Two-Dimensional and Three-Dimensional Computer Vision

Analyzing the defects of two-dimensional facial expression recognition algorithm, this paper proposes a new three-dimensional facial expression recognition algorithm. The algorithm is tested in JAFFE facial expression database. The results show that the proposed algorithm dynamically determines the size of the local neighborhood according to the manifold structure, effectively solves the problem of facial expression recognition, and has good recognition rate.

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Development of Computer Vision Applications to Automate the Measurement of the Dimensions of Skin Wounds

Proceedings ◽

10.3390/proceedings2019019018 ◽

2019 ◽

Vol 19 (1) ◽

pp. 18

Author(s):

David Sánchez-Jiménez ◽

Fernando Buchón-Moragues ◽

Begoña Escutia-Muñoz ◽

Rafael Botella-Estrada

Keyword(s):

Computer Vision ◽

Real Time ◽

Programming Language ◽

Three Dimensional ◽

Skin Wounds ◽

Two Dimensional ◽

Python Programming Language ◽

Computer Vision Applications ◽

Python Programming

This paper shows the progress in the development of two computer vision applications for measuring skin wounds. Both applications have been written in Python programming language and make use of OpenCV and Scipy open source libraries. Their objective is to be part of a software that calculates the dimensions of skin wounds in an objective and reliable way. This could be useful in the clinical follow-up, assessing the evolution of skin wounds, as well as in research, comparing the efficacy of different treatments. Merging these two applications into a single one would allow to generate two-dimensional results in real time, and three-dimensional results after a few hours of processing.

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A Hilbert Scheme in Computer Vision

Canadian Journal of Mathematics ◽

10.4153/cjm-2012-023-2 ◽

2013 ◽

Vol 65 (5) ◽

pp. 961-988 ◽

Cited By ~ 33

Author(s):

Chris Aholt ◽

Bernd Sturmfels ◽

Rekha Thomas

Keyword(s):

Computer Vision ◽

Fixed Point ◽

Hilbert Scheme ◽

Gröbner Basis ◽

Three Dimensional ◽

Two Dimensional ◽

Grobner Basis ◽

Multiview Geometry ◽

Universal Gröbner Basis ◽

Two Dimensional Images

AbstractMultiview geometry is the study of two-dimensional images of three-dimensional scenes, a foundational subject in computer vision. We determine a universal Gröbner basis for the multiview ideal of ngeneric cameras. As the cameras move, the multiview varieties vary in a family of dimension 11n − 15. This family is the distinguished component of a multigraded Hilbert scheme with a unique Borel-fixed point. We present a combinatorial study of ideals lying on that Hilbert scheme.

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A review of computer vision–based structural health monitoring at local and global levels

Structural Health Monitoring ◽

10.1177/1475921720935585 ◽

2020 ◽

pp. 147592172093558 ◽

Cited By ~ 4

Author(s):

Chuan-Zhi Dong ◽

F Necati Catbas

Keyword(s):

Computer Vision ◽

Structural Health Monitoring ◽

Health Monitoring ◽

Local Level ◽

Three Dimensional ◽

Load Factor ◽

Two Dimensional ◽

Long Distance ◽

Technology Application ◽

Structural Health

Structural health monitoring at local and global levels using computer vision technologies has gained much attention in the structural health monitoring community in research and practice. Due to the computer vision technology application advantages such as non-contact, long distance, rapid, low cost and labor, and low interference to the daily operation of structures, it is promising to consider computer vision–structural health monitoring as a complement to the conventional structural health monitoring. This article presents a general overview of the concepts, approaches, and real-life practice of computer vision–structural health monitoring along with some relevant literature that is rapidly accumulating. The computer vision–structural health monitoring covered in this article at local level includes applications such as crack, spalling, delamination, rust, and loose bolt detection. At the global level, applications include displacement measurement, structural behavior analysis, vibration serviceability, modal identification, model updating, damage detection, cable force monitoring, load factor estimation, and structural identification using input–output information. The current research studies and applications of computer vision–structural health monitoring mainly focus on the implementation and integration of two-dimensional computer vision techniques to solve structural health monitoring problems and the projective geometry methods implemented are utilized to convert the three-dimensional problems into two-dimensional problems. This review mainly puts emphasis on two-dimensional computer vision–structural health monitoring applications. Subsequently, a brief review of representative developments of three-dimensional computer vision in the area of civil engineering is presented along with the challenges and opportunities of two-dimensional and three-dimensional computer vision–structural health monitoring. Finally, the article presents a forward look to the future of computer vision–structural health monitoring.

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High Resolution Microscopy of Multi-Layered Biological Crystals

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s042482010005319x ◽

1982 ◽

Vol 40 ◽

pp. 80-83

Author(s):

H.A. Cohen ◽

T.W. Jeng ◽

W. Chiu

Keyword(s):

High Resolution ◽

Low Dose ◽

Three Dimensional ◽

Data Interpretation ◽

Two Dimensional ◽

Biological Objects ◽

Density Maps ◽

Density Map ◽

Complex Crystal ◽

High Resolution Microscopy

This tutorial will discuss the methodology of low dose electron diffraction and imaging of crystalline biological objects, the problems of data interpretation for two-dimensional projected density maps of glucose embedded protein crystals, the factors to be considered in combining tilt data from three-dimensional crystals, and finally, the prospects of achieving a high resolution three-dimensional density map of a biological crystal. This methodology will be illustrated using two proteins under investigation in our laboratory, the T4 DNA helix destabilizing protein gp32*I and the crotoxin complex crystal.

