Showing papers on "Graphics published in 2015"

Deep convolutional inverse graphics network

Abstract: This paper presents the Deep Convolution Inverse Graphics Network (DC-IGN), a model that aims to learn an interpretable representation of images, disentangled with respect to three-dimensional scene structure and viewing transformations such as depth rotations and lighting variations. The DC-IGN model is composed of multiple layers of convolution and de-convolution operators and is trained using the Stochastic Gradient Variational Bayes (SGVB) algorithm [10]. We propose a training procedure to encourage neurons in the graphics code layer to represent a specific transformation (e.g. pose or light). Given a single input image, our model can generate new images of the same object with variations in pose and lighting. We present qualitative and quantitative tests of the model's efficacy at learning a 3D rendering engine for varied object classes including faces and chairs.

Deep Convolutional Inverse Graphics Network

Abstract: This paper presents the Deep Convolution Inverse Graphics Network (DC-IGN), a model that learns an interpretable representation of images. This representation is disentangled with respect to transformations such as out-of-plane rotations and lighting variations. The DC-IGN model is composed of multiple layers of convolution and de-convolution operators and is trained using the Stochastic Gradient Variational Bayes (SGVB) algorithm. We propose a training procedure to encourage neurons in the graphics code layer to represent a specific transformation (e.g. pose or light). Given a single input image, our model can generate new images of the same object with variations in pose and lighting. We present qualitative and quantitative results of the model's efficacy at learning a 3D rendering engine.

User guide to Exploration and Graphics for RivEr Trends (EGRET) and dataRetrieval: R packages for hydrologic data

Abstract: .........................................................................................................................................................

semPlot: Unified Visualizations of Structural Equation Models

Abstract: Structural equation modeling (SEM) has a long history of representing models graphically as path diagrams. This article presents the freely available semPlot package for R, which fills the gap between advanced, but time-consuming, graphical software and the limited graphics produced automatically by SEM software. In addition, semPlot offers more functionality than drawing path diagrams: It can act as a common ground for importing SEM results into R. Any result usable as input to semPlot can also be represented in any of the 3 popular SEM frameworks, as well as translated to input syntax for the R packages sem (Fox, Nie, & Byrnes, 2013) and lavaan (Rosseel, 2012). Special considerations are made in the package for the automatic placement of variables, using 3 novel algorithms that extend the earlier work of Boker, McArdle, and Neale (2002). The article concludes with detailed instructions on these node-placement algorithms.

A User’s Guide to Network Analysis in R

Abstract: Introducing Network Analysis in R.- The Network Analysis "5 Number Summary".- Network Data Management in R.- Basic Network Plotting and Layout.- Effective Network Graphic Design.- Advanced Network Graphics.- Actor Prominence.- Subgroups.- Affiliation Networks.- Random Network Models.- Statistical Network Models.- Dynamic Network Models.- Simulations.

A fast GNU method to draw accurate scientific illustrations for taxonomy.

Abstract: Nowadays only digital figures are accepted by the most important journals of taxonomy. These may be produced by scanning conventional drawings, made with high precision technical ink-pens, which normally use capillary cartridge and various line widths. Digital drawing techniques that use vector graphics, have already been described in literature to support scientists in drawing figures and plates for scientific illustrations; these techniques use many different software and hardware devices. The present work gives step-by-step instructions on how to make accurate line drawings with a new procedure that uses bitmap graphics with the GNU Image Manipulation Program (GIMP). This method is noteworthy: it is very accurate, producing detailed lines at the highest resolution; the raster lines appear as realistic ink-made drawings; it is faster than the traditional way of making illustrations; everyone can use this simple technique; this method is completely free as it does not use expensive and licensed software and it can be used with different operating systems. The method has been developed drawing figures of terrestrial isopods and some examples are here given.

