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An Examination of Print and Web Graphics’ Effect on Readers’ Recall Specific research regarding how information graphics, which are defined as charts, maps or diagrams, in print and online affect readers’ recall and understanding of information is scarce at best and at times, even contradictory. Previous print research suggests that information graphics does improve readers’ recall. Online graphics research notes that the freedom of the Web allows readers to learn more efficiently than traditional media. While, other research notes that the Web’s freedom causes users to spend more effort on orienting themselves with the Web site, and therefore, users don’t learn efficiently. This study examined whether the presentation of information graphics affects readers’ recall and found no statistical significance between readers’ recall scores who had read a print graphic and those who read an online graphic. / Department of Journalism

Fluid simulation is widely used in the visual effects industry. The high level of detail required to produce realistic visual effects requires significant computation. Usually, expensive computer clusters are used in order to reduce the time required. However, general purpose Graphics Processing Unit (GPU) computing has potential as a relatively inexpensive way to reduce these simulation times. In recent years, GPUs have been used to achieve enormous speedups via their massively parallel architectures. Within the field of fluid simulation, the Lattice Boltzmann Method (LBM) stands out as a candidate for GPU execution because its grid-based structure is a natural fit for GPU parallelism. This thesis describes the design and implementation of a GPU-based free-surface LBM fluid simulation. Broadly, our approach is to ensure that the steps that perform most of the work in the LBM (the stream and collide steps) make efficient use of GPU resources. We achieve this by removing complexity from the core stream and collide steps and handling interactions with obstacles and tracking of the fluid interface in separate GPU kernels. To determine the efficiency of our design, we perform separate, detailed analyses of the performance of the kernels associated with the stream and collide steps of the LBM. We demonstrate that these kernels make efficient use of GPU resources and achieve speedups of 29.6 and 223.7, respectively. Our analysis of the overall performance of all kernels shows that significant time is spent performing obstacle adjustment and interface movement as a result of limitations associated with GPU memory accesses. Lastly, we compare our GPU LBM implementation with a single-core CPU LBM implementation. Our results show speedups of up to 81.6 with no significant differences in output from the simulations on both platforms. We conclude that order of magnitude speedups are possible using GPUs to perform free-surface LBM fluid simulations, and that GPUs can, therefore, significantly reduce the cost of performing high-detail fluid simulations for visual effects.

This thesis addresses the issue of increasing the efficiency of microcomputers for solid modeling systems. In this study, the winged-edge and vertex-edge data structures are implemented in a solid Modeling subsystem on the Rainbow 100B microcomputer, with solid modeling algorithms that manipulate and display these structures. The data structures and algorithms are compared and evaluated for their efficiency, feasibility and ease of implementation.

An often overlooked area of graphics is the ability of application programs to create graphical images. Many programs exist which allow creation interactively, but few offer the same ability for noninteractive application programs. By allowing an application program to create graphical images more user friendly programs may be created by programmers. / Department of Computer Science

This Creative Project has investigated current methods being used to produce computer animation. This investigation has resulted in the creation of a computer rendering program capable of generating animation. In order for the program to be able to execute though, a model of the object to be viewed must first be specified. This is done by assembling together a series of graphic primitives that when viewed collectively, form the desired model. Next, the rendering software is told where to point an imaginary camera in space. This camera is used to focus the image found in the viewfinder onto the computer monitor. Lastly, the imaginary camera is provided with a three dimensional path to follow as it tracks its way through space. By combining this data with the rendering software, a person viewing the resulting computer animation is able to interact with the computer model. / Department of Computer Science

Pixel art is a popular style of digital art often found in video games. It is typically characterized by its low resolution and use of limited colour palettes. Pixel art is created manually with little automation because it requires attention to pixel-level details. Working with individual pixels is a challenging and abstract task, whereas manipulating higher-level objects in vector graphics is much more intuitive. However, it is difficult to bridge this gap because although many rasterization algorithms exist, they are not well-suited for the particular needs of pixel artists, particularly at low resolutions. In this thesis, we introduce a class of rasterization algorithms called pixelation that is tailored to pixel art needs. We describe how our algorithm suppresses artifacts when pixelating vector paths and preserves shape-level features when pixelating geometric primitives. We also developed methods inspired by pixel art for drawing lines and angles more effectively at low resolutions. We compared our results to rasterization algorithms, rasterizers used in commercial software, and human subjects---both amateurs and pixel artists. Through formal analyses of our user study studies and a close collaboration with professional pixel artists, we showed that, in general, our pixelation algorithms produce more visually appealing results than na\"{i}ve rasterization algorithms do.

In many applications, such as conversational agents, virtual reality, movies, and games, animated facial expressions of computer-generated (CG) characters are used to communicate, teach, or entertain. With an increased demand for CG characters, it is important to animate accurate, realistic facial expressions because human facial expressions communicate a wealth of information. However, realistically animating faces is challenging and time-consuming for two reasons. First, human observers are adept at detecting anomalies in realistic CG facial animations. Second, traditional animation techniques based on keyframing sometimes approximate the dynamics of facial expressions or require extensive artistic input while high-resolution performance capture techniques are cost prohibitive. In this thesis, we develop a framework to explore representations of two key facial expressions, blinks and smiles, and we show that data-driven models are needed to realistically animate these expressions. Our approach relies on utilizing high-resolution performance capture data to build models that can be used in traditional keyframing systems. First, we record large collections of high-resolution dynamic expressions through video and motion capture technology. Next, we build expression-specific models of the dynamic data properties of blinks and smiles. We explore variants of the model and assess whether viewers perceive the models as more natural than the simplified models present in the literature. In the first part of the thesis, we build a generative model of the characteristic dynamics of blinks: fast closing of the eyelids followed by a slow opening. Blinks have a characteristic profile with relatively little variation across instances or people. Our results demonstrate the need for an accurate model of eye blink dynamics rather than simple approximations, as viewers perceive the difference. In the second part of the thesis, we investigate how spatial and temporal linearities impact smile genuineness and build a model for genuine smiles. Our perceptual results indicate that a smile model needs to preserve temporal information. With this model, we synthesize perceptually genuine smiles that outperform traditional animation methods accompanied by plausible head motions. In the last part of the thesis, we investigate how blinks synchronize with the start and end of spontaneous smiles. Our analysis shows that eye blinks correlate with the end of the smile and occur before the lip corners stop moving downwards. We argue that the timing of blinks relative to smiles is useful in creating compelling facial expressions. Our work is directly applicable to current methods in animation. For example, we illustrate how our models can be used in the popular framework of blendshape animation to increase realism while keeping the system complexity low. Furthermore, our perceptual results can inform the design of realistic animation systems by highlighting common assumptions that over-simplify the dynamics of expressions.