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.
| Identifer | oai:union.ndltd.org:cuhk.edu.hk/oai:cuhk-dr:cuhk_344124 |
| Date | January 2007 |
| Contributors | Wang, Guangyu, Chinese University of Hong Kong Graduate School. Division of Computer Science and Engineering. |
| Source Sets | The Chinese University of Hong Kong |
| Language | English, Chinese |
| Detected Language | English |
| Type | Text, theses |
| Format | electronic resource, microform, microfiche, 1 online resource (xiv, 131 p. : ill.) |
| Rights | Use of this resource is governed by the terms and conditions of the Creative Commons “Attribution-NonCommercial-NoDerivatives 4.0 International” License (http://creativecommons.org/licenses/by-nc-nd/4.0/) |
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