scholarly journals Three-Dimensional Image Reconstruction for Virtual Talent Training Scene

2021 ◽  
Vol 38 (6) ◽  
pp. 1719-1726
Author(s):  
Tanbo Zhu ◽  
Die Wang ◽  
Yuhua Li ◽  
Wenjie Dong
Keyword(s):  
Image Reconstruction ◽  
Three Dimensional ◽  
Semantic Segmentation ◽  
Image Features ◽  
Practical Training ◽  
3D Image Reconstruction ◽  
Training Cost ◽  
Training Quality ◽  
Strongly Regular ◽  
Scene Depth

In real training, the training conditions are often undesirable, and the use of equipment is severely limited. These problems can be solved by virtual practical training, which breaks the limit of space, lowers the training cost, while ensuring the training quality. However, the existing methods work poorly in image reconstruction, because they fail to consider the fact that the environmental perception of actual scene is strongly regular by nature. Therefore, this paper investigates the three-dimensional (3D) image reconstruction for virtual talent training scene. Specifically, a fusion network model was deigned, and the deep-seated correlation between target detection and semantic segmentation was discussed for images shot in two-dimensional (2D) scenes, in order to enhance the extraction effect of image features. Next, the vertical and horizontal parallaxes of the scene were solved, and the depth-based virtual talent training scene was reconstructed three dimensionally, based on the continuity of scene depth. Finally, the proposed algorithm was proved effective through experiments.

Three dimensional deblurring of transmitted-light brightfield micrographs

1993 ◽  
Vol 51 ◽  
pp. 564-565
Author(s):  
Santosh Bhattacharyya
Keyword(s):  
Image Reconstruction ◽  
Three Dimensional ◽  
Transmitted Light ◽  
Reconstruction Algorithms ◽  
3D Image Reconstruction ◽  
Structural Details ◽  
Spread Function ◽  
Early Testing ◽  
Linearized Model ◽  
Image Reconstruction Algorithms

Three dimensional microscopic structures play an important role in the understanding of various biological and physiological phenomena. Structural details of neurons, such as the density, caliber and volumes of dendrites, are important in understanding physiological and pathological functioning of nervous systems. Even so, many of the widely used stains in biology and neurophysiology are absorbing stains, such as horseradish peroxidase (HRP), and yet most of the iterative, constrained 3D optical image reconstruction research has concentrated on fluorescence microscopy. It is clear that iterative, constrained 3D image reconstruction methodologies are needed for transmitted light brightfield (TLB) imaging as well. One of the difficulties in doing so, in the past, has been in determining the point spread function of the system.We have been developing several variations of iterative, constrained image reconstruction algorithms for TLB imaging. Some of our early testing with one of them was reported previously. These algorithms are based on a linearized model of TLB imaging.

scholarly journals Latent 3D Volume for Joint Depth Estimation and Semantic Segmentation from a Single Image

Sensors ◽  
2020 ◽  
Vol 20 (20) ◽  
pp. 5765 ◽  
Author(s):  
Seiya Ito ◽  
Naoshi Kaneko ◽  
Kazuhiko Sumi
Keyword(s):  
3D Structure ◽  
Three Dimensional ◽  
Semantic Segmentation ◽  
Depth Estimation ◽  
Image Features ◽  
Feature Representation ◽  
Single Image ◽  
Feature Vectors ◽  
3D Space ◽  
3D Volume

This paper proposes a novel 3D representation, namely, a latent 3D volume, for joint depth estimation and semantic segmentation. Most previous studies encoded an input scene (typically given as a 2D image) into a set of feature vectors arranged over a 2D plane. However, considering the real world is three-dimensional, this 2D arrangement reduces one dimension and may limit the capacity of feature representation. In contrast, we examine the idea of arranging the feature vectors in 3D space rather than in a 2D plane. We refer to this 3D volumetric arrangement as a latent 3D volume. We will show that the latent 3D volume is beneficial to the tasks of depth estimation and semantic segmentation because these tasks require an understanding of the 3D structure of the scene. Our network first constructs an initial 3D volume using image features and then generates latent 3D volume by passing the initial 3D volume through several 3D convolutional layers. We apply depth regression and semantic segmentation by projecting the latent 3D volume onto a 2D plane. The evaluation results show that our method outperforms previous approaches on the NYU Depth v2 dataset.

scholarly journals Performance of Cone Beam Computed Tomography Systems in Visualizing the Cortical Plate in 3D Image Reconstruction: An In Vitro Study

2018 ◽  
Vol 12 (1) ◽  
pp. 586-595 ◽  
Author(s):  
Abbas Shokri ◽  
Mohammad Reza Jamalpour ◽  
Amir Eskandarloo ◽  
Mostafa Godiny ◽  
Payam Amini ◽  
...  
Keyword(s):  
Image Reconstruction ◽  
3D Reconstruction ◽  
Cortical Bone ◽  
Three Dimensional ◽  
Cortical Plate ◽  
3D Image ◽  
Bone Thickness ◽  
Anatomical Structures ◽  
3D Image Reconstruction ◽  
Dental Procedures

