scholarly journals Layout visualization algorithms

2021 ◽  
pp. 353-359
Keyword(s):  
Visualization Algorithms

CPU-GPU Rendering of CT Scan Images for Vertebra Reconstruction from CT Scan Images with a Calibration Policy

2020 ◽  
pp. 1-5
Author(s):  
Usman Khan ◽  
Usman Khan ◽  
AmanUllah Yasin ◽  
Imran Shafi ◽  
Muhammad Abid
Keyword(s):  
3D Reconstruction ◽  
Ct Scan ◽  
Surface Reconstruction ◽  
3D Visualization ◽  
Cervical Vertebra ◽  
Marching Cubes ◽  
Affine Transform ◽  
Visualization Techniques ◽  
Visualization Algorithms ◽  
Gpu Implementation

In this work GPU implementation of classic 3D visualization algorithms namely Marching Cubes and Raycasting has been carried for cervical vertebra using VTK libraries. A proposed framework has been introduced for efficient and duly calibrated 3D reconstruction using Dicom Affine transform and Python Mayavi framework to address the limitation of benchmark visualization techniques i.e. lack of calibration, surface reconstruction artifacts and latency.

Top Museums on Instagram

2019 ◽  
Vol 3 (2) ◽  
pp. 18-42 ◽  
Author(s):  
Vasiliki G. Vrana ◽  
Dimitrios A. Kydros ◽  
Evangelos C. Kehris ◽  
Anastasios-Ioannis T. Theocharidis ◽  
George I. Kavavasilis
Keyword(s):  
Social Network ◽  
Network Analysis ◽  
Small World ◽  
Marketing Strategies ◽  
The Other ◽  
Small World Network ◽  
Scale Free ◽  
Centrality Metrics ◽  
Photo Sharing ◽  
Visualization Algorithms

Pictures speak louder than words. In this fast-moving world where people hardly have time to read anything, photo-sharing sites become more and more popular. Instagram is being used by millions of people and has created a “sharing ecosystem” that also encourages curation, expression, and produces feedback. Museums are moving quickly to integrate Instagram into their marketing strategies, provide information, engage with audience and connect to other museums Instagram accounts. Taking into consideration that people may not see museum accounts in the same way that the other museum accounts do, the article first describes accounts' performance of the top, most visited museums worldwide and next investigates their interconnection. The analysis uses techniques from social network analysis, including visualization algorithms and calculations of well-established metrics. The research reveals the most important modes of the network by calculating the appropriate centrality metrics and shows that the network formed by the museum Instagram accounts is a scale–free small world network.

scholarly journals Intelligent Algorithms-Based CT Image Segmentation in Patients with Cardiovascular Diseases and Realization of Visualization Algorithms

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Xianhua Huang
Keyword(s):  
Cardiovascular Diseases ◽  
Blood Vessel ◽  
Ct Images ◽  
Ct Image ◽  
Segmentation Method ◽  
Intelligent Algorithms ◽  
Curved Planar Reformation ◽  
Cylinder Model ◽  
Visualization Algorithms ◽  
Coronary Artery Tree

The study focused on the intelligent algorithms-based segmentation of computed tomography (CT) images of patients with cardiovascular diseases (CVD) and the realization of visualization algorithms. The first step was to design a method for precise segmentation under the cylinder model based on the coronary body data of the coarse segmentation, and then the principles of different visualization algorithms were discussed. The results showed that the precise segmentation method can effectively eliminate most of the branches and calcified lesions; curved planar reformation (CPR) and straightened CPR can display the entire blood vessel on one image; and spherical CPR can display the complete coronary artery tree on an image, so that a problem with a certain blood vessel can be quickly found. In conclusion, the precise segmentation of CT images of CVD and visualization algorithm based on the cylinder model have clinical significance in the diagnosis of CVD.

Visualization-Based Decision Support Systems

2013 ◽  
pp. 162-180
Author(s):  
Vicki L. Sauter ◽  
Srikanth Mudigonda ◽  
Ashok Subramanian ◽  
Ray Creely
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Support Systems ◽  
Lessons Learned ◽  
Decision Makers ◽  
Specific Information ◽  
Visualization Software ◽  
General Rules ◽  
Visualization Technology ◽  
Visualization Algorithms

Increasingly, decision makers are incorporating large quantities of interrelated data in their decision making. Decision support systems need to provide visualization tools to help decision makers glean trends and patterns that will help them design and evaluate alternative actions. While visualization software that might be incorporated into decision support systems is available, the literature does not provide sufficient guidelines for selecting among possible visualizations or their attributes. This paper describes a case study of the development of a visualization component to represent regional relationship data. It addresses the specific information goals of the target organization, various constraints that needed to be satisfied, and how the goals were achieved via a suitable choice of visualization technology and visualization algorithms. The development process highlighted the need for specific visualizations to be driven by the specific problem characteristics as much as general rules of visualization. Lessons learned during the process and how these lessons may be generalized to address similar requirements is presented.

scholarly journals Visualizing Near Infrared Hyperspectral Images with Generative Adversarial Networks

2020 ◽  
Vol 12 (23) ◽  
pp. 3848
Author(s):  
Rongxin Tang ◽  
Hualin Liu ◽  
Jingbo Wei
Keyword(s):  
Near Infrared ◽  
Hyperspectral Image ◽  
Geographic Location ◽  
Hyperspectral Images ◽  
Generative Adversarial Networks ◽  
Landsat 8 ◽  
Multispectral Images ◽  
Adversarial Network ◽  
Adversarial Networks ◽  
Visualization Algorithms

The visualization of near infrared hyperspectral images is valuable for quick view and information survey, whereas methods using band selection or dimension reduction fail to produce good colors as reasonable as corresponding multispectral images. In this paper, an end-to-end neural network of hyperspectral visualization is proposed, based on the convolutional neural networks, to transform a hyperspectral image of hundreds of near infrared bands to a three-band image. Supervised learning is used to train the network where multispectral images are targeted to reconstruct naturally looking images. Each pair of the training images shares the same geographic location and similar moments. The generative adversarial framework is used with an adversarial network to improve the training of the generating network. In the experimental procedure, the proposed method is tested for the near infrared bands of EO-1 Hyperion images with LandSat-8 images as the benchmark, which is compared with five state-of-the-art visualization algorithms. The experimental results show that the proposed method performs better in producing naturally looking details and colors for near infrared hyperspectral images.

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