Acquisition of three-dimensional coordinates of objects from Axi-Vision image data and application to stereoscopic display using integral photography system

2007 ◽  
Author(s):  
Tahito Aida ◽  
Akira Yoneda ◽  
Takeshi Uragaki ◽  
Masahiro Kawakita
Keyword(s):  
Three Dimensional ◽  
Image Data ◽  
Stereoscopic Display

High-speed brightfield 3-D imaging and 3-D image analysis of thick and overlapped cell clusters in cytological preparations

1994 ◽  
Vol 52 ◽  
pp. 218-219
Author(s):  
Robert W. Mackin
Keyword(s):  
Image Analysis ◽  
High Speed ◽  
Three Dimensional ◽  
Image Data ◽  
Ccd Camera ◽  
Objective Lens ◽  
Array Processor ◽  
Vaginal Smears ◽  
Low Contrast ◽  
Computer Controlled

This paper presents two advances towards the automated three-dimensional (3-D) analysis of thick and heavily-overlapped regions in cytological preparations such as cervical/vaginal smears. First, a high speed 3-D brightfield microscope has been developed, allowing the acquisition of image data at speeds approaching 30 optical slices per second. Second, algorithms have been developed to detect and segment nuclei in spite of the extremely high image variability and low contrast typical of such regions. The analysis of such regions is inherently a 3-D problem that cannot be solved reliably with conventional 2-D imaging and image analysis methods.High-Speed 3-D imaging of the specimen is accomplished by moving the specimen axially relative to the objective lens of a standard microscope (Zeiss) at a speed of 30 steps per second, where the stepsize is adjustable from 0.2 - 5μm. The specimen is mounted on a computer-controlled, piezoelectric microstage (Burleigh PZS-100, 68/μm displacement). At each step, an optical slice is acquired using a CCD camera (SONY XC-11/71 IP, Dalsa CA-D1-0256, and CA-D2-0512 have been used) connected to a 4-node array processor system based on the Intel i860 chip.

scholarly journals Generation and Annotation of Simulation-Real Ship Images for Convolutional Neural Networks Training and Testing

2021 ◽  
Vol 11 (13) ◽  
pp. 5931
Author(s):  
Ji’an You ◽  
Zhaozheng Hu ◽  
Chao Peng ◽  
Zhiqiang Wang
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Image Annotation ◽  
Three Dimensional ◽  
Image Data ◽  
Detection Algorithm ◽  
Simulation Software ◽  
High Quality ◽  
Annotation Method ◽  
Detection Of Objects

Large amounts of high-quality image data are the basis and premise of the high accuracy detection of objects in the field of convolutional neural networks (CNN). It is challenging to collect various high-quality ship image data based on the marine environment. A novel method based on CNN is proposed to generate a large number of high-quality ship images to address this. We obtained ship images with different perspectives and different sizes by adjusting the ships’ postures and sizes in three-dimensional (3D) simulation software, then 3D ship data were transformed into 2D ship image according to the principle of pinhole imaging. We selected specific experimental scenes as background images, and the target ships of the 2D ship images were superimposed onto the background images to generate “Simulation–Real” ship images (named SRS images hereafter). Additionally, an image annotation method based on SRS images was designed. Finally, the target detection algorithm based on CNN was used to train and test the generated SRS images. The proposed method is suitable for generating a large number of high-quality ship image samples and annotation data of corresponding ship images quickly to significantly improve the accuracy of ship detection. The annotation method proposed is superior to the annotation methods that label images with the image annotation software of Label-me and Label-img in terms of labeling the SRS images.

An efficient approach to converting three-dimensional image data into highly accurate computational models

2008 ◽  
Vol 366 (1878) ◽  
pp. 3155-3173 ◽  
Author(s):  
P.G Young ◽  
T.B.H Beresford-West ◽  
S.R.L Coward ◽  
B Notarberardino ◽  
B Walker ◽  
...  
Keyword(s):  
Mesh Generation ◽  
Computational Models ◽  
Three Dimensional ◽  
Image Data ◽  
Model Material ◽  
Imaging Data ◽  
Material Inhomogeneity ◽  
Dimensional Image ◽  
Wide Range ◽  
Impact Modelling

