Thesis (M.S.)--Oregon State University, 2009. / Printout. Includes bibliographical references (leaves 106-112). Also available on the World Wide Web.

Thesis (Ph. D., Computer Science)--University of Idaho, December 2008. / Major professor: Robert E. Hiromoto. Includes bibliographical references (leaves 111-118). Also available online (PDF file) by subscription or by purchasing the individual file.

Real-time performance is necessary for video-rate processing. By utilizing GPU for acceleration, we propose an efficient technique for the warped display of surveillance video signal. Usually, there are regions of interest (ROIs) in video surveillance, such as entrance or exit, and moving objects or persons. The ii surveillant wants to see more of the ROIs, but also wants to have an overview of the whole surveillance scope. The warped display solves this conflict by locally zooming in the ROIs. / The above warped-display technique may not be able to capture more information. It only provides an efficient way to display the captured frame. To solve this problem, we propose a novel technique to automatically adjust the exposure and capture more information. Traditional automatic exposure control (AEC) is usually based on the intensity level. On the other hand, our technique is based on the information theory and the amount of information is measured by Shannon entropy. The computation of entropy is accelerated by GPU to achieve the video-rate performance. / Volume rendering is another hot research area. In this area, isosurfaces have been widely adopted to reveal the complex structures in volumetric data, due to its fine visual quality. We describe a GPU-based marching cubes (MC) algorithm to visualize multiple translucent isosurfaces. With the proposed parallel algorithm, we can naturally generate triangles in order, which facilitates the visibility-correct visualization of multiple translucent isosurfaces without computationally expensive sorting. Upon a commodity GPU, our implementation can extract isosurfaces from a high-resolution volume in real time and render the result. / We first present a GPU-friendly image rendering framework, which can achieve a wide range of non-photorealistic rendering (NPR) effects. Most of these effects usually require the tailor-made algorithms. By feeding with constant kernels, the usage of our framework is as simple as that of discrete linear filtering. However, our framework is non-linear and hence can mimic complex NPR effects, such as watercolor, painting, sketching, and so on. The core of our framework is the cellular neural networks (CNN). By relaxing the constraints in the traditional CNN, we demonstrate that various interesting and convincing results can be obtained. As CNN is locally connected and designed for massively parallel hardware, it fits nicely into the GPU hardware and the performance is improved a lot. / With the development of graphics processing unit (GPU), it is more and more efficient to solve complex algorithms with GPU because of its highly parallel structure and fast floating point operations. These complex algorithms were usually implemented with CPU previously. In this thesis, we propose several GPU-friendly concepts and algorithms to address some problems of visual computing, including: image rendering, video rendering, and volume rendering. / Wang Guangyu. / "September 2007." / Advisers: Pheng Am Heng; Tien-Tsin Wong. / Source: Dissertation Abstracts International, Volume: 69-08, Section: B, page: 4865. / Thesis (Ph.D.)--Chinese University of Hong Kong, 2007. / Includes bibliographical references (p. 115-131). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Electronic reproduction. [Ann Arbor, MI] : ProQuest Information and Learning, [200-] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstracts in English and Chinese. / School code: 1307.

This thesis presents an efficient computational voxelization approach that utilizes the graphics pipeline. Our approach is hybrid in that it performs a precise gap-free computational voxelization, employs fixed-function components of the GPU, and utilizes the stages of the graphics pipeline to improve parallelism. This approach makes use of the latest features of OpenGL and fully supports both conservative and thin voxelization. In contrast to other computational voxelization approaches, this approach is implemented entirely in OpenGL, and achieves both triangle and fragment parallelism through its use of both the geometry and fragment shaders. A novel approach utilizing the graphics pipeline to complement geometric triangle intersection computations is presented. By exploiting features of the existing graphics pipeline we are able to rapidly compute accurate scene voxelization in a manner that integrates well with existing OpenGL applications, is robust across many different models, and eschews the need for complex work/load-balancing schemes. / Graduation date: 2013

Includes bibliographical references (p. 114-123).

Title from title page of PDF (University of Missouri--St. Louis, viewed February 23, 2010). Includes bibliographical references (p. 33-35).

Includes bibliographical references (p. 51-57).

