Elastic Surface Model For Beta-Barrels: Geometric, Computational, And Statistical Analysis

2017 ◽  
Vol 86 (1) ◽  
pp. 35-42 ◽  
Author(s):  
Magdalena Toda ◽  
Fangyuan Zhang ◽  
Bhagya Athukorallage
Keyword(s):  
Statistical Analysis ◽  
Surface Model ◽  
Elastic Surface ◽  
Beta Barrels

scholarly journals Analysis of the Accuracy of Reconstruction of a Human Cornea by Two Geometric Modelling Techniques: A Comparative Study

2021 ◽  
pp. 255-260
Author(s):  
F. J. F. Cañavate ◽  
F. Cavas ◽  
J. S. Velázquez ◽  
J. M. Bolarín ◽  
J. L. Alió
Keyword(s):  
Statistical Analysis ◽  
Geometric Analysis ◽  
Surface Measurement ◽  
Surface Model ◽  
Human Cornea ◽  
Software Environment ◽  
Mesh Model ◽  
Modelling Techniques ◽  
Tomographic Data ◽  
Healthy Eyes

AbstractThis study compares two techniques of reconstruction and representation of the anterior and posterior surfaces of the human cornea, both for healthy and for keratoconus eyes, in a CAD software environment using raw tomographic data, by two different methods: a mesh model (obtained from a grid of points) and a NURBS surface model. After reconstruction, a morpho-geometric analysis was made, and several parameters were defined and measured in a set of 100 healthy eyes and 61 keratoconus eyes, detecting the statistical analysis significant differences between methods for both groups. Mesh method proved to be more accurate, but less eyes could be modelled. Volumetric and surface measurement values showed a total equivalence between both methods, so these would be the ones to use when modelling eyes for keratoconus detection and characterization purposes.

scholarly journals A Facial Expression Parameterization by Elastic Surface Model

2009 ◽  
Vol 2009 ◽  
pp. 1-11 ◽  
Author(s):  
Ken Yano ◽  
Koichi Harada
Keyword(s):  
Facial Expression ◽  
Facial Expressions ◽  
Source Model ◽  
Target Face ◽  
Surface Model ◽  
Control Points ◽  
Elastic Surface ◽  
Data Format ◽  
Storage Size ◽  
Facial Muscles

We introduce a novel parameterization of facial expressions by using elastic surface model. The elastic surface model has been used as a deformation tool especially for nonrigid organic objects. The parameter of expressions is either retrieved from existing articulated face models or obtained indirectly by manipulating facial muscles. The obtained parameter can be applied on target face models dissimilar to the source model to create novel expressions. Due to the limited number of control points, the animation data created using the parameterization require less storage size without affecting the range of deformation it provides. The proposed method can be utilized in many ways: (1) creating a novel facial expression from scratch, (2) parameterizing existing articulation data, (3) parameterizing indirectly by muscle construction, and (4) providing a new animation data format which requires less storage.

Statistical Analysis of a Rough-Surface Model

1978 ◽  
Vol 20 (6) ◽  
pp. 315-318 ◽  
Author(s):  
N. B. Demkin ◽  
I.I. Berkovich ◽  
M. S. Kourova
Keyword(s):  
Statistical Analysis ◽  
Rough Surface ◽  
Surface Model ◽  
Surface Layers ◽  
Equal Radius ◽  
Surface Contact ◽  
Rough Surface Contact ◽  
Contact Parameters

The ‘stereometrical’ characteristics of the rough surface layers of engineering components, which are necessary for the calculation of rough-surface contact parameters, are often identified with the ‘planimetrical’ ones obtained from single profiles. In this paper, a relationship is obtained between these characteristics. Asperities are modelled by segments of spheres of equal radius and arbitrary height.