RgoogleMaps and loa: Unleashing R Graphics Power on Map Tiles

Abstract: The RgoogleMaps package provides (1) an R interface to query the Google and the OpenStreetMap servers for static maps in the form of PNGs, and (2) enables the user to overlay plots on those maps within R. The loa package provides dedicated panel functions to integrate RgoogleMaps within the lattice plotting environment. In addition to solving the generic task of plotting on a map background in R, we introduce several specific algorithms to detect and visualize spatio-temporal clusters. This task can often be reduced to detecting over-densities in space relative to a background density. The relative density estimation is framed as a binary classification problem. An integrated hotspot visualizer is presented which allows the efficient identification and visualization of clusters in one environment. Competing clustering methods such as the scan statistic and the density scan offer higher detection power at a much larger computational cost. Such clustering methods can then be extended using the lattice trellis framework to provide further insight into the relationship between clusters and potentially influential parameters. While there are other options for such map ‘mashups’ we believe that the integration of RgoogleMaps and lattice using loa can in certain circumstances be advantageous, e.g., by providing a highly intuitive working environment for multivariate analysis and flexible testbed for the rapid development of novel data visualizations.

cellVIEW: a tool for illustrative and multi-scale rendering of large biomolecular datasets

Abstract: In this article we introduce cellVIEW, a new system to interactively visualize large biomolecular datasets on the atomic level. Our tool is unique and has been specifically designed to match the ambitions of our domain experts to model and interactively visualize structures comprised of several billions atom. The cellVIEW system integrates acceleration techniques to allow for real-time graphics performance of 60 Hz display rate on datasets representing large viruses and bacterial organisms. Inspired by the work of scientific illustrators, we propose a level-of-detail scheme which purpose is two-fold: accelerating the rendering and reducing visual clutter. The main part of our datasets is made out of macromolecules, but it also comprises nucleic acids strands which are stored as sets of control points. For that specific case, we extend our rendering method to support the dynamic generation of DNA strands directly on the GPU. It is noteworthy that our tool has been directly implemented inside a game engine. We chose to rely on a third party engine to reduce software development work-load and to make bleeding-edge graphics techniques more accessible to the end-users. To our knowledge cellVIEW is the only suitable solution for interactive visualization of large bimolecular landscapes on the atomic level and is freely available to use and extend.

Statistical Analysis and Data Display: An Intermediate Course with Examples in R

Abstract: This contemporary presentation of statistical methods features extensive use of graphical displays for exploring data and for displaying the analysis. The authors demonstrate how to analyze data-showing code, graphics, and accompanying tabular listings-for all the methods they cover. They emphasize how to construct and interpret graphs. They discuss principles of graphical design. They identify situations where visual impressions from graphs may need confirmation from traditional tabular results. All chapters have exercises. The authors provide and discuss R functions for all the new graphical display formats. All graphs and tabular output in the book were constructed using these functions. Complete R scripts for all examples and figures are provided for readers to use as models for their own analyses. This book can serve as a standalone text for statistics majors at the master's level and for other quantitatively oriented disciplines at the doctoral level, and as a reference book for researchers. In-depth discussions of regression analysis, analysis of variance, and design of experiments are followed by introductions to analysis of discrete bivariate data, nonparametrics, logistic regression, and ARIMA time series modeling. The authors illustrate classical concepts and techniques with a variety of case studies using both newer graphical tools and traditional tabular displays. The Second Edition features graphs that are completely redrawn using the more powerful graphics infrastructure provided by R's lattice package. There are new sections in several of the chapters, revised sections in all chapters and several completely new appendices. New graphical material includes: * an expanded chapter on graphics * a section on graphing Likert Scale Data to build on the importance of rating scales in fields from population studies to psychometrics * a discussion on design of graphics that will work for readers with color-deficient vision * an expanded discussion on the design of multi-panel graphics * expanded and new sections in the discrete bivariate statistics capter on the use of mosaic plots for contingency tables including the nx2x2 tables for which the Mantel-Haenszel-Cochran test is appropriate * an interactive (using the shiny package) presentation of the graphics for the normal and t-tables that is introduced early and used in many chapters The new appendices include discussions of R, the HH package designed for R (the material in the HH package was distributed as a set of standalone functions with the First Edition of this book), the R Commander package, the RExcel system, the shiny package, and a minimal discussion on writing R packages. There is a new appendix on computational precision illustrating and explaining the FAQ (Frequently Asked Questions) about the differences between the familiar real number system and the less-familiar floating point system used in computers. The probability distributions appendix has been expanded to include more distributions (all the distributions in base R) and to include graphs of each. The editing appendix from the First Edition has been split into four expanded appendices-on working style, writing style, use of a powerful editor, and use of LaTeX for document preparation.

