A Reference Image Generation Method for Marker-less AR

2013 ◽  
Author(s):  
Satoshi Yonemoto
Keyword(s):  
Reference Image ◽  
Image Generation

Reference image generation based on three-dimensional realistic scene modeling for contour-based infrared ATR algorithm

2011 ◽  
Author(s):  
Hang Li ◽  
Delie Ming ◽  
Jinwen Tian ◽  
Chengqiang Gao ◽  
Feiran Jie ◽  
...  
Keyword(s):  
Three Dimensional ◽  
Reference Image ◽  
Image Generation ◽  
Scene Modeling

scholarly journals Image Generation from STL Models and its Potential Role in Layer-Quality Evaluation for the Electron Beam Melting Process

2019 ◽  
Author(s):  
Hay Wong ◽  
Peter Fox ◽  
Derek Neary ◽  
Eric Jones
Keyword(s):  
Electron Beam ◽  
Quality Assessment ◽  
Process Monitoring ◽  
Electron Beam Melting ◽  
Quality Evaluation ◽  
3D Model ◽  
Reference Image ◽  
Image Generation ◽  
Model Reference ◽  
Industrial Sectors

Electron Beam Melting (EBM) is an increasingly used Additive Manufacturing (AM) technique employed by many industrial sectors, including the medical device and aerospace industries. In EBM process monitoring, data analysis for processed layer quality evaluation is currently focused on the extraction of information from the raw data collected in-EBM process, i.e. thermal/ optical / electronic images, and the comparison between the collected data and the Computed Tomography (CT)/ microscopy images generated post-EBM process. This article postulates that a stack of bitmaps could be generated from the 3D model at a range of Z heights during file preparation of the EBM process, and serve as a reference image set. In-EBM process comparison between that and the workpiece images collected during the EBM process could then be used for quality assessment purposes. In addition, despite the extensive literature on 3D model slicing and contour generation for AM process preparation, no methods regarding image generation from cross sections of the 3D models have been disseminated in details. This article aims to address this by presenting a piece of 3D model-image generation software. The software is capable of generating binary 3D model reference images with user-defined Region-of-Interest (ROI) of the processing area, and Z heights of the model. It is envisaged that this 3D model-reference image generation ability opens up new opportunities in quality assessment for the in-process monitoring of the EBM process.

Background subtraction in STEM energy-loss mapping

1984 ◽  
Vol 42 ◽  
pp. 568-569
Author(s):  
R.D. Leapman ◽  
K.E. Gorlen ◽  
C.R. Swyt
Keyword(s):  
Energy Loss ◽  
Signal To Noise Ratio ◽  
Statistical Error ◽  
Scanning Transmission Electron Microscope ◽  
Reference Image ◽  
Inverse Power Law ◽  
Transmission Electron ◽  
Least Squares Fit ◽  
Scanning Transmission ◽  
Single Number

The determination of elemental distributions by electron energy loss spectroscopy necessitates removal of the non-characteristic spectral background from a core-edge at each point in the image. In the scanning transmission electron microscope this is made possible by computer controlled data acquisition. Data may be processed by fitting the pre-edge counts, at two or more channels, to an inverse power law, AE-r, where A and r are parameters and E is energy loss. Processing may be performed in real-time so a single number is saved at each pixel. Detailed analysis, shows that the largest contribution to noise comes from statistical error in the least squares fit to the background. If the background shape remains constant over the entire image, the signal-to-noise ratio can be improved by fitting only one parameter. Such an assumption is generally implicit in subtraction of the “reference image” in energy selected micrographs recorded in the CTEM with a Castaing-Henry spectrometer.