Introduction: Cortical bone is an important anatomical structure and its thickness needs to be determined prior to many dental procedures to ensure treatment success. Imaging modalities are necessarily used in dentistry for treatment planning and dental procedures. Three-dimensional image reconstruction not only provides visual information but also enables accurate measurement of anatomical structures; thus, it is necessarily required for maxillofacial examination and in case of skeletal problems in this region. Aims: This study aimed to assess the ability of three Cone Beam Computed Tomography (CBCT) systems including Cranex 3D, NewTom 3G and 3D Promax for Three-Dimensional (3D) image reconstruction of the cortical plate with variable thicknesses. Methods: Depending on the cortical bone thickness, samples were evaluated in three groups of ≤ 0. 5 mm, 0.6 -1 mm and 1.1-1.5 mm cortical bone thickness. The CBCT scans were obtained from each sample using three systems, their respective FOVs, and 3D scans were reconstructed using their software programs. Two observers viewed the images twice with a two-week interval. The ability of each system in the 3D reconstruction of different thicknesses of cortical bone was determined based on its visualization on the scans. The data were analyzed using SPSS and Kappa test. Results: The three systems showed the greatest difference in the 3D reconstruction of cortical bone with < 0.5 mm thickness. Cranex 3D with 4×6 cm2 FOV had the highest and 3D Promax with 8×8 cm2 FOV had the lowest efficacy for 3D reconstruction of cortical bone. Cranex 3D with 4×6 cm2 and 6×8 cm2 FOVs and NewTom 3G with 5×5 cm2 and 8×5 cm2 FOVs showed significantly higher efficacy for 3D reconstruction of cortical bone with 0.6-1mm thickness while 3D Promax followed by NewTom 3G with 8×8 cm2 FOV had the lowest efficacy for this purpose. Conclusion: Most CBCT systems have high efficacy for 3D image reconstruction of cortical bone with thicknesses over 1 mm while they have poor efficacy for image reconstruction of cortical bone with less than 0.5 mm thickness. Thus, for accurate visualization of anatomical structures on CBCT scans, systems with smaller FOVs and consequently smaller voxel size are preferred.

Three-dimensional transmitted brightfield imaging: pragmatic data collection and preprocessing considerations

1993 ◽  
Vol 51 ◽  
pp. 276-277
Author(s):  
J.A. Cooper ◽  
S. Bhattacharyya ◽  
J.N. Turner ◽  
T.J. Holmes
Keyword(s):  
Image Reconstruction ◽  
Data Collection ◽  
3D Visualization ◽  
Three Dimensional ◽  
Reconstruction Algorithm ◽  
3D Image ◽  
Serial Sectioning ◽  
Correlative Analysis ◽  
3D Image Reconstruction ◽  
Imaging Research

We have been developing algorithms for 3D image reconstruction of biological specimens with absorbing stains. This is important because there are many absorbing stains which are widely used in conjunction with transmitted light brightfield (TLB) microscopy, yet most of the 3D microscopic imaging research has been directed toward fluorescence microscopy. For instance, horseradish peroxidase (HRP) is used widely in the neurosciences for its many advantages as a tracer and intracellular marker. It is readily injected into individual neurons, transported long distances, and fills both the dendritic and axonal fields, while it may double as an electron microscopy stain for correlative analysis. With such advantages, it is clear that absorbing stains will continue to be widely used. Their utility will furthermore broaden with 3D visualization and quantitation.The main principles behind our methodology are the following. Standard optical serial sectioning data collection is used. The iterative, constrained image reconstruction algorithm is designed to reconstruct the 3D optical density distribution .

Fast 3D image reconstruction algorithm based on artificial intelligence technology

2021 ◽  
pp. 002072092098504
Author(s):  
An Weigang ◽  
Pan Jinxiao
Keyword(s):  
Artificial Intelligence ◽  
High Resolution ◽  
Image Reconstruction ◽  
Three Dimensional ◽  
Feature Analysis ◽  
3D Image ◽  
3D Images ◽  
Dimensional Image ◽  
Fine Grained ◽  
3D Image Reconstruction