The Tactile Graphics Helper: Providing Audio Clarification for Tactile Graphics Using Machine Vision

Abstract: Tactile graphics use raised lines, textures, and elevations to provide individuals with visual impairments access to graphical materials through touch. Tactile graphics are particularly important for students in science, technology, engineering, and mathematics (STEM) fields, where educational content is often conveyed using diagrams and charts. However, providing a student who has a visual impairment with a tactile graphic does not automatically provide the student access to the graphic's educational content. Instead, the student may struggle to decipher subtle differences between textures or line styles, and must deal with cramped and confusing placement of lines and braille. These format-related issues prevent students with visual impairments from accessing educational content in graphics independently, because they necessitate the students ask for sighted clarification. We propose a machine-vision based "tactile graphics helper" (TGH), which tracks a student's fingers as he/she explores a tactile graphic, and allows the student to gain clarifying audio information about the tactile graphic without sighted assistance. Using an embedded mixed-methods case study with three STEM university students with visual impairments, we confirmed that format-related issues prevent these students from accessing some graphical content independently, and established that TGH provides a promising approach for overcoming tactile-graphic format issues.

Numerical Algorithms: Methods for Computer Vision, Machine Learning, and Graphics

Abstract: Numerical Algorithms: Methods for Computer Vision, Machine Learning, and Graphics presents a new approach to numerical analysis for modern computer scientists. Using examples from a broad base of computational tasks, including data processing, computational photography, and animation, the textbook introduces numerical modeling and algorithmic design from a practical standpoint and provides insight into the theoretical tools needed to support these skills. The book covers a wide range of topicsfrom numerical linear algebra to optimization and differential equationsfocusing on real-world motivation and unifying themes. It incorporates cases from computer science research and practice, accompanied by highlights from in-depth literature on each subtopic. Comprehensive end-of-chapter exercises encourage critical thinking and build students intuition while introducing extensions of the basic material. The text is designed for advanced undergraduate and beginning graduate students in computer science and related fields with experience in calculus and linear algebra. For students with a background in discrete mathematics, the book includes some reminders of relevant continuous mathematical background.

DiagramFlyer: A Search Engine for Data-Driven Diagrams

Abstract: A large amount of data is available only through data-driven diagrams such as bar charts and scatterplots These diagrams are stylized mixtures of graphics and text and are the result of complicated data-centric production pipelines Unfortunately, neither text nor image search engines exploit these diagram-specific properties, making it difficult for users to find relevant diagrams in a large corpus In response, we propose DiagramFlyer, a search engine for finding data-driven diagrams on the web By recovering the semantic roles of diagram components (eg, axes, labels, etc), we provide faceted indexing and retrieval for various statistical diagrams A unique feature of DiagramFlyer is that it is able to "expand" queries to include not only exactly matching diagrams, but also diagrams that are likely to be related in terms of their production pipelines We demonstrate the resulting search system by indexing over 300k images pulled from over 150k PDF documents

Words Versus Pictures: Leveraging the Research on Visual Communication

Abstract: Librarians, like many other occupations, tend to rely on text and underutilize graphics. Research on visual communication shows that pictures have a number of advantages over words. We can interact more effectively with colleagues and patrons by incorporating ideas from this research.

Alinea: An Advanced Linear Algebra Library for Massively Parallel Computations on Graphics Processing Units

Abstract: Direct and iterative methods are often used to solve linear systems in engineering. The matrices involved can be large, which leads to heavy computations on the central processing unit. A graphics ...

D3 Data-Driven Documents Pocket Primer

Abstract: This book provides an overview of D3, such as creating charts and graphs, handling mouse events, and creating animation effects. This book also covers CSS3 and SVG, along with an integrated code sample that uses D3, CSS3, SVG, and HTML5 Canvas. This Pocket Primer is primarily for self-directed learners who are comfortable with HTML/CSS/JavaScript and who also want to learn about managing data with D3. In essence, this Pocket Primer serves as a starting point for deeper exploration of D3 and data visualization. Includes source code and videos on the companion DVD.Features: Includes source code and videos on the companion DVD Covers Ajax, CSV-based data, and JSON-based files Contains coverage of D3 with CSS3 and SVG graphics and animation effects Brief Table of Contents:1: Introduction to D3. 2: Mouse Events and Animation Effects. 3: Working with Bar Charts and Graphs. 4: Other Graphs and Other Data Formats. 5: CSS3 Introduction. 6: Quick Overview of SVG. 7: D3 with CSS3, Canvas, and SVG. 8: Other D3 APIs and Toolkits. 9: Ajax, WebSockets, and NodeJS. 10: Mobile Applications

Render target command reordering in graphics processing

Abstract: In an example, a method for rendering graphics data includes receiving a plurality of commands associated with a plurality of render targets, where the plurality of commands are received in an initial order. The method also includes determining an execution order for the plurality of commands including reordering one or more of the plurality of commands in a different order than the initial order based on data dependencies between commands. The method also includes executing the plurality of commands in the determined execution order.

