Stereo image visualization of numerically simulated turbulence

Image shifts in out-of-focus dark field images have been used in the past to determine, for example, epitaxial relationships in thin films. A recent extension of the use of dark field image shifts has been to out-of-focus images in conjunction with stereoviewing to produce an artificial stereo image effect. The technique, called through-focus dark field electron microscopy or 2-1/2D microscopy, basically involves obtaining two beam-tilted dark field images such that one is slightly over-focus and the other slightly under-focus, followed by examination of the two images through a conventional stereoviewer. The elevation differences so produced are usually unrelated to object positions in the thin foil and no specimen tilting is required.In order to produce this artificial stereo effect for the purpose of phase separation and identification, it is first necessary to select a region of the diffraction pattern containing more than just one discrete spot, with the objective aperture.

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Advantages of Complementary Stereo Images as Viewed with the Low-Temperature Field-Emission SEM

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100131206 ◽

1992 ◽

Vol 50 (2) ◽

pp. 1314-1315

Author(s):

William P. Wergin ◽

Eric F. Erbe

Keyword(s):

Fold Increase ◽

Horizontal Displacement ◽

Three Dimensions ◽

Stereo Image ◽

Stereo Pair ◽

Sem Micrograph ◽

Left And Right ◽

The Common ◽

The Brain ◽

Pair Analysis

The eye-brain complex allows those of us with normal vision to perceive and evaluate our surroundings in three-dimensions (3-D). The principle factor that makes this possible is parallax - the horizontal displacement of objects that results from the independent views that the left and right eyes detect and simultaneously transmit to the brain for superimposition. The common SEM micrograph is a 2-D representation of a 3-D specimen. Depriving the brain of the 3-D view can lead to erroneous conclusions about the relative sizes, positions and convergence of structures within a specimen. In addition, Walter has suggested that the stereo image contains information equivalent to a two-fold increase in magnification over that found in a 2-D image. Because of these factors, stereo pair analysis should be routinely employed when studying specimens.Imaging complementary faces of a fractured specimen is a second method by which the topography of a specimen can be more accurately evaluated.

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Computational Micrograph Registration with Sieve Processes

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100164660 ◽

1996 ◽

Vol 54 ◽

pp. 440-441

Author(s):

P.J. Phillips ◽

J. Huang ◽

S. M. Dunn

Keyword(s):

Planar Graph ◽

Efficient Algorithm ◽

Spatial Organization ◽

Spatial Arrangement ◽

Stereo Image ◽

Image Model ◽

Geometric Invariants ◽

Point Set

In this paper we present an efficient algorithm for automatically finding the correspondence between pairs of stereo micrographs, the key step in forming a stereo image. The computation burden in this problem is solving for the optimal mapping and transformation between the two micrographs. In this paper, we present a sieve algorithm for efficiently estimating the transformation and correspondence.In a sieve algorithm, a sequence of stages gradually reduce the number of transformations and correspondences that need to be examined, i.e., the analogy of sieving through the set of mappings with gradually finer meshes until the answer is found. The set of sieves is derived from an image model, here a planar graph that encodes the spatial organization of the features. In the sieve algorithm, the graph represents the spatial arrangement of objects in the image. The algorithm for finding the correspondence restricts its attention to the graph, with the correspondence being found by a combination of graph matchings, point set matching and geometric invariants.

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Objective quality evaluation method of stereo image based on steerable pyramid

Journal of Computer Applications ◽

10.3724/sp.j.1087.2012.00710 ◽

2013 ◽

Vol 32 (3) ◽

pp. 710-714

Author(s):

Jin-jin WEI ◽

Su-mei LI ◽

Wen-juan LIU ◽

Yan-jun ZANG

Keyword(s):

Quality Evaluation ◽

Evaluation Method ◽

Stereo Image ◽

Objective Quality ◽

Steerable Pyramid

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Region-based stereo image coding

Proceedings., International Conference on Image Processing ◽

10.1109/icip.1995.537414 ◽

2002 ◽

Cited By ~ 12

Author(s):

H. Aydinoglu ◽

F. Kossentini ◽

Qin Jiang ◽

M.H. Hayes

Keyword(s):

Image Coding ◽

Stereo Image

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Cross View Capture for Stereo Image Super-Resolution

IEEE Transactions on Multimedia ◽

10.1109/tmm.2021.3092571 ◽

2021 ◽

pp. 1-1

Author(s):

Xiangyuan Zhu ◽

Kehua Guo ◽

Hui Fang ◽

Liang Chen ◽

Sheng Ren ◽

...

Keyword(s):

Super Resolution ◽

Stereo Image ◽

Image Super Resolution

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Generative Adversarial Networks to Improve the Robustness of Visual Defect Segmentation by Semantic Networks in Manufacturing Components

Applied Sciences ◽

10.3390/app11146368 ◽

2021 ◽

Vol 11 (14) ◽

pp. 6368

Author(s):

Fátima A. Saiz ◽

Garazi Alfaro ◽

Iñigo Barandiaran ◽

Manuel Graña

Keyword(s):

Ad Hoc ◽

Data Augmentation ◽

Semantic Network ◽

Semantic Networks ◽

Stereo Image ◽

Generative Adversarial Networks ◽

Specific Class ◽

Adversarial Networks ◽

Augmentation Techniques ◽

Image Acquisition System

This paper describes the application of Semantic Networks for the detection of defects in images of metallic manufactured components in a situation where the number of available samples of defects is small, which is rather common in real practical environments. In order to overcome this shortage of data, the common approach is to use conventional data augmentation techniques. We resort to Generative Adversarial Networks (GANs) that have shown the capability to generate highly convincing samples of a specific class as a result of a game between a discriminator and a generator module. Here, we apply the GANs to generate samples of images of metallic manufactured components with specific defects, in order to improve training of Semantic Networks (specifically DeepLabV3+ and Pyramid Attention Network (PAN) networks) carrying out the defect detection and segmentation. Our process carries out the generation of defect images using the StyleGAN2 with the DiffAugment method, followed by a conventional data augmentation over the entire enriched dataset, achieving a large balanced dataset that allows robust training of the Semantic Network. We demonstrate the approach on a private dataset generated for an industrial client, where images are captured by an ad-hoc photometric-stereo image acquisition system, and a public dataset, the Northeastern University surface defect database (NEU). The proposed approach achieves an improvement of 7% and 6% in an intersection over union (IoU) measure of detection performance on each dataset over the conventional data augmentation.

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