scholarly journals High-Quality On-Patient Medical Data Visualization in a Markerless Augmented Reality Environment

2014 ◽  
Vol 5 (3) ◽  
pp. 1
Author(s):  
Márcio Cerqueira de Farias Macedo ◽  
Antônio Lopes Apolinário Júnior ◽  
Antonio Carlos dos Santos Souza ◽  
Gilson Antônio Giraldi
Keyword(s):  
Augmented Reality ◽  
Real Time ◽  
Data Visualization ◽  
Volume Rendering ◽  
Reference Model ◽  
Region Of Interest ◽  
Image Data ◽  
Visual Quality ◽  
Medical Data ◽  
High Quality

To provide on-patient medical data visualization, a medical augmented reality environment must support volume rendering, accurate tracking, real-time performance and high visual quality in the final rendering. Another interesting feature is markerless registration, to solve the intrusiveness introduced by the use of fiducial markers for tracking. In this paper we address the problem of on-patient medical data visualization in a real-time high-quality markerless augmented reality environment. The medical data consists of a volume reconstructed from 3D computed tomography image data. Markerless registration is done by generating a 3D reference model of the region of interest in the patient and tracking it from the depth stream of an RGB-D sensor. From the estimated camera pose, the volumetric medical data and the reference model are combined allowing a visualization of the patient as well as part of his anatomy. To improve the visual perception of the scene, focus+context visualization is used in the augmented reality scene to dynamically define which parts of the medical volume will be visualized in the context of the patient’s image. Moreover, context-preserving volume rendering is employed to dynamically control which parts of the volume will be rendered. The results obtained show that the markerless environment runs in real-time and the techniques applied greatly improve the visual quality of the final rendering.

Augmented Reality Overlay System for Computer-guided Needle Procedures

1969 ◽  
Author(s):  
Zachary Baum
Keyword(s):  
Augmented Reality ◽  
Real Time ◽  
Region Of Interest ◽  
Calibration Method ◽  
Position Error ◽  
Ct Image ◽  
Entire System ◽  
Projected Image ◽  
Needle Interventions

Purpose: Augmented reality overlay systems can be used to project a CT image directly onto a patient during procedures. They have been actively trialed for computer-guided procedures, however they have not become commonplace in practice due to restrictions of previous systems. Previous systems have not been handheld, and have had complicated calibration procedures. We put forward a handheld tablet-based system for assisting with needle interventions. Methods: The system consists of a tablet display and a 3-D printed reusable and customizable frame. A simple and accurate calibration method was designed to align the patient to the projected image. The entire system is tracked via camera, with respect to the patient, and the projected image is updated in real time as the system is moved around the region of interest. Results: The resulting system allowed for 0.99mm mean position error in the plane of the image, and a mean position error of 0.61mm out of the plane of the image. This accuracy was thought to be clinically acceptable for tool using computer-guidance in several procedures that involve musculoskeletal needle placements. Conclusion: Our calibration method was developed and tested using the designed handheld system. Our results illustrate the potential for the use of augmented reality handheld systems in computer-guided needle procedures. 

Augmented Reality Based Real-Time Data Visualization Concept for Ultra-Efficient Car Prototype

2017 ◽  
Author(s):  
Peter Wozniak ◽  
Oliver Vauderwange ◽  
Nicolas Javahiraly ◽  
Dan Curticapean
Keyword(s):  
Augmented Reality ◽  
Real Time ◽  
Data Visualization ◽  
Time Data ◽  
Real Time Data

scholarly journals Pengaruh Konversi Nurbs Ke Polygonal Pada Desain Mobil 3d Terhadap Penilaian Kualitas 3d Model

2021 ◽  
Vol 6 (2) ◽  
pp. 119
Author(s):  
Awaludin Abid ◽  
Kusrini Kusrini ◽  
Amir Fatah Sofyan
Keyword(s):  
Virtual Reality ◽  
Augmented Reality ◽  
Real Time ◽  
Hausdorff Distance ◽  
Visual Quality ◽  
Quality Metrics ◽  
Data Preparation ◽  
Mesh Quality ◽  
Output Data ◽  
3D Mesh

Di Industri otomotif, biaya prototyping meningkat berbanding lurus dengan kompleksitas dan dependensi kendaraan. Sebagai alternatif untuk prototyping fisik dapat memanfaatkan teknologi baru seperti Augmented Reality (AR) dan Virtual Reality (VR) digunakan. Penggunaan VR dan AR melibatkan real-time rendering data CAD yang mengkonsumsi banyak memori dan mengurangi kinerja aplikasi. Persiapan data memiliki peran penting untuk meningkatkan kinerja sementara tetap mempertahankan topologi dan kualitas mesh. Proses optimalisasi data CAD yang digunakan yaitu Tessellation atau mengkonversi NURBS ke Polygons, berperan untuk menghasilkan output data yang memiliki efisien kinerja dengan topologi serta kualitas mesh yang baik. Hadirnya software 3D Data preparation dan optimasi pada kelas Tessellator. Autodesk Maya merupakan software pemodelan 3D yang mendukung Non-Uniform Rational Basis Spline ataupun CAD memiliki fitur mengkonversi model NURBS ke polygons, pemilihan kebutuhan atau requirement pada tessellation berpengaruh terhadap hasil output. Penilaian dilakukan menggunakan penilaian Objektif menggunakan 3D mesh visual quality metrics berbasis vertex-position Hausdorff Distance sehingga didapatkan requirement pada Tessellation yang efektif. Hasil dari konversi memiliki topologi yang serupa dengan software khusus data preparation dan optimasi, sedangkan hasil penilaian mesh visual quality metrics requirement yang mendekati yaitu menggunakan Tessellation Method Count dan General. Kata Kunci— Tessellation, Mesh Visual Quality, CAD, Polygon In automotive industry, cost of prototyping increases directly with complexity and dependencies of vehicle. As an alternative to physical prototyping can utilize new technologies such as Augmented Reality (AR) and Virtual Reality (VR) are used. And involves the real-time rendering of CAD data which consumes a lot of memory and reduces application performance. Data preparation has an important role to improve performance while maintaining topology and mesh quality. Process of optimizing CAD data used is Tessellation or converting NURBS to Polygons, whose role is to produce output data that has an efficient performance with topology and good mesh quality. Autodesk Maya is a 3D modeling software that supports Non-Uniform Rational Base Spline or CAD which has the feature of converting NURBS models to polygons, the selection of requirements or requirements on tessellation influences the output results. The assessment is done using objective assessment with 3D mesh visual quality metrics based on Hausdorff Distance vertex-position so that the requirements for effective Tessellation are obtained. The results of the conversion have a topology similar to special data preparation and optimization software, while the results of the mesh visual quality metrics requirement approach are close to using the Count and General Tessellation method. Keywords— Tessellation, Mesh Visual Quality, CAD, Polygon

Augmented Reality: A Balancing Act Between High Quality and Real-Time Constraints

Mixed Reality ◽  
1999 ◽  
pp. 325-346 ◽  
Author(s):  
Gudrun Klinker ◽  
Didier Strieker ◽  
Dirk Reiners
Keyword(s):  
Augmented Reality ◽  
Real Time ◽  
Time Constraints ◽  
High Quality

scholarly journals Genetic Programming for Evolving a Front of Interpretable Models for Data Visualization

2020 ◽  
Author(s):  
Andrew Lensen ◽  
Bing Xue ◽  
Mengjie Zhang
Keyword(s):  
Genetic Programming ◽  
Data Visualization ◽  
State Of The Art ◽  
Visual Quality ◽  
Model Complexity ◽  
High Quality ◽  
Depth Analysis ◽  
Multiobjective Approach ◽  
Interpretable Models ◽  
Insight Into

Data visualization is a key tool in data mining for understanding big datasets. Many visualization methods have been proposed, including the well-regarded state-of-the-art method t-distributed stochastic neighbor embedding. However, the most powerful visualization methods have a significant limitation: the manner in which they create their visualization from the original features of the dataset is completely opaque. Many domains require an understanding of the data in terms of the original features; there is hence a need for powerful visualization methods which use understandable models. In this article, we propose a genetic programming (GP) approach called GP-tSNE for evolving interpretable mappings from the dataset to high-quality visualizations. A multiobjective approach is designed that produces a variety of visualizations in a single run which gives different tradeoffs between visual