In order to improve the 3D reconstruction capability of high-resolution fine-grained 3D images, a fast 3D image reconstruction algorithm based on artificial intelligence technology is proposed. The cross-gradient sharpening detection method is used to collect features and extract information from high-resolution fine-grained three-dimensional images, and establish an edge contour feature detection model for high-resolution fine-grained three-dimensional images. Combining the salient feature analysis method and the subspace feature analysis method to cluster and analyze the high-resolution fine-grained three-dimensional image. In the artificial intelligence environment, the saliency of the three-dimensional image is detected and analyzed, and the multi-dimensional segmentation and gray histogram of the high-resolution fine-grained three-dimensional image are reconstructed through the subspace segmentation method. According to the reconstruction results of the gray histogram, fast 3D image reconstruction and image fusion processing are performed. Finally, the accurate detection and recognition of the reconstructed image is realized. The simulation results show that this method has a good effect on 3D image reconstruction, and the time cost of image reconstruction is relatively short. It improves the recognition and feature analysis capabilities of high-resolution fine-grained 3D images, and has good application value in the reconstruction, detection and recognition of high-resolution fine-grained 3D images.

scholarly journals Treatment of Giant Left Atrial Diverticulum: A Case Report and Literature Review

2020 ◽  
Vol 23 (2) ◽  
pp. E098-E100
Author(s):  
Hongmu Li ◽  
Sunang Yan ◽  
Haibo Liu
Keyword(s):  
Case Report ◽  
Image Reconstruction ◽  
Literature Review ◽  
Left Atrial ◽  
Clinical Characteristics ◽  
Three Dimensional ◽  
3D Image ◽  
3D Image Reconstruction ◽  
Surgical Positioning ◽  
Left Atrial Diverticulum

In this paper, we present a giant left atrial diverticulum (LAD) in a 10-year-old girl, whose three-dimensional (3D) image reconstruction was used to help diagnosis and surgical positioning. Previously reported cases were reviewed, and the clinical characteristics of this disease also was summarized to improve the diagnosis and treatment of LAD.

scholarly journals Reconstruction of Three-Dimensional Images Based on Estimation of Spinning Target Parameters in Radar Network

2018 ◽  
Vol 10 (12) ◽  
pp. 1997 ◽  
Author(s):  
Xiao-wen Liu ◽  
Qun Zhang ◽  
Lei Jiang ◽  
Jia Liang ◽  
Yi-jun Chen
Keyword(s):  
Image Reconstruction ◽  
Radon Transform ◽  
Three Dimensional ◽  
3D Image ◽  
3D Image Reconstruction ◽  
Radar Network ◽  
Imaging Result ◽  
2D Imaging ◽  
Reconstruction Model ◽  
Imaging Plane

A high-resolution three-dimensional (3D) image reconstruction method for a spinning target is proposed in this paper and the anisotropy is overcome by fusing different observation information acquired from the radar network. The proposed method will reconstruct the 3D scattering distribution, and the mapping of the reconstructed 3D image onto the imaging plane is identical to the two-dimensional (2D) imaging result. At first, the range compression and inverse radon transform is employed to produce the 2D image of the spinning target. In addition, the process of mapping the spinning target onto the imaging plane is analyzed and the mapping formulas which are to map the point onto the 2D image plane are derived. After the micro-Doppler signature about which every reconstructed point in 2D imaging result is extracted by the Radon transform, the extended Hough transform is adopted to calculate an important parameter about the micro-Doppler signature, and the 3D image reconstruction model for the spinning target is constructed based on the radar network. Finally, the algorithm for solving the reconstruction model is proposed and the 3D image of the spinning target is obtained. Some simulation results are given to illustrate the effectiveness of the proposed method, and results show that the mean square error (MSE) relatively holds a steady trend when the signal-to-noise ratio (SNR) is higher than −10 dB and the MSE of the reconstructed 3D target image is less than 0.15 when SNR is at the level of −10 dB.

FAST RECONSTRUCTION METHOD BASED ON COMMON UNIFIED DEVICE ARCHITECTURE (CUDA) FOR MICRO-CT

2010 ◽  
Vol 03 (01) ◽  
pp. 39-43 ◽  
Author(s):  
GUOTAO QUAN ◽  
TANGYOU SUN ◽  
YONG DENG
Keyword(s):  
Image Reconstruction ◽  
Graphics Processing Units ◽  
Three Dimensional ◽  
Reconstruction Method ◽  
Micro Ct ◽  
3D Image ◽  
Reconstruction Process ◽  
Device Architecture ◽  
3D Image Reconstruction ◽  
Fdk Algorithm

Three-dimensional image reconstruction with Feldkamp, Davis, and Kress (FDK) algorithm is the most time consuming part in Micro-CT. The parallel algorithm based on the computer cluster is capable of accelerating image reconstruction speed; however, the hardware is very expensive. In this paper, using the most current graphics processing units (GPU), we present a method based on common unified device architecture (CUDA) for speeding up the Micro-CT image reconstruction process. The most time consuming filtering and back-projection parts of the FDK algorithm are parallelized for the CUDA architecture. The CUDA-based reconstruction speed and image qualities are compared with CPU results for the projecting data of the Micro-CT system. The results show that the 3D image reconstruction speed based on CUDA is ten times faster than the speed with CPU. In conclusion the FDK algorithm based on CUDA for Micro-CT can reconstruct the 3D image right after the end of data acquisition.

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