CONTROL OF OBJECT VISIBILITY IN VOLUME RENDERING — A DISTANCE-BASED APPROACH

2005 ◽  
Vol 05 (04) ◽  
pp. 699-714 ◽  
Author(s):  
JIANLONG ZHOU ◽  
ANDREAS DÖRING ◽  
KLAUS D. TÖNNIES
Keyword(s):  
Transfer Function ◽  
Volume Rendering ◽  
Transfer Functions ◽  
Redundant Information ◽  
Volume Data ◽  
Data Value ◽  
Rendering Pipeline

Volume data often have redundant information for clinical uses. The essence of volume rendering can be regarded as a mechanism to determine visibility of redundant information and structures of interest using different approaches. Controlling the visibility of these structures in volume rendering depends on the following factors in existing rendering algorithms: The data value of current voxel and its derivatives (used in transfer function based approaches), and the voxel position (used in volume clipping). This paper introduces the distance which is defined by the user into volume rendering pipeline to control the visibility of structures. The distance based approach, which is named as distance transfer function, has the flexibility of transfer functions for depicting data information and the advantages of volume clippings for visualizing inner structures. The results show that the distance based approach is a powerful tool for volume data information depiction.

Multi-Dimensional Transfer Functions Design

2011 ◽  
pp. 223-240
Author(s):  
Hai Lin
Keyword(s):  
Transfer Function ◽  
Volume Rendering ◽  
Large Scale ◽  
Spatial Information ◽  
Transfer Functions ◽  
Design Method ◽  
Data Layout ◽  
Volume Data ◽  
Function Design ◽  
Transfer Function Design

Transfer function design is one of the most important procedures in volume rendering. Transfer function maps, which is a function mapping relationship, data values to display attributes, such as color and opacity. This chapter introduces region growing- based multi-dimensional transfer function design method, which can improve the effect of the multi-dimensional transfer function design, and help the users save the time used in the interactive design and decrease the difficult. In order to use the spatial information as independent variable, we combine spatial information to generate multi-dimensional transfer function. This chapter discusses the GPU-based transfer function lookup method and illumination parameter setting problems. In the last part of this chapter, we discuss the data layout of large scale volume data set and its volume rendering methods.

scholarly journals Information-assisted volume rendering and visual evaluation through machine intelligence

2021 ◽  
Author(s):  
Naimul M. Khan
Keyword(s):  
Volume Rendering ◽  
Transfer Functions ◽  
Design Method ◽  
Machine Intelligence ◽  
Training Data ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Complex Data ◽  
Volume Data ◽  
Visual Evaluation

Exploration and visualization of complex data has become an integral part of life. But there is a semantic gap between the users and the visualization scientists. The priority of the users is usability while that of the scientists is techniques. Information-Assisted Visualization (IAV) can help bridge this gap, where additional information extracted from the raw data is presented to the user in an easily interpretable way. This thesis proposes some novel machine intelligence based systems for intuitive IAV. The majority of the thesis focuses on Direct Volume Rendering, where Transfer Functions (TF) are used to color the volume data to expose structures. Existing TF design methods require manipulating complex widgets, which may be difficult for the user. We propose two novel approaches towards TF design. In the data-centric approach, we generate an organized representation of the data through clustering and provide the user with some intuitive control over the output in the cluster domain. We use Spherical Self-Organizing Maps (SS)M) as the core of this approach. Instead of manipulating complex widgets, the user interacts with the simple SSOM color-coded lattice to design the TF. In the image-centric approach, the user interaction with the data is direct and minimal. The user interactions create the training data, and supervised classification is used to generate the TF. First, we propose novel supervised classifiers that combine the local information available through Support Vector Machine-based classifiers and the global information available through Nonparametric Discriminant Analysis-based classifiers. Using these classifiers, we propose a TF design method where the user interacts with the volume slices directly to generate the output. Finally, we explore the use of IAV for home-based physical rehabilitation. We propose an information-assisted visual valuation framework which can compare a user’s performance of a physical exercise with that of an expert using our novel Incremental Dynamic Time Warping method and communicate the results visually through our color-mapped skeleton silhouette. All the proposed techniques are accompanied by detailed experimental results comparing them against the state-of-the-art. The results shows the potential of using machine learning techniques to achieve visualization tasks in a simpler yet more effective way.

scholarly journals Region Space Guided Transfer Function Design for Nonlinear Neural Network Augmented Image Visualization

2018 ◽  
Vol 2018 ◽  
pp. 1-8
Author(s):  
Fei Yang ◽  
Xiangxu Meng ◽  
JiYing Lang ◽  
Weigang Lu ◽  
Lei Liu
Keyword(s):  
Neural Network ◽  
Transfer Function ◽  
Ray Casting ◽  
Volume Data ◽  
Field Quality ◽  
Transfer Function Design ◽  
3D Volume ◽  
Rendering Pipeline ◽  
2D Histogram

Visualization provides an interactive investigation of details of interest and improves understanding the implicit information. There is a strong need today for the acquisition of high quality visualization result for various fields, such as biomedical or other scientific field. Quality of biomedical volume data is often impacted by partial effect, noisy, and bias seriously due to the CT (Computed Tomography) or MRI (Magnetic Resonance Imaging) devices, which may give rise to an extremely difficult task of specifying transfer function and thus generate poor visualized image. In this paper, firstly a nonlinear neural network based denoising in the preprocessing stage is provided to improve the quality of 3D volume data. Based on the improved data, a novel region space with depth based 2D histogram construction method is then proposed to identify boundaries between materials, which is helpful for designing the proper semiautomated transfer function. Finally, the volume rendering pipeline with ray-casting algorithm is implemented to visualize several biomedical datasets. The noise in the volume data is suppressed effectively and the boundary between materials can be differentiated clearly by the transfer function designed via the modified 2D histogram.

Photographic Volume Visualization with LH Histogram and Color Distance Gradient

2013 ◽  
Vol 333-335 ◽  
pp. 1034-1037
Author(s):  
Yong Xia ◽  
Li Bing Zeng ◽  
Kuan Quan Wang
Keyword(s):  
Transfer Function ◽  
Volume Rendering ◽  
Volume Visualization ◽  
Volume Data ◽  
Data Set ◽  
Color Gradient ◽  
Opengl Shading Language

Volume rendering is an important issue in the field of images visualization, which can display the details of the volume data intuitively. In this paper, we provide an new transfer function for volume visualization of photographic data set. LH histogram and color gradient are used for opacity design. And special organ can be enhanced in 3D view for special mission. We use GLSL (OpenGL Shading Language) for the GPUs accelerate. VHP data set is used for test and the result confirms that our method is effective.

scholarly journals Information-assisted volume rendering and visual evaluation through machine intelligence

2021 ◽  
Author(s):  
Naimul M. Khan
Keyword(s):  
Volume Rendering ◽  
Transfer Functions ◽  
Design Method ◽  
Machine Intelligence ◽  
Training Data ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Complex Data ◽  
Volume Data ◽  
Visual Evaluation

Exploration and visualization of complex data has become an integral part of life. But there is a semantic gap between the users and the visualization scientists. The priority of the users is usability while that of the scientists is techniques. Information-Assisted Visualization (IAV) can help bridge this gap, where additional information extracted from the raw data is presented to the user in an easily interpretable way. This thesis proposes some novel machine intelligence based systems for intuitive IAV. The majority of the thesis focuses on Direct Volume Rendering, where Transfer Functions (TF) are used to color the volume data to expose structures. Existing TF design methods require manipulating complex widgets, which may be difficult for the user. We propose two novel approaches towards TF design. In the data-centric approach, we generate an organized representation of the data through clustering and provide the user with some intuitive control over the output in the cluster domain. We use Spherical Self-Organizing Maps (SS)M) as the core of this approach. Instead of manipulating complex widgets, the user interacts with the simple SSOM color-coded lattice to design the TF. In the image-centric approach, the user interaction with the data is direct and minimal. The user interactions create the training data, and supervised classification is used to generate the TF. First, we propose novel supervised classifiers that combine the local information available through Support Vector Machine-based classifiers and the global information available through Nonparametric Discriminant Analysis-based classifiers. Using these classifiers, we propose a TF design method where the user interacts with the volume slices directly to generate the output. Finally, we explore the use of IAV for home-based physical rehabilitation. We propose an information-assisted visual valuation framework which can compare a user’s performance of a physical exercise with that of an expert using our novel Incremental Dynamic Time Warping method and communicate the results visually through our color-mapped skeleton silhouette. All the proposed techniques are accompanied by detailed experimental results comparing them against the state-of-the-art. The results shows the potential of using machine learning techniques to achieve visualization tasks in a simpler yet more effective way.

Analytic integration of contrast transfer functions

1988 ◽  
Vol 46 ◽  
pp. 822-823
Author(s):  
Peter Rez
Keyword(s):  
Wave Function ◽  
Transfer Function ◽  
Transfer Functions ◽  
Spherical Aberration ◽  
Continuous Distribution ◽  
Objective Lens ◽  
Direction Parallel ◽  
Scattering Angle ◽  
Analytic Integration ◽  
Image Position

In high resolution microscopy the image amplitude is given by the convolution of the specimen exit surface wave function and the microscope objective lens transfer function. This is usually done by multiplying the wave function and the transfer function in reciprocal space and integrating over the effective aperture. For very thin specimens the scattering can be represented by a weak phase object and the amplitude observed in the image plane is1where fe (Θ) is the electron scattering factor, r is a postition variable, Θ a scattering angle and x(Θ) the lens transfer function. x(Θ) is given by2where Cs is the objective lens spherical aberration coefficient, the wavelength, and f the defocus.We shall consider one dimensional scattering that might arise from a cross sectional specimen containing disordered planes of a heavy element stacked in a regular sequence among planes of lighter elements. In a direction parallel to the disordered planes there will be a continuous distribution of scattering angle.

Trombone Transfer Functions: Comparison Between Frequency-Swept Sine Wave and Human Performer Input

2012 ◽  
Vol 37 (4) ◽  
pp. 447-454
Author(s):  
James W. Beauchamp
Keyword(s):  
Transfer Function ◽  
Transfer Functions ◽  
Cutoff Frequency ◽  
Sine Wave ◽  
Nonlinear Propagation ◽  
Linear Filter ◽  
Wave System ◽  
General Effect ◽  
Filter Characteristic ◽  
High Pass

Abstract Source/filter models have frequently been used to model sound production of the vocal apparatus and musical instruments. Beginning in 1968, in an effort to measure the transfer function (i.e., transmission response or filter characteristic) of a trombone while being played by expert musicians, sound pressure signals from the mouthpiece and the trombone bell output were recorded in an anechoic room and then subjected to harmonic spectrum analysis. Output/input ratios of the signals’ harmonic amplitudes plotted vs. harmonic frequency then became points on the trombone’s transfer function. The first such recordings were made on analog 1/4 inch stereo magnetic tape. In 2000 digital recordings of trombone mouthpiece and anechoic output signals were made that provide a more accurate measurement of the trombone filter characteristic. Results show that the filter is a high-pass type with a cutoff frequency around 1000 Hz. Whereas the characteristic below cutoff is quite stable, above cutoff it is extremely variable, depending on level. In addition, measurements made using a swept-sine-wave system in 1972 verified the high-pass behavior, but they also showed a series of resonances whose minima correspond to the harmonic frequencies which occur under performance conditions. For frequencies below cutoff the two types of measurements corresponded well, but above cutoff there was a considerable difference. The general effect is that output harmonics above cutoff are greater than would be expected from linear filter theory, and this effect becomes stronger as input pressure increases. In the 1990s and early 2000s this nonlinear effect was verified by theory and measurements which showed that nonlinear propagation takes place in the trombone, causing a wave steepening effect at high amplitudes, thus increasing the relative strengths of the upper harmonics.

Structural-parametric model of an electroelastic actuator for nanomechanics

2020 ◽  
pp. 3-11
Author(s):  
S.M. Afonin
Keyword(s):  
Transfer Function ◽  
Piezoelectric Actuator ◽  
Transfer Functions ◽  
Piezoelectric Effect ◽  
Parametric Model ◽  
Elastic Compliance ◽  
Parametric Models ◽  
Piezo Actuator ◽  
Structural Scheme ◽  
Model Transfer

Structural-parametric models, structural schemes are constructed and the transfer functions of electro-elastic actuators for nanomechanics are determined. The transfer functions of the piezoelectric actuator with the generalized piezoelectric effect are obtained. The changes in the elastic compliance and rigidity of the piezoactuator are determined taking into account the type of control. Keywords electro-elastic actuator, piezo actuator, structural-parametric model, transfer function, parametric structural scheme

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