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Visualization of Large Volumetric Data on CPU
Dlabaja, Drahomír ; Milet, Tomáš (referee) ; Španěl, Michal (advisor)
This thesis deals with the problem of displaying volumetric data that exceeds the operating memory capacity of the machine. The work describes the design of a visualization pipeline, which consists of a data structure for large volumetric data and an algorithm that visualizes such data. The proposed hierarchical data structure accelerates sampling and allows the reduction of the total amount of data that needs to be loaded into physical memory during visualization. Visualization of processed data is achieved by the ray casting method with existing optimization techniques, such as empty space skipping and early ray termination. The data structure allows up to 12x faster sampling compared to the sampling of raw large volumetric data serialized by rows. Up to 150x faster visualization of large volumetric data in near-lossless mode has been achieved compared to the fully lossless mode by utilizing the data hierarchy. The display scheme is implemented in the form of a library in C++20 language. The implementation uses acceleration by vectorization and allows easy parallelization by the user. The library provides tools for processing and visualization of large volumetric data on the CPU.
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Acceleration of 3D Image Processing Using GPU
Kozovský, Radoslav ; Klepárník, Petr (referee) ; Španěl, Michal (advisor)
p { margin-bottom: 0.21cm; direction: ltr; color: rgb(0, 0, 0); line-height: 150%; }p.western { font-family: "Times New Roman",serif; font-size: 11pt; }p.cjk { font-family: "Times New Roman",serif; font-size: 11pt; }p.ctl { font-family: "Times New Roman",serif; font-size: 12pt; } The aim of this work is to accelerate the calculation of selected 2D and 3D image filters using GPU using OpenCL. Specifically, it deals with the implementation and comparison of various variants of Sobel's edge detector and Gauss filter using global or local memory. Acceleration has been achieved on 3D filter variants. On 2D filter variants, the overhead for data transfer to and from the GPU was too high.
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Software for visualization and processing of volumetric data
Viktorínová, Michaela ; Jakubíček, Roman (referee) ; Harabiš, Vratislav (advisor)
The work summarizes the possibilities of volume data displaying. It deals with the scalar type of volume algorithms, which are then divided into techniques of indirect and direct rendering of volume data. From indirect techniques the Marching Cubes method is mentioned. Direct techniques are further divided into trivial and advanced algorithms. The practical part describes the design, implementation and evaluation of the functionality of implemented program that uses trivial methods. The overall evaluation consists of partial evaluations of the computational complexity, the memory load and the quality of final rendering while using different methods and types of input volume data.
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Java Client for 3D Displaing of Medical Image Data
Bireš, Jiří ; Štancl, Vít (referee) ; Kršek, Přemysl (advisor)
The thesis considers use of Java language in interactive 3D displaying of medical image data. Main point is to find out advantages of using this language in medical visualisations and find suitable tools for its future usage. At the conclusion it evaluates advantages and disadvantages of used technologies and suggests possible development of the appliacation
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Image Processing on GPUs
Bačík, Ladislav ; Španěl, Michal (referee) ; Smrž, Pavel (advisor)
This master thesis deals with modern technologies in graphic hardware and using their for general purpose computing. It is primary focused on architecture of unified processors and algorithm implementation via CUDA programming interface. Thesis base is to choose suited algorithm for GPU horsepower demonstration. Main aim of this work is implementation of multiplatform library offering algorithms for discrete volumetric data vectorization. For this purpose was chosen algorithm Marching cubes that is able to find surface of processed object. In created library will be contained algorithm runnable on graphic device and also one runnable on CPU. Finally we compare both variants and discuss their pros and cons.
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Endless Cave
Pospíšil, Petr ; Pečiva, Jan (referee) ; Milet, Tomáš (advisor)
The goal of this thesis is to implement an application showing an endless cave. The basis of this cave is simplex noise method. On the noise produced by this function is afterwards applied thresholding. Produced grid of points is used like input for marching tetrahedrons algorithm. This algorithm converts volumetric data to boundary representation. Phong reflection model and Bump mapping were used in the application, too, in order to improve the visual quality. The application is based on OpenGL library. The first part of the technical report contains theoretical description of mentioned methods, the second part contains description of implementation.
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Visualization of Marked Cells of a Model Organism
Kubíček, Radek ; Kršek, Přemysl (referee) ; Herout, Adam (advisor)
This master thesis is focused on volumetric data rendering and on highlighting and visualization of the selected cells of the model organisms. These data are captured by a confocal deconvolution microscope. Input data form one large volumetric block containing separate slices. This data block is rendered by an applicable method and then are identified and visualized the cells marked by the GFP (Green Fluorescent Protein) process or by chlorophyle fluorescency. The principal aim of this work is to find out the preferably optimal effective method enabling this highlighting, most preferably working without a manual check. Due to the data structure, this ambition seems hardly realizable, so it suffices to find out a manual working method. The last step is to embed the results of this work into FluorCam application, the confocal deconvolution microscope data visualizer.
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CPU Rendering of Large Volumetric Data
Svoboda, Jan ; Vlnas, Michal (referee) ; Španěl, Michal (advisor)
This thesis deals with design and implementation of a system that allows displaying large volumetric data in real time on the CPU of a conventional computer. The thesis aims to solve two biggest problems. Firstly, it aims to solve the problem with rendering itself, where this amount of data often cannot be placed into the main memory of a target computer. Secondly, it aims to solve the problem of storing of this data, where, in the case of large datasets, storing them in the storage of a target computer may not be desirable. The proposed solution contains two applications -- the server one and the client one. The server part is used as a remote storage of volumetric data that is provided to the client application in small blocks and in different qualities. The client application renders this data by the ray casting method and, according to the created strategies, performs loading and storing of required blocks in the local memory. In order to achieve high performance, the client application was implemented with an emphasis on parallelization of the main processes. The resulting system allows a user to display large datasets stored on a server's storage and to manage the datasets using a simple graphical user interface.
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Visualization of Large Volumetric Data on CPU
Dlabaja, Drahomír ; Milet, Tomáš (referee) ; Španěl, Michal (advisor)
This thesis deals with the problem of displaying volumetric data that exceeds the operating memory capacity of the machine. The work describes the design of a visualization pipeline, which consists of a data structure for large volumetric data and an algorithm that visualizes such data. The proposed hierarchical data structure accelerates sampling and allows the reduction of the total amount of data that needs to be loaded into physical memory during visualization. Visualization of processed data is achieved by the ray casting method with existing optimization techniques, such as empty space skipping and early ray termination. The data structure allows up to 12x faster sampling compared to the sampling of raw large volumetric data serialized by rows. Up to 150x faster visualization of large volumetric data in near-lossless mode has been achieved compared to the fully lossless mode by utilizing the data hierarchy. The display scheme is implemented in the form of a library in C++20 language. The implementation uses acceleration by vectorization and allows easy parallelization by the user. The library provides tools for processing and visualization of large volumetric data on the CPU.
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Software for visualization and processing of volumetric data
Viktorínová, Michaela ; Jakubíček, Roman (referee) ; Harabiš, Vratislav (advisor)
The work summarizes the possibilities of volume data displaying. It deals with the scalar type of volume algorithms, which are then divided into techniques of indirect and direct rendering of volume data. From indirect techniques the Marching Cubes method is mentioned. Direct techniques are further divided into trivial and advanced algorithms. The practical part describes the design, implementation and evaluation of the functionality of implemented program that uses trivial methods. The overall evaluation consists of partial evaluations of the computational complexity, the memory load and the quality of final rendering while using different methods and types of input volume data.
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