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Instrumentation For Quantitative Microscopy

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100078584 ◽

1977 ◽

Vol 35 ◽

pp. 206-209

Author(s):

B. Ralph ◽

A.R. Jones

Keyword(s):

Volume Fraction ◽

Three Dimensional ◽

Two Dimensional ◽

Automatic Data ◽

Phase Volume Fraction ◽

Statistical Confidence ◽

Resultant Data ◽

Automatic Data Collection ◽

Third Dimension ◽

Evolution Of Microstructure

In all fields of microscopy there is an increasing interest in the quantification of microstructure. This interest may stem from a desire to establish quality control parameters or may have a more fundamental requirement involving the derivation of parameters which partially or completely define the three dimensional nature of the microstructure. This latter categorey of study may arise from an interest in the evolution of microstructure or from a desire to generate detailed property/microstructure relationships. In the more fundamental studies some convolution of two-dimensional data into the third dimension (stereological analysis) will be necessary.In some cases the two-dimensional data may be acquired relatively easily without recourse to automatic data collection and further, it may prove possible to perform the data reduction and analysis relatively easily. In such cases the only recourse to machines may well be in establishing the statistical confidence of the resultant data. Such relatively straightforward studies tend to result from acquiring data on the whole assemblage of features making up the microstructure. In this field data mode, when parameters such as phase volume fraction, mean size etc. are sought, the main case for resorting to automation is in order to perform repetitive analyses since each analysis is relatively easily performed.

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A linearity test for thick specimens imaged by HVEM over a 180° angular range

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100087070 ◽

1991 ◽

Vol 49 ◽

pp. 552-553

Author(s):

Yu Liu

Keyword(s):

Phase Contrast ◽

Three Dimensional ◽

Angular Range ◽

Two Dimensional ◽

Back Projection ◽

Line Integral ◽

Exponential Law ◽

Transmission Electron ◽

Absorption Law ◽

Linearity Test

The image obtained in a transmission electron microscope is the two-dimensional projection of a three-dimensional (3D) object. The 3D reconstruction of the object can be calculated from a series of projections by back-projection, but this algorithm assumes that the image is linearly related to a line integral of the object function. However, there are two kinds of contrast in electron microscopy, scattering and phase contrast, of which only the latter is linear with the optical density (OD) in the micrograph. Therefore the OD can be used as a measure of the projection only for thin specimens where phase contrast dominates the image. For thick specimens, where scattering contrast predominates, an exponential absorption law holds, and a logarithm of OD must be used. However, for large thicknesses, the simple exponential law might break down due to multiple and inelastic scattering.

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An Image Reconstruction System Applied to High Voltage Microscopy

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100115080 ◽

1975 ◽

Vol 33 ◽

pp. 292-293

Author(s):

D. E. Johnson

Keyword(s):

High Voltage ◽

Prior Information ◽

Block Diagram ◽

Three Dimensional ◽

Real Space ◽

Two Dimensional ◽

Efficient Manner ◽

Fourier Methods ◽

Tilt Series ◽

Methods Of Reconstruction

Increased specimen penetration; the principle advantage of high voltage microscopy, is accompanied by an increased need to utilize information on three dimensional specimen structure available in the form of two dimensional projections (i.e. micrographs). We are engaged in a program to develop methods which allow the maximum use of information contained in a through tilt series of micrographs to determine three dimensional speciman structure.In general, we are dealing with structures lacking in symmetry and with projections available from only a limited span of angles (±60°). For these reasons, we must make maximum use of any prior information available about the specimen. To do this in the most efficient manner, we have concentrated on iterative, real space methods rather than Fourier methods of reconstruction. The particular iterative algorithm we have developed is given in detail in ref. 3. A block diagram of the complete reconstruction system is shown in fig. 1.

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Computer Assisted Image Reconstruction of Oligomer Structure

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100092323 ◽

1976 ◽

Vol 34 ◽

pp. 472-473

Author(s):

A.M. Jones ◽

A. Max Fiskin

Keyword(s):

Three Dimensional ◽

Depth Of Field ◽

Computer Assisted ◽

Projection Data ◽

Two Dimensional ◽

Single Particles ◽

Dimensional Reconstruction ◽

Computer Assisted Image ◽

The Fourier Transform ◽

Space Approach

If the tilt of a specimen can be varied either by the strategy of observing identical particles orientated randomly or by use of a eucentric goniometer stage, three dimensional reconstruction procedures are available (l). If the specimens, such as small protein aggregates, lack periodicity, direct space methods compete favorably in ease of implementation with reconstruction by the Fourier (transform) space approach (2). Regardless of method, reconstruction is possible because useful specimen thicknesses are always much less than the depth of field in an electron microscope. Thus electron images record the amount of stain in columns of the object normal to the recording plates. For single particles, practical considerations dictate that the specimen be tilted precisely about a single axis. In so doing a reconstructed image is achieved serially from two-dimensional sections which in turn are generated by a series of back-to-front lines of projection data.

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