Parallel Computation of Trajectories Using Graphics Processing Units and Interpolated Gravity Models

Abstract: Seeking improvements in speed and accuracy in multiobject trajectory simulations, a solution methodology is presented that takes advantage of 1) new high-fidelity geopotential and third-body perturbation models that efficiently trade memory for speed, and 2) a graphics processing unit based integrator to achieve parallelism across multiple objects. The two methods combined lead to multiplicative speedups, making the tool three orders of magnitude faster, in some cases, compared to the same simulation performed in serial on a single central processing unit. The tool is capable of Monte Carlo simulations of a single object or of propagating the mean and covariance of all the objects in a space catalog. The tool performance is demonstrated for 1) a five-day Monte Carlo simulation of the state uncertainty point cloud modeled with over one million objects, and 2) a seven-day simulation of position and velocity states of a full space catalog with over 250,000 objects. The simulations required approximately 1 an...

Surface and deep structures in graphics comprehension

Abstract: Comprehension of graphics can be considered as a process of schema-mediated structure mapping from external graphics on internal mental models. Two experiments were conducted to test the hypothesis that graphics possess a perceptible surface structure as well as a semantic deep structure both of which affect mental model construction. The same content was presented to different groups of learners by graphics from different perspectives with different surface structures but the same deep structure. Deep structures were complementary: major features of the learning content in one experiment became minor features in the other experiment, and vice versa. Text was held constant. Participants were asked to read, understand, and memorize the learning material. Furthermore, they were either instructed to process the material from the perspective supported by the graphic or from an alternative perspective, or they received no further instruction. After learning, they were asked to recall the learning content from different perspectives by completing graphs of different formats as accurately as possible. Learners’ recall was more accurate if the format of recall was the same as the learning format which indicates surface structure influences. However, participants also showed more accurate recall when they remembered the content from a perspective emphasizing the deep structure, regardless of the graphics format presented before. This included better recall of what they had not seen than of what they really had seen before. That is, deep structure effects overrode surface effects. Depending on context conditions, stimulation of additional cognitive processing by instruction had partially positive and partially negative effects.

Crowd simulation based on constrained and controlled group formation

Abstract: Freestyle formations appear widely in animation of groups. Most existing algorithms for generating special formations focus on the visualization performances of target formations, while social dynamics factors in the process of crowd motion are ignored. Thus, disregarding those factors will decrease the bionic features and fidelity of the crowd motion. According to this problem, a method based on bionic intelligence algorithm and self-adaptive evaluation to generate special formations is proposed in this paper. Simulation effect with good fluency and lively interaction is generated by means of user interaction, data analysis and crowd motion. In this method, 3D reconstruction is used to repaint characters, graphics or patterns in the 3D modeling system to build the basic virtual scene. Then, station points are generated through interlacing cross sampling. Based on the concentric circles model of fitness, each individual, self-adaptively, chooses a target station point which matches it aptly. Finally, the Artificial Bee Colony algorithm is used for path planing to generate the optimum route to the destination without collision. Visual simulation experiments are also made on the platforms of ACIS/HOOPS and Maya. The results show that this method can generate the optimum target formation with natural motion features and in accordance with users' input. This method is also insensitive to the scale of crowd, exhibiting good performance when the number of individuals is large.

Multimedia mining research - an overview

Abstract: Multimedia data mining is a popular research domain which helps to extract interesting knowledge from multimedia data sets such as audio, video, images, graphics, speech, text and combination of several types of data sets. Normally, multimedia data are categorized into unstructured and semi-structured data. These data are stored in multimedia databases and multimedia mining is used to find useful information from large multimedia database system by using various multimedia techniques and powerful tools. This paper provides the basic concepts of multimedia mining and its essential characteristics. Multimedia mining architectures for structured and unstructured data, research issues in multimedia mining, data mining models used for multimedia mining and applications are also discussed in this paper. It helps the researchers to get the knowledge about how to do their research in the field of multimedia mining.

Graphic pattern text detection method based on deep learning

Abstract: The invention discloses a graphic pattern text detection method based on deep learning. The method includes firstly, training a depth convolution self-encoding network by combining graphic pattern text samples, than adopting marked samples, and classifying through a sparse dictionary; extracting graphic pattern texts from a sample library, rotating, shifting and transmitting, and combining the graphic pattern texts with pure background graphics; adopting a combined sample seat, establishing a depth convolution self-encoding network, and learning characteristic templates in layered training and entire optimizing manners; performing characteristic extraction on the characteristic templates acquired by deep network learning according to the acquired marked samples; sampling the extracted characteristics in the size of the original graphics, adopting single blocks as identifying units, and training the sparse dictionary and a classifier; after training, performing multi resolution decomposition on the graphics to be processed, utilizing the characteristic templates to extract characteristics, and utilizing the sparse dictionary to acquire results in a classified manner.

Accessible On-Line Floor Plans

Abstract: Better access to on-line information graphics is a pressing need for people who are blind or have severe vision impairment. We present a new model for accessible presentation of on-line information graphics and demonstrate its use for presenting floor plans. While floor plans are increasingly provided on-line, people who are blind are at best provided with only a high-level textual description. This makes it difficult for them to understand the spatial arrangement of the objects on the floor plan. Our new approach provides users with significantly better access to such plans. The users can automatically generate an accessible version of a floor plan from an on-line floor plan image quickly and independently by using a web service. This generates a simplified graphic showing the rooms, walls, doors and windows in the original floor plan as well as a textual overview. The accessible floor plan is presented on an iPad using audio feedback. As the users touch graphic elements on the screen, the element they are touching is described by speech and non-speech audio in order to help them navigate the graphic.

A Survey of Motion Capture Data Earning as High Dimensional Time Series

Abstract: Constructing effective and generalizable synthesized motions is crucial for creating naturalistic, versatile, and effective virtual characters and robots. High dimensional time series are endemic in applications of machine learning such as robotics (sensor data), computational biology (gene expression data), vision (video sequences) and graphics (motion capture data). Practical nonlinear probabilistic approaches to this data are required. I would like to go through several existing models such as Gaussian Process Dynamic Systems and Deep Belief Networks. I would analyze their strengths and limitations. I would also try to incorporate physical constraints to improve the motion quality. And on the other hand, try to improve the structure of the models or the learning algorithms.

Simulations for Validation of Vision Systems

Abstract: As the computer vision matures into a systems science and engineering discipline, there is a trend in leveraging latest advances in computer graphics simulations for performance evaluation, learning, and inference. However, there is an open question on the utility of graphics simulations for vision with apparently contradicting views in the literature. In this paper, we place the results from the recent literature in the context of performance characterization methodology outlined in the 90's and note that insights derived from simulations can be qualitative or quantitative depending on the degree of fidelity of models used in simulation and the nature of the question posed by the experimenter. We describe a simulation platform that incorporates latest graphics advances and use it for systematic performance characterization and trade-off analysis for vision system design. We verify the utility of the platform in a case study of validating a generative model inspired vision hypothesis, Rank-Order consistency model, in the contexts of global and local illumination changes, and bad weather, and high-frequency noise. Our approach establishes the link between alternative viewpoints, involving models with physics based semantics and signal and perturbation semantics and confirms insights in literature on robust change detection.

Identification of Natural Images and Computer-Generated Graphics Based on Statistical and Textural Features

Abstract: To discriminate the acquisition pipelines of digital images, a novel scheme for the identification of natural images and computer-generated graphics is proposed based on statistical and textural features. First, the differences between them are investigated from the view of statistics and texture, and 31 dimensions of feature are acquired for identification. Then, LIBSVM is used for the classification. Finally, the experimental results are presented. The results show that it can achieve an identification accuracy of 97.89% for computer-generated graphics, and an identification accuracy of 97.75% for natural images. The analyses also demonstrate the proposed method has excellent performance, compared with some existing methods based only on statistical features or other features. The method has a great potential to be implemented for the identification of natural images and computer-generated graphics.