Image-based meshing is opening up exciting new possibilities for the application of computational continuum mechanics methods (finite-element and computational fluid dynamics) to a wide range of biomechanical and biomedical problems that were previously intractable owing to the difficulty in obtaining suitably realistic models. Innovative surface and volume mesh generation techniques have recently been developed, which convert three-dimensional imaging data, as obtained from magnetic resonance imaging, computed tomography, micro-CT and ultrasound, for example, directly into meshes suitable for use in physics-based simulations. These techniques have several key advantages, including the ability to robustly generate meshes for topologies of arbitrary complexity (such as bioscaffolds or composite micro-architectures) and with any number of constituent materials (multi-part modelling), providing meshes in which the geometric accuracy of mesh domains is only dependent on the image accuracy (image-based accuracy) and the ability for certain problems to model material inhomogeneity by assigning the properties based on image signal strength. Commonly used mesh generation techniques will be compared with the proposed enhanced volumetric marching cubes (EVoMaCs) approach and some issues specific to simulations based on three-dimensional image data will be discussed. A number of case studies will be presented to illustrate how these techniques can be used effectively across a wide range of problems from characterization of micro-scaffolds through to head impact modelling.

scholarly journals Deep learning-based estimation of Flory–Huggins parameter of A–B block copolymers from cross-sectional images of phase-separated structures

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Katsumi Hagita ◽  
Takeshi Aoyagi ◽  
Yuto Abe ◽  
Shinya Genda ◽  
Takashi Honda
Keyword(s):  
Deep Learning ◽  
Diblock Copolymers ◽  
Three Dimensional ◽  
Image Data ◽  
Real Space ◽  
Cross Sectional ◽  
Regression Problem ◽  
Self Consistent Field ◽  
Experimental Image ◽  
Chain Lengths

AbstractIn this study, deep learning (DL)-based estimation of the Flory–Huggins χ parameter of A-B diblock copolymers from two-dimensional cross-sectional images of three-dimensional (3D) phase-separated structures were investigated. 3D structures with random networks of phase-separated domains were generated from real-space self-consistent field simulations in the 25–40 χN range for chain lengths (N) of 20 and 40. To confirm that the prepared data can be discriminated using DL, image classification was performed using the VGG-16 network. We comprehensively investigated the performances of the learned networks in the regression problem. The generalization ability was evaluated from independent images with the unlearned χN. We found that, except for large χN values, the standard deviation values were approximately 0.1 and 0.5 for A-component fractions of 0.2 and 0.35, respectively. The images for larger χN values were more difficult to distinguish. In addition, the learning performances for the 4-class problem were comparable to those for the 8-class problem, except when the χN values were large. This information is useful for the analysis of real experimental image data, where the variation of samples is limited.

Analysis of Three-Dimensional Image Data: Display and Feature Tracking

1992 ◽  
pp. 237-256 ◽  
Author(s):  
Zvi Kam ◽  
Hans Chen ◽  
John W. Sedat ◽  
David A. Agard
Keyword(s):  
Feature Tracking ◽  
Three Dimensional ◽  
Image Data ◽  
Data Display ◽  
Dimensional Image

Three-dimensional reconstruction of metabolic data from quantitative autoradiography of rat brain

1984 ◽  
Vol 247 (3) ◽  
pp. E412-E419 ◽  
Author(s):  
L. S. Hibbard ◽  
R. A. Hawkins
Keyword(s):  
High Speed ◽  
Three Dimensional ◽  
Image Data ◽  
Brain Structures ◽  
Three Dimensions ◽  
Quantitative Autoradiography ◽  
Alignment Algorithm ◽  
Computer Methods ◽  
Mathematical Properties ◽  
Dimensional Reconstruction

Quantitative autoradiography is a powerful method for studying brain function by the determination of blood flow, glucose utilization, or transport of essential nutrients. Autoradiographic images contain vast amounts of potentially useful information, but conventional analyses can practically sample the data at only a small number of points arbitrarily chosen by the experimenter to represent discrete brain structures. To use image data more fully, computer methods for its acquisition, storage, quantitative analysis, and display are required. We have developed a system of computer programs that performs these tasks and has the following features: 1) editing and analysis of single images using interactive graphics, 2) an automatic image alignment algorithm that places images in register with one another using only the mathematical properties of the images themselves, 3) the calculation of mean images from equivalent images in different experimental serial image sets, 4) the calculation of difference images (e.g., experiment-minus-control) with the option to display only differences estimated to be statistically significant, and 5) the display of serial image metabolic maps reconstructed in three dimensions using a high-speed computer graphics system.

scholarly journals Current Applications, Opportunities, and Limitations of AI for 3D Imaging in Dental Research and Practice