Includes bibliographical references (p. 106-115).

Xie, Yongming. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2008. / Includes bibliographical references (leaves 77-85). / Abstracts in English and Chinese. / Abstract --- p.i / Acknowledgement --- p.ii / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Isosurfaces --- p.1 / Chapter 1.2 --- Graphics Processing Unit --- p.2 / Chapter 1.3 --- Objective --- p.3 / Chapter 1.4 --- Contribution --- p.3 / Chapter 1.5 --- Thesis Organization --- p.4 / Chapter 2 --- Marching Cubes --- p.5 / Chapter 2.1 --- Introduction --- p.5 / Chapter 2.2 --- Marching Cubes Algorithm --- p.7 / Chapter 2.3 --- Triangulated Cube Configuration Table --- p.12 / Chapter 2.4 --- Summary --- p.16 / Chapter 3 --- Graphics Processing Unit --- p.18 / Chapter 3.1 --- Introduction --- p.18 / Chapter 3.2 --- History of Graphics Processing Unit --- p.19 / Chapter 3.2.1 --- First Generation GPU --- p.20 / Chapter 3.2.2 --- Second Generation GPU --- p.20 / Chapter 3.2.3 --- Third Generation GPU --- p.20 / Chapter 3.2.4 --- Fourth Generation GPU --- p.21 / Chapter 3.3 --- The Graphics Pipelining --- p.21 / Chapter 3.3.1 --- Standard Graphics Pipeline --- p.21 / Chapter 3.3.2 --- Programmable Graphics Pipeline --- p.23 / Chapter 3.3.3 --- Vertex Processors --- p.25 / Chapter 3.3.4 --- Fragment Processors --- p.26 / Chapter 3.3.5 --- Frame Buffer Operations --- p.28 / Chapter 3.4 --- GPU CPU Analogy --- p.31 / Chapter 3.4.1 --- Memory Architecture --- p.31 / Chapter 3.4.2 --- Processing Model --- p.32 / Chapter 3.4.3 --- Limitation of GPU --- p.33 / Chapter 3.4.4 --- Input and Output --- p.34 / Chapter 3.4.5 --- Data Readback --- p.34 / Chapter 3.4.6 --- FramebufFer --- p.34 / Chapter 3.5 --- Summary --- p.35 / Chapter 4 --- Volume Rendering --- p.37 / Chapter 4.1 --- Introduction --- p.37 / Chapter 4.2 --- History of Volume Rendering --- p.38 / Chapter 4.3 --- Hardware Accelerated Volume Rendering --- p.40 / Chapter 4.3.1 --- Hardware Acceleration Volume Rendering Methods --- p.41 / Chapter 4.3.2 --- Proxy Geometry --- p.42 / Chapter 4.3.3 --- Object-Aligned Slicing --- p.43 / Chapter 4.3.4 --- View-Aligned Slicing --- p.45 / Chapter 4.4 --- Summary --- p.48 / Chapter 5 --- GPU-Friendly Marching Cubes --- p.49 / Chapter 5.1 --- Introduction --- p.49 / Chapter 5.2 --- Previous Work --- p.50 / Chapter 5.3 --- Traditional Method --- p.52 / Chapter 5.3.1 --- Scalar Volume Data --- p.53 / Chapter 5.3.2 --- Isosurface Extraction --- p.53 / Chapter 5.3.3 --- Flow Chart --- p.54 / Chapter 5.3.4 --- Transparent Isosurfaces --- p.56 / Chapter 5.4 --- Our Method --- p.56 / Chapter 5.4.1 --- Cell Selection --- p.59 / Chapter 5.4.2 --- Vertex Labeling --- p.61 / Chapter 5.4.3 --- Cell Indexing --- p.62 / Chapter 5.4.4 --- Interpolation --- p.65 / Chapter 5.5 --- Rendering Translucent Isosurfaces --- p.67 / Chapter 5.6 --- Implementation and Results --- p.69 / Chapter 5.7 --- Summary --- p.74 / Chapter 6 --- Conclusion --- p.76 / Bibliography --- p.77

Thesis (M.S.)--Oregon State University, 2010. / Printout. Includes bibliographical references (leaves 103-104). Also available on the World Wide Web.