Volumenvisualisierung auf handelsüblicher Grafik-Hardware (Volume Rendering Techniques for General Purpose Graphics Hardware)

2005 ◽  
Vol 47 (1) ◽  
Author(s):  
Christof Rezk Salama
Keyword(s):  
Volume Rendering ◽  
General Purpose ◽  
Graphics Hardware ◽  
Rendering Techniques

AbstractTechniken der Volumenvisualisierung werden zur räumlichen Darstellung dreidimensionaler Skalarfelder benötigt, wie sie beispielsweise in der Medizin in Form von tomografischen Daten entstehen. Diese Arbeit beschäftigt sich mit Ansätzen, hochqualitative Bilder solcher Volumendaten in Echtzeit mithilfe handelsüblicher Grafikkarten zu erzeugen.

Identification of Brain Tumors using Volume rendering Techniques

2021 ◽  
Author(s):  
K.Sudha Rani ◽  
K.Mani kumari ◽  
T. Nireekshna ◽  
D.V. Shobana ◽  
N. Kavitha ◽  
...  
Keyword(s):  
Brain Tumors ◽  
Volume Rendering ◽  
Rendering Techniques

Fast Volume Rendering Techniques for 3D Reconstruction of Geological Objects

2012 ◽  
Vol 182-183 ◽  
pp. 1343-1346
Author(s):  
De Wen Seng ◽  
Da Qing Li
Keyword(s):  
3D Reconstruction ◽  
Volume Rendering ◽  
Spatial Data ◽  
Real Data ◽  
Tree Structure ◽  
Marching Cubes ◽  
Geological Data ◽  
Iron Mine ◽  
Effectiveness And Efficiency ◽  
Rendering Techniques

The procedure of volume rendering techniques is introduced. The principles and methods of two kinds of different volume rendering techniques of 3D spatial data are discussed. Application of Marching Cubes (MC) algorithm in the modeling of geological objects is given. This algorithm is modified and improved in several aspects. The asymptotic decider algorithm is employed to solve the ambiguity problem and oct-tree structure is used to reduce the number of polygons generated, which will increases the efficiency of the algorithm. The improved algorithm is applied to real geological data obtained from an iron mine in China. Real data derived from an iron mine of China demonstrates the effectiveness and efficiency of the system and the algorithms.

The Use of Data Explorer as a 3-D Reconstruction Tool for Microscopy Data Sets

1997 ◽  
Vol 3 (S2) ◽  
pp. 1131-1132
Author(s):  
Jansma P.L ◽  
M.A. Landis ◽  
L.C. Hansen ◽  
N.C. Merchant ◽  
N.J. Vickers ◽  
...  
Keyword(s):  
Volume Rendering ◽  
Laser Scanning ◽  
Three Dimensional ◽  
Building Blocks ◽  
General Purpose ◽  
Data Sets ◽  
Laser Scanning Confocal Microscope ◽  
Use Of Data ◽  
Using Data ◽  
Microscopy Data

We are using Data Explorer (DX), a general-purpose, interactive visualization program developed by IBM, to perform three-dimensional reconstructions of neural structures from microscopic or optical sections. We use the program on a Silicon Graphics workstation; it also can run on Sun, IBM RS/6000, and Hewlett Packard workstations. DX comprises modular building blocks that the user assembles into data-flow networks for specific uses. Many modules come with the program, but others, written by users (including ourselves), are continually being added and are available at the DX ftp site, http://www.tc.cornell.edu/DXhttp://www.nice.org.uk/page.aspx?o=43210.Initally, our efforts were aimed at developing methods for isosurface- and volume-rendering of structures visible in three-dimensional stacks of optical sections of insect brains gathered on our Bio-Rad MRC-600 laser scanning confocal microscope. We also wanted to be able to merge two 3-D data sets (collected on two different photomultiplier channels) and to display them at various angles of view.

AN EVALUATION OF MULTIPLE FEED-FORWARD NETWORKS ON GPUs

2011 ◽  
Vol 21 (01) ◽  
pp. 31-47 ◽  
Author(s):  
NOEL LOPES ◽  
BERNARDETE RIBEIRO
Keyword(s):  
Graphics Processing Unit ◽  
Parallel Implementation ◽  
Low Cost ◽  
Back Propagation ◽  
General Purpose ◽  
Training System ◽  
Graphics Hardware ◽  
Processing Unit ◽  
Data Parallel ◽  
Graphics Processing

The Graphics Processing Unit (GPU) originally designed for rendering graphics and which is difficult to program for other tasks, has since evolved into a device suitable for general-purpose computations. As a result graphics hardware has become progressively more attractive yielding unprecedented performance at a relatively low cost. Thus, it is the ideal candidate to accelerate a wide variety of data parallel tasks in many fields such as in Machine Learning (ML). As problems become more and more demanding, parallel implementations of learning algorithms are crucial for a useful application. In particular, the implementation of Neural Networks (NNs) in GPUs can significantly reduce the long training times during the learning process. In this paper we present a GPU parallel implementation of the Back-Propagation (BP) and Multiple Back-Propagation (MBP) algorithms, and describe the GPU kernels needed for this task. The results obtained on well-known benchmarks show faster training times and improved performances as compared to the implementation in traditional hardware, due to maximized floating-point throughput and memory bandwidth. Moreover, a preliminary GPU based Autonomous Training System (ATS) is developed which aims at automatically finding high-quality NNs-based solutions for a given problem.

scholarly journals A perceptive evaluation of volume rendering techniques

2009 ◽  
Vol 5 (4) ◽  
pp. 1-24 ◽  
Author(s):  
Christian Boucheny ◽  
Georges-Pierre Bonneau ◽  
Jacques Droulez ◽  
Guillaume Thibault ◽  
Stephane Ploix
Keyword(s):  
Volume Rendering ◽  
Rendering Techniques

scholarly journals Hyoid Bone Development: An Assessment Of Optimal CT Scanner Parameters and Three-Dimensional Volume Rendering Techniques

2015 ◽  
Vol 298 (8) ◽  
pp. 1408-1415 ◽  
Author(s):  
MEGHAN M. COTTER ◽  
BRIAN J. WHYMS ◽  
MICHAEL P. KELLY ◽  
BENJAMIN M. DOHERTY ◽  
LINDELL R. GENTRY ◽  
...  
Keyword(s):  
Volume Rendering ◽  
Hyoid Bone ◽  
Bone Development ◽  
Three Dimensional ◽  
Ct Scanner ◽  
Rendering Techniques
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