2020 ◽  
Vol 17 (12) ◽  
pp. 4424
Author(s):  
Kuofeng Hung ◽  
Andy Wai Kan Yeung ◽  
Ray Tanaka ◽  
Michael M. Bornstein
Keyword(s):  
Treatment Planning ◽  
3D Imaging ◽  
Imaging Techniques ◽  
Current Trend ◽  
Three Dimensional ◽  
Image Data ◽  
Anatomical Landmarks ◽  
Automated Diagnosis ◽  
Dental Practitioners ◽  
Facial Scanning

The increasing use of three-dimensional (3D) imaging techniques in dental medicine has boosted the development and use of artificial intelligence (AI) systems for various clinical problems. Cone beam computed tomography (CBCT) and intraoral/facial scans are potential sources of image data to develop 3D image-based AI systems for automated diagnosis, treatment planning, and prediction of treatment outcome. This review focuses on current developments and performance of AI for 3D imaging in dentomaxillofacial radiology (DMFR) as well as intraoral and facial scanning. In DMFR, machine learning-based algorithms proposed in the literature focus on three main applications, including automated diagnosis of dental and maxillofacial diseases, localization of anatomical landmarks for orthodontic and orthognathic treatment planning, and general improvement of image quality. Automatic recognition of teeth and diagnosis of facial deformations using AI systems based on intraoral and facial scanning will very likely be a field of increased interest in the future. The review is aimed at providing dental practitioners and interested colleagues in healthcare with a comprehensive understanding of the current trend of AI developments in the field of 3D imaging in dental medicine.

scholarly journals USE OF IMAGE BASED MODELLING FOR DOCUMENTATION OF INTRICATELY SHAPED OBJECTS

2016 ◽  
Vol XLI-B5 ◽  
pp. 327-334
Author(s):  
M. Marčiš ◽  
P. Barták ◽  
D. Valaška ◽  
M. Fraštia ◽  
O. Trhan
Keyword(s):  
Laser Scanning ◽  
Three Dimensional ◽  
Image Data ◽  
3D Models ◽  
Architectural Elements ◽  
Advantages And Disadvantages ◽  
Interior Spaces ◽  
Automated Processing ◽  
2D Drawing ◽  
Photogrammetric Measurement

In the documentation of cultural heritage, we can encounter three dimensional shapes and structures which are complicated to measure. Such objects are for example spiral staircases, timber roof trusses, historical furniture or folk costume where it is nearly impossible to effectively use the traditional surveying or the terrestrial laser scanning due to the shape of the object, its dimensions and the crowded environment. The actual methods of digital photogrammetry can be very helpful in such cases with the emphasis on the automated processing of the extensive image data. The created high resolution 3D models and 2D orthophotos are very important for the documentation of architectural elements and they can serve as an ideal base for the vectorization and 2D drawing documentation. This contribution wants to describe the various usage of image based modelling in specific interior spaces and specific objects. The advantages and disadvantages of the photogrammetric measurement of such objects in comparison to other surveying methods are reviewed.

scholarly journals DETECTION OF CRACKS IN PAVED ROAD SURFACE USING LASER SCAN IMAGE DATA

2016 ◽  
Vol XLI-B1 ◽  
pp. 559-562 ◽  
Author(s):  
J. Choi ◽  
L. Zhu ◽  
H. Kurosu
Keyword(s):  
Image Segmentation ◽  
Background Subtraction ◽  
Three Dimensional ◽  
Laser Scanner ◽  
Image Data ◽  
Road Surface ◽  
Surface Model ◽  
Rolling Ball ◽  
The Difference ◽  
Paved Road

In the current study, we developed a methodology for detecting cracks in the surface of paved road using 3D digital surface model of road created by measuring with three-dimensional laser scanner which works on the basis of the light-section method automatically. For the detection of cracks from the imagery data of the model, the background subtraction method (Rolling Ball Background Subtraction Algorithm) was applied to the data for filtering out the background noise originating from the undulation and gradual slope and also for filtering the ruts that were caused by wearing, aging and excessive use of road and other reasons. We confirmed the influence from the difference in height (depth) caused by forgoing reasons included in a data can be reduced significantly at this stage. Various parameters of ball radius were applied for checking how the result of data obtained with this process vary according to the change of parameter and it becomes clear that there are not important differences by the change of parameters if they are in a certain range radius. And then, image segmentation was performed by multi-resolution segmentation based on the object-based image analysis technique. The parameters for the image segmentation, scale, pixel value (height/depth) and the compactness of objects were used. For the classification of cracks in the database, the height, length and other geometric property are used and we confirmed the method is useful for the detection of cracks in a paved road surface.

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