161 research outputs found
New techniques for the scientific visualization of three-dimensional multi-variate and vector fields
GPU-friendly marching cubes.
Xie, Yongming.Thesis (M.Phil.)--Chinese University of Hong Kong, 2008.Includes bibliographical references (leaves 77-85).Abstracts in English and Chinese.Abstract --- p.iAcknowledgement --- p.iiChapter 1 --- Introduction --- p.1Chapter 1.1 --- Isosurfaces --- p.1Chapter 1.2 --- Graphics Processing Unit --- p.2Chapter 1.3 --- Objective --- p.3Chapter 1.4 --- Contribution --- p.3Chapter 1.5 --- Thesis Organization --- p.4Chapter 2 --- Marching Cubes --- p.5Chapter 2.1 --- Introduction --- p.5Chapter 2.2 --- Marching Cubes Algorithm --- p.7Chapter 2.3 --- Triangulated Cube Configuration Table --- p.12Chapter 2.4 --- Summary --- p.16Chapter 3 --- Graphics Processing Unit --- p.18Chapter 3.1 --- Introduction --- p.18Chapter 3.2 --- History of Graphics Processing Unit --- p.19Chapter 3.2.1 --- First Generation GPU --- p.20Chapter 3.2.2 --- Second Generation GPU --- p.20Chapter 3.2.3 --- Third Generation GPU --- p.20Chapter 3.2.4 --- Fourth Generation GPU --- p.21Chapter 3.3 --- The Graphics Pipelining --- p.21Chapter 3.3.1 --- Standard Graphics Pipeline --- p.21Chapter 3.3.2 --- Programmable Graphics Pipeline --- p.23Chapter 3.3.3 --- Vertex Processors --- p.25Chapter 3.3.4 --- Fragment Processors --- p.26Chapter 3.3.5 --- Frame Buffer Operations --- p.28Chapter 3.4 --- GPU CPU Analogy --- p.31Chapter 3.4.1 --- Memory Architecture --- p.31Chapter 3.4.2 --- Processing Model --- p.32Chapter 3.4.3 --- Limitation of GPU --- p.33Chapter 3.4.4 --- Input and Output --- p.34Chapter 3.4.5 --- Data Readback --- p.34Chapter 3.4.6 --- FramebufFer --- p.34Chapter 3.5 --- Summary --- p.35Chapter 4 --- Volume Rendering --- p.37Chapter 4.1 --- Introduction --- p.37Chapter 4.2 --- History of Volume Rendering --- p.38Chapter 4.3 --- Hardware Accelerated Volume Rendering --- p.40Chapter 4.3.1 --- Hardware Acceleration Volume Rendering Methods --- p.41Chapter 4.3.2 --- Proxy Geometry --- p.42Chapter 4.3.3 --- Object-Aligned Slicing --- p.43Chapter 4.3.4 --- View-Aligned Slicing --- p.45Chapter 4.4 --- Summary --- p.48Chapter 5 --- GPU-Friendly Marching Cubes --- p.49Chapter 5.1 --- Introduction --- p.49Chapter 5.2 --- Previous Work --- p.50Chapter 5.3 --- Traditional Method --- p.52Chapter 5.3.1 --- Scalar Volume Data --- p.53Chapter 5.3.2 --- Isosurface Extraction --- p.53Chapter 5.3.3 --- Flow Chart --- p.54Chapter 5.3.4 --- Transparent Isosurfaces --- p.56Chapter 5.4 --- Our Method --- p.56Chapter 5.4.1 --- Cell Selection --- p.59Chapter 5.4.2 --- Vertex Labeling --- p.61Chapter 5.4.3 --- Cell Indexing --- p.62Chapter 5.4.4 --- Interpolation --- p.65Chapter 5.5 --- Rendering Translucent Isosurfaces --- p.67Chapter 5.6 --- Implementation and Results --- p.69Chapter 5.7 --- Summary --- p.74Chapter 6 --- Conclusion --- p.76Bibliography --- p.7
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New techniques for the scientific visualization of three-dimensional multi-variate and vector fields
Volume rendering allows us to represent a density cloud with ideal properties (single scattering, no self-shadowing, etc.). Scientific visualization utilizes this technique by mapping an abstract variable or property in a computer simulation to a synthetic density cloud. This thesis extends volume rendering from its limitation of isotropic density clouds to anisotropic and/or noisy density clouds. Design aspects of these techniques are discussed that aid in the comprehension of scientific information. Anisotropic volume rendering is used to represent vector based quantities in scientific visualization. Velocity and vorticity in a fluid flow, electric and magnetic waves in an electromagnetic simulation, and blood flow within the body are examples of vector based information within a computer simulation or gathered from instrumentation. Understand these fields can be crucial to understanding the overall physics or physiology. Three techniques for representing three-dimensional vector fields are presented: Line Bundles, Textured Splats and Hair Splats. These techniques are aimed at providing a high-level (qualitative) overview of the flows, offering the user a substantial amount of information with a single image or animation. Non-homogenous volume rendering is used to represent multiple variables. Computer simulations can typically have over thirty variables, which describe properties whose understanding are useful to the scientist. Trying to understand each of these separately can be time consuming. Trying to understand any cause and effect relationships between different variables can be impossible. NoiseSplats is introduced to represent two or more properties in a single volume rendering of the data. This technique is also aimed at providing a qualitative overview of the flows
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Fast embedding for image classification & retrieval and its application to the hostel industry
This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonContent-based image classification and retrieval are the automatic processes of taking
an unseen image input and extracting its features representing the input image. Then,
for the classification task, this mathematically measured input is categorized according
to established criteria in the server and consequently shows the output as a result. On
the other hand, for the retrieval task, the extracted features of an unseen query image
are sent to the server to search for the most visually similar images to a given image
and retrieve these images as a result. Despite image features could be represented
by classical features, artificial intelligence-based features, Convolutional Neural
Networks (CNN) to be precise, have become powerful tools in the field. Nonetheless,
the high dimensional CNN features have been a challenge in particular for applications
on mobile or Internet of Things devices. Therefore, in this thesis, several fast
embeddings are explored and proposed to overcome the constraints of low memory,
bandwidth, and power. Furthermore, the first hostel image database is created with
three datasets, hostel image dataset containing 13,908 interior and exterior images of
hostels across the world, and Hostels-900 dataset and Hostels-2K dataset containing
972 images and 2,380 images, respectively, of 20 London hostel buildings. The results
demonstrate that the proposed fast embeddings such as the application of GHM-Rand
operator, GHM-Fix operator, and binary feature vectors are able to outperform or give
competitive results to those state-of-the-art methods with a lot less computational
resource. Additionally, the findings from a ten-year literature review of CBIR study in
the tourism industry could picturize the relevant research activities in the past decade
which are not only beneficial to the hostel industry or tourism sector but also to the
computer science and engineering research communities for the potential real-life
applications of the existing and developing technologies in the field
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