A novel method to acquire 3D data from serial 2D images of a dental cast

2007 ◽  
Author(s):  
Yaxing Yi ◽  
Zhongke Li ◽  
Qi Chen ◽  
Jun Shao ◽  
Xinshe Li ◽  
...  
Keyword(s):  
3D Data ◽  
Novel Method ◽  
Dental Cast ◽  
2D Images

A MPS-based novel method of reconstructing 3D reservoir models from 2D images using seismic constraints

2022 ◽  
Vol 209 ◽  
pp. 109974
Author(s):  
Lixin Wang ◽  
Yanshu Yin ◽  
Changmin Zhang ◽  
Wenjie Feng ◽  
Guoyong Li ◽  
...  
Keyword(s):  
Reservoir Models ◽  
Novel Method ◽  
2D Images

scholarly journals Permeability Prediction using multivariant structural regression

2020 ◽  
Vol 146 ◽  
pp. 04001 ◽  
Author(s):  
Matthew Andrew
Keyword(s):  
Target Function ◽  
Permeability Prediction ◽  
3D Data ◽  
Open Source Data ◽  
Object Based ◽  
Source Data ◽  
Fractional Error ◽  
Novel Method ◽  
Structural Regression ◽  
Synthetic Datasets

A novel method for permeability prediction is presented using multivariant structural regression. A machine learning based model is trained using a large number (2,190, extrapolated to 219,000) of synthetic datasets constructed using a variety of object-based techniques. Permeability, calculated on each of these networks using traditional digital rock approaches, was used as a target function for a multivariant description of the pore network structure, created from the statistics of a discrete description of grains, pores and throats, generated through image analysis. A regression model was created using an Extra-Trees method with an error of <4% on the target set. This model was then validated using a composite series of data created both from proprietary datasets of carbonate and sandstone samples and open source data available from the Digital Rocks Portal (www.digitalrocksporta.org) with a Root Mean Square Fractional Error of <25%. Such an approach has wide applicability to problems of heterogeneity and scale in pore scale analysis of porous media, particularly as it has the potential of being applicable on 2D as well as 3D data.

scholarly journals 3D IMAGE RECONSTRUCTION OF OVARIAN TUMOR IN THE ULTRASONIC IMAGES

2001 ◽  
Vol 13 (02) ◽  
pp. 93-98 ◽  
Author(s):  
C. F. JIANG
Keyword(s):  
Ovarian Tumor ◽  
The Other ◽  
3D Image ◽  
Data Set ◽  
3D Image Reconstruction ◽  
3D Data ◽  
3D Information ◽  
Ultrasonic Images ◽  
2D Images ◽  
Degree Of Irregularity

The prevalence of ovarian tumor malignancy can be monitored by the degree of irregularity in the ovarian contour and by the septal structure inside the tumor observed in ultrasonic images. However the 2D ultrasonic images can not integrate 3D information form the ovarian tumor. In this paper, we present an algorithm that can render the 3D image of an ovarian tumor by reconstructing the 2D ultrasonic images into a 3D data set. This is based on sequentially boundary detection in a series of 2D images to form a 3D tumor contour. This contour is then used as a barrier to remove the data containing the other tissue adhering to the tumor surface. The final 3D image rendered by the isolated data provides a clear view of both the surface and inner structure of the ovarian tumor.

scholarly journals Evaluation of metal objects surface parameters informativity using 2D­ and 3D­data For classification of fractures

2022 ◽  
pp. 55-60
Author(s):  
V. A. Ganchenko ◽  
E. E. Marushko ◽  
L. P. Podenok ◽  
A. V. Inyutin
Keyword(s):  
Information Content ◽  
Digital Images ◽  
Local Binary Patterns ◽  
Automated System ◽  
3D Scanner ◽  
3D Data ◽  
Solid Objects ◽  
2D And 3D ◽  
2D Images

