a twin-tailed decoder. The encoder generates a disentangled graphics code. The first decoder generates a volume, and the second decoder reconstructs the input image using a novel training regime that allows the graphics code to learn a separate representation of the 3D object and a description of its lighting and pose conditions. We demonstrate this method by generating volumes and disentangled graphical descriptions from images and videos of faces and chairs.