scholarly journals 3D Object's shape and motion recovery using stereo image and Paraperspective Camera Model

2003 ◽  
Vol 10B (2) ◽  
pp. 135-142
Keyword(s):  
Stereo Image ◽  
Camera Model ◽  
Motion Recovery

Applications of Through-Focus Dark Field Image Shifts to Phase Identification

1975 ◽  
Vol 33 ◽  
pp. 178-179
Author(s):  
Prakash Rao
Keyword(s):  
Dark Field Image ◽  
Thin Foil ◽  
Dark Field ◽  
The Other ◽  
Stereo Image ◽  
Phase Identification ◽  
The Past ◽  
Field Electron Microscopy ◽  
Dark Field Electron ◽  
Recent Extension

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.

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

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.

Merging of electron-diffraction intensities acquired with a slow-scan CCD camera of crotoxin complex crystals to 3.5Å resolution

1994 ◽  
Vol 52 ◽  
pp. 104-105
Author(s):  
Jaap Brink ◽  
Wah Chiu
Keyword(s):  
Electron Diffraction ◽  
Ccd Camera ◽  
Crystal Thickness ◽  
Local Contrast ◽  
Camera Model ◽  
Data Set ◽  
3 Dimensional ◽  
Diffraction Mode ◽  
Diffraction Patterns ◽  
Complex Crystals

Crotoxin complex is the principal neurotoxin of the South American rattlesnake, Crotalus durissus terrificus and has a molecular weight of 24 kDa. The protein is a heterodimer with subunit A assigneda chaperone function. Subunit B carries the lethal activity, which is exerted on both sides ofthe neuro-muscular junction, and which is thought to involve binding to the acetylcholine receptor. Insight in crotoxin complex’ mode of action can be gained from a 3 Å resolution structure obtained by electron crystallography. This abstract communicates our progress in merging the electron diffraction amplitudes into a 3-dimensional (3D) intensity data set close to completion. Since the thickness of crotoxin complex crystals varies from one crystal to the other, we chose to collect tilt series of electron diffraction patterns after determining their thickness. Furthermore, by making use of the symmetry present in these tilt data, intensities collected only from similar crystals will be merged.Suitable crystals of glucose-embedded crotoxin complex were searched for in the defocussed diffraction mode with the goniometer tilted to 55° of higher in a JEOL4000 electron cryo-microscopc operated at 400 kV with the crystals kept at -120°C in a Gatan 626 cryo-holder. The crystal thickness was measured using the local contrast of the crystal relative to the supporting film from search-mode images acquired using a 1024 x 1024 slow-scan CCD camera (model 679, Gatan Inc.).

Computational Micrograph Registration with Sieve Processes

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.

Skin Chromophore Estimation from Mobile Selfie Images using Constrained Independent Component Analysis

2020 ◽  
Vol 2020 (14) ◽  
pp. 357-1-357-6
Author(s):  
Luisa F. Polanía ◽  
Raja Bala ◽  
Ankur Purwar ◽  
Paul Matts ◽  
Martin Maltz
Keyword(s):  
Independent Component Analysis ◽  
Spectral Reflectance ◽  
Source Separation ◽  
Principal Component ◽  
Component Analysis ◽  
Previous Method ◽  
Independent Component ◽  
Light Transport ◽  
Camera Model ◽  
Constrained Independent Component Analysis

Human skin is made up of two primary chromophores: melanin, the pigment in the epidermis giving skin its color; and hemoglobin, the pigment in the red blood cells of the vascular network within the dermis. The relative concentrations of these chromophores provide a vital indicator for skin health and appearance. We present a technique to automatically estimate chromophore maps from RGB images of human faces captured with mobile devices such as smartphones. The ultimate goal is to provide a diagnostic aid for individuals to monitor and improve the quality of their facial skin. A previous method approaches the problem as one of blind source separation, and applies Independent Component Analysis (ICA) in camera RGB space to estimate the chromophores. We extend this technique in two important ways. First we observe that models for light transport in skin call for source separation to be performed in log spectral reflectance coordinates rather than in RGB. Thus we transform camera RGB to a spectral reflectance space prior to applying ICA. This process involves the use of a linear camera model and Principal Component Analysis to represent skin spectral reflectance as a lowdimensional manifold. The camera model requires knowledge of the incident illuminant, which we obtain via a novel technique that uses the human lip as a calibration object. Second, we address an inherent limitation with ICA that the ordering of the separated signals is random and ambiguous. We incorporate a domain-specific prior model for human chromophore spectra as a constraint in solving ICA. Results on a dataset of mobile camera images show high quality and unambiguous recovery of chromophores.

Objective quality evaluation method of stereo image based on steerable pyramid

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

Region-based stereo image coding

2002 ◽  
Author(s):  
H. Aydinoglu ◽  
F. Kossentini ◽  
Qin Jiang ◽  
M.H. Hayes
Keyword(s):  
Image Coding ◽  
Stereo Image

scholarly journals Cross View Capture for Stereo Image Super-Resolution

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

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

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.

Sign in / Sign up

Export Citation Format

Share Document