This article describes evaluation the information content of metal objects surfaces for classification of fractures using 2D and 3D data. As parameters, the textural characteristics of Haralick, local binary patterns of pixels for 2D images, macrogeometric descriptors of metal objects digitized by a 3D scanner are considered. The analysis carried out on basis of information content estimation to select the features that are most suitable for solving the problem of metals fractures classification. The results will be used for development of methods for complex forensic examination of complex polygonal surfaces of solid objects for automated system for analyzing digital images.

scholarly journals Deep learning-Based 3D inpainting of brain MR images

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Seung Kwan Kang ◽  
Seong A. Shin ◽  
Seongho Seo ◽  
Min Soo Byun ◽  
Dong Young Lee ◽  
...  
Keyword(s):  
Neural Network ◽  
Morphological Changes ◽  
Gray Matter Volume ◽  
Brain Anatomy ◽  
Mr Images ◽  
3D Data ◽  
Scan Time ◽  
Magnetic Resonance Imaging Mri ◽  
Anatomical Information ◽  
2D Images

AbstractThe detailed anatomical information of the brain provided by 3D magnetic resonance imaging (MRI) enables various neuroscience research. However, due to the long scan time for 3D MR images, 2D images are mainly obtained in clinical environments. The purpose of this study is to generate 3D images from a sparsely sampled 2D images using an inpainting deep neural network that has a U-net-like structure and DenseNet sub-blocks. To train the network, not only fidelity loss but also perceptual loss based on the VGG network were considered. Various methods were used to assess the overall similarity between the inpainted and original 3D data. In addition, morphological analyzes were performed to investigate whether the inpainted data produced local features similar to the original 3D data. The diagnostic ability using the inpainted data was also evaluated by investigating the pattern of morphological changes in disease groups. Brain anatomy details were efficiently recovered by the proposed neural network. In voxel-based analysis to assess gray matter volume and cortical thickness, differences between the inpainted data and the original 3D data were observed only in small clusters. The proposed method will be useful for utilizing advanced neuroimaging techniques with 2D MRI data.

scholarly journals Comparison of 3D interest point detectors and descriptors for point cloud fusion

2014 ◽  
Vol II-3 ◽  
pp. 57-64 ◽  
Author(s):  
R. Hänsch ◽  
T. Weber ◽  
O. Hellwich
Keyword(s):  
Point Cloud ◽  
Point Clouds ◽  
Specific Method ◽  
Scanner Data ◽  
3D Scanner ◽  
Strong Reduction ◽  
Interest Point Detectors ◽  
3D Point Clouds ◽  
3D Data ◽  
2D Images

The extraction and description of keypoints as salient image parts has a long tradition within processing and analysis of 2D images. Nowadays, 3D data gains more and more importance. This paper discusses the benefits and limitations of keypoints for the task of fusing multiple 3D point clouds. For this goal, several combinations of 3D keypoint detectors and descriptors are tested. The experiments are based on 3D scenes with varying properties, including 3D scanner data as well as Kinect point clouds. The obtained results indicate that the specific method to extract and describe keypoints in 3D data has to be carefully chosen. In many cases the accuracy suffers from a too strong reduction of the available points to keypoints.

3D Shape Compression Using Holoimage

2013 ◽  
pp. 939-956
Author(s):  
Nikolaus Karpinsky ◽  
Song Zhang
Keyword(s):  
Special Kind ◽  
Video Encoding ◽  
High Definition ◽  
Depth Data ◽  
Digital Fringe Projection ◽  
3D Data ◽  
3D Data Processing ◽  
Display Technology ◽  
Processing Techniques ◽  
2D Images

As 3D becomes more ubiquitous with the advent of 3D scanning and display technology, methods of compressing and transmitting 3D data need to be explored. One method of doing such is depth mapping, in which 3D depth data is compressed into a 2D image, and then 2D image processing techniques may be leveraged. This chapter presents a technique of depth mapping 3D scenes into 2D images, entitled Holoimage. In this technique, digital fringe projection, a special kind of structured light technique from optical metrology, is used to encode and decode 3D scenes pixel-by-pixel. Due to the pixel-by-pixel 3D data processing nature, this technique can be used on parallel hardware to realize real-time speed for high definition 3D video encoding and decoding.