Interactive multiple area spectrum integration (MASI)

1994 ◽  
Vol 52 ◽  
pp. 942-943
Author(s):  
John A. Hunt ◽  
Richard D. Leapman ◽  
David B. Williams
Keyword(s):  
Image Processing ◽  
Low Dose ◽  
Routine Analysis ◽  
Reference Image ◽  
Area Spectrum ◽  
List Type ◽  
Type Number ◽  
Image Processor ◽  
X Ray ◽  
Scanning Path

Interactive MASI involves controlling the raster of a STEM or SEM probe to areas predefined byan integration mask which is formed by image processing, drawing or selecting regions manually. EELS, x-ray, or other spectra are then acquired while the probe is scanning over the areas defined by the integration mask. The technique has several advantages: (1) Low-dose spectra can be acquired by averaging the dose over a great many similar features. (2) MASI can eliminate the risks of spatial under- or over-sampling of multiple, complicated, and irregularly shaped objects. (3) MASI is an extremely rapid and convenient way to record spectra for routine analysis. The technique is performed as follows:Acquire reference imageOptionally blank beam for beam-sensitive specimensUse image processor to select integration mask from reference imageCalculate scanning path for probeUnblank probe (if blanked)Correct for specimen drift since reference image acquisition

Atomic resolution characterisation of interface structures by Electron Energy Loss Spectroscopy

1993 ◽  
Vol 51 ◽  
pp. 576-577
Author(s):  
N. D. Browning ◽  
M. M. McGibbon ◽  
M. F. Chisholm ◽  
S. J. Pennycook
Keyword(s):  
High Resolution ◽  
Energy Loss ◽  
Electron Energy ◽  
Electron Energy Loss Spectroscopy ◽  
Imaging Technique ◽  
Atomic Scale ◽  
Atomic Resolution ◽  
Reference Image ◽  
The Impact ◽  
Electron Energy Loss

The recent development of the Z-contrast imaging technique for the VG HB501 UX dedicated STEM, has added a high-resolution imaging facility to a microscope used mainly for microanalysis. This imaging technique not only provides a high-resolution reference image, but as it can be performed simultaneously with electron energy loss spectroscopy (EELS), can be used to position the electron probe at the atomic scale. The spatial resolution of both the image and the energy loss spectrum can be identical, and in principle limited only by the 2.2 Å probe size of the microscope. There now exists, therefore, the possibility to perform chemical analysis of materials on the scale of single atomic columns or planes.In order to achieve atomic resolution energy loss spectroscopy, the range over which a fast electron can cause a particular excitation event, must be less than the interatomic spacing. This range is described classically by the impact parameter, b, which ranges from ~10 Å for the low loss region of the spectrum to <1Å for the core losses.

Molecular Views of Ice-Embedded Lumbricus Terrestris Erythrocruorin Obtained By Invariant Classification

1990 ◽  
Vol 48 (1) ◽  
pp. 450-451
Author(s):  
Michael schatz ◽  
Joachim Jäger ◽  
Marin van Heel
Keyword(s):  
Image Interpretation ◽  
Lumbricus Terrestris ◽  
Reference Image ◽  
Large Set ◽  
Multivariate Statistical ◽  
Data Set ◽  
Low Contrast ◽  
Negative Stain ◽  
Reference Images ◽  
Statistical Resolution

Lumbricus terrestris erythrocruorin is a giant oxygen-transporting macromolecule in the blood of the common earth worm (worm "hemoglobin"). In our current study, we use specimens (kindly provided by Drs W.E. Royer and W.A. Hendrickson) embedded in vitreous ice (1) to avoid artefacts encountered with the negative stain preparation technigue used in previous studies (2-4).Although the molecular structure is well preserved in vitreous ice, the low contrast and high noise level in the micrographs represent a serious problem in image interpretation. Moreover, the molecules can exhibit many different orientations relative to the object plane of the microscope in this type of preparation. Existing techniques of analysis requiring alignment of the molecular views relative to one or more reference images often thus yield unsatisfactory results.We use a new method in which first rotation-, translation- and mirror invariant functions (5) are derived from the large set of input images, which functions are subsequently classified automatically using multivariate statistical techniques (6). The different molecular views in the data set can therewith be found unbiasedly (5). Within each class, all images are aligned relative to that member of the class which contributes least to the classes′ internal variance (6). This reference image is thus the most typical member of the class. Finally the aligned images from each class are averaged resulting in molecular views with enhanced statistical resolution.

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