A CNN-Based Subpixel Level DSM Generation Approach via Single Image Super-Resolution

2019 ◽  
Vol 85 (10) ◽  
pp. 765-775 ◽  
Author(s):  
Yongjun Zhang ◽  
Zhi Zheng ◽  
Yimin Luo ◽  
Yanfeng Zhang ◽  
Jun Wu ◽  
...  
Keyword(s):  
Quality Improvement ◽  
Statistical Analysis ◽  
Super Resolution ◽  
Surface Model ◽  
Generation Process ◽  
Digital Surface Model ◽  
Single Image ◽  
Image Quality Improvement ◽  
Image Super Resolution ◽  
Single Image Super Resolution

Previous work for subpixel level Digital Surface Model (DSM) generation mainly focused on data fusion techniques, which are extremely limited by the difficulty of multisource data acquisition. Although several DSM super resolution (SR) methods have been developed to ease the problem, a new issue that plenty of DSM samples are needed to train the model is raised. Therefore, considering the original images have vital influence on its DSM's accuracy, we address the problem by directly improving images resolution. Several SR models are refined and brought into the traditional DSM generation process as an image quality improvement stage to construct an easy but effective workflow for subpixel level DSM generation. Experiments verified the validity and significance of bringing SR technology into this kind of application. Statistical analysis also confirmed that a subpixel level DSM with higher fidelity can be obtained more easily compared to directly DSM interpolation.

Statistical analysis of classification

1966 ◽  
Vol 24 ◽  
pp. 188-189
Author(s):  
T. J. Deeming
Keyword(s):  
Multivariate Analysis ◽  
Statistical Analysis ◽  
Classification Scheme ◽  
Analytical Procedure ◽  
Narrow Band ◽  
Scientific Research ◽  
Maximum Amount ◽  
Amount Of Information

If we make a set of measurements, such as narrow-band or multicolour photo-electric measurements, which are designed to improve a scheme of classification, and in particular if they are designed to extend the number of dimensions of classification, i.e. the number of classification parameters, then some important problems of analytical procedure arise. First, it is important not to reproduce the errors of the classification scheme which we are trying to improve. Second, when trying to extend the number of dimensions of classification we have little or nothing with which to test the validity of the new parameters.Problems similar to these have occurred in other areas of scientific research (notably psychology and education) and the branch of Statistics called Multivariate Analysis has been developed to deal with them. The techniques of this subject are largely unknown to astronomers, but, if carefully applied, they should at the very least ensure that the astronomer gets the maximum amount of information out of his data and does not waste his time looking for information which is not there. More optimistically, these techniques are potentially capable of indicating the number of classification parameters necessary and giving specific formulas for computing them, as well as pinpointing those particular measurements which are most crucial for determining the classification parameters.

Quantitative parallel EELS spectrum imaging developed as a new tool in AEM

1992 ◽  
Vol 50 (2) ◽  
pp. 1202-1203
Author(s):  
Gianluigi Botton ◽  
Gilles L'espérance
Keyword(s):  
Statistical Analysis ◽  
Spatial Resolution ◽  
Personal Computer ◽  
Systematic Study ◽  
Present Author ◽  
Chemical Elements ◽  
Detection Limits ◽  
Research Groups ◽  
Quantitative Performance ◽  
Spectrum Imaging

As interest for parallel EELS spectrum imaging grows in laboratories equipped with commercial spectrometers, different approaches were used in recent years by a few research groups in the development of the technique of spectrum imaging as reported in the literature. Either by controlling, with a personal computer both the microsope and the spectrometer or using more powerful workstations interfaced to conventional multichannel analysers with commercially available programs to control the microscope and the spectrometer, spectrum images can now be obtained. Work on the limits of the technique, in terms of the quantitative performance was reported, however, by the present author where a systematic study of artifacts detection limits, statistical errors as a function of desired spatial resolution and range of chemical elements to be studied in a map was carried out The aim of the present paper is to show an application of quantitative parallel EELS spectrum imaging where statistical analysis is performed at each pixel and interpretation is carried out using criteria established from the statistical analysis and variations in composition are analyzed with the help of information retreived from t/γ maps so that artifacts are avoided.

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