3D Shape Compression Using Holoimage

2011 ◽  
pp. 87-104
Author(s):  
Nikolaus Karpinsky ◽  
Song Zhang
Keyword(s):  
Special Kind ◽  
Video Encoding ◽  
High Definition ◽  
Depth Data ◽  
Digital Fringe Projection ◽  
3D Data ◽  
3D Data Processing ◽  
Display Technology ◽  
Processing Techniques ◽  
2D Images

As 3D becomes more ubiquitous with the advent of 3D scanning and display technology, methods of compressing and transmitting 3D data need to be explored. One method of doing such is depth mapping, in which 3D depth data is compressed into a 2D image, and then 2D image processing techniques may be leveraged. This chapter presents a technique of depth mapping 3D scenes into 2D images, entitled Holoimage. In this technique, digital fringe projection, a special kind of structured light technique from optical metrology, is used to encode and decode 3D scenes pixel-by-pixel. Due to the pixel-by-pixel 3D data processing nature, this technique can be used on parallel hardware to realize real-time speed for high definition 3D video encoding and decoding.

Data-driven edge detectors for seismic data interpretation

Geophysics ◽  
2021 ◽  
pp. 1-41
Author(s):  
Julián L. Gómez ◽  
Lucía E. N. Gelis ◽  
Danilo R. Velis
Keyword(s):  
Seismic Data ◽  
Structural Information ◽  
Data Interpretation ◽  
Data Driven ◽  
Data Sets ◽  
3D Data ◽  
Edge Detectors ◽  
Novel Method ◽  
2D And 3D ◽  
Structure Enhancement

We present a novel method to assist in seismic interpretation. The algorithm learns data-driven edge-detectors for structure enhancement when applied to time slices of 3D poststack seismic data. We obtain the operators by distilling the local and structural information retrieved from patches taken randomly from the input time slices. The filters conform to an orthogonal family that behaves as structure-aware Sobel-like edge detectors, and the user can set their size and number. The results from marine Canada and New Zealand 3D seismic data demonstrate that the proposed algorithm allows the semblance attribute to improve the delineation of the subsurface channels. This fact is further supported by testing the method with realistic synthetic 2D and 3D data sets containing channeling and meandering systems. We contrast the results with standard plain Sobel filtering, multidirectional Sobel filters of variable size, and the dip-oriented plane-wave destruction Sobel attribute. The proposed method gives results that are comparable or superior to those of Sobel-based approaches. In addition, the obtained filters can adapt to the geological structures present in each time slice, which reduces the number of unwanted artifacts in the final product.

Direct phase determination in electron crystallography: small organic molecules

1992 ◽  
Vol 50 (2) ◽  
pp. 1166-1167
Author(s):  
Douglas L. Dorset
Keyword(s):  
Organic Molecules ◽  
Data Sets ◽  
Temperature Structure ◽  
3D Analysis ◽  
Intensity Data ◽  
Electron Crystallography ◽  
Phase Determination ◽  
Measured Intensity ◽  
3D Data

The quantitative use of electron diffraction intensity data for the determination of crystal structures represents the pioneering achievement in the electron crystallography of organic molecules, an effort largely begun by B. K. Vainshtein and his co-workers. However, despite numerous representative structure analyses yielding results consistent with X-ray determination, this entire effort was viewed with considerable mistrust by many crystallographers. This was no doubt due to the rather high crystallographic R-factors reported for some structures and, more importantly, the failure to convince many skeptics that the measured intensity data were adequate for ab initio structure determinations.We have recently demonstrated the utility of these data sets for structure analyses by direct phase determination based on the probabilistic estimate of three- and four-phase structure invariant sums. Examples include the structure of diketopiperazine using Vainshtein's 3D data, a similar 3D analysis of the room temperature structure of thiourea, and a zonal determination of the urea structure, the latter also based on data collected by the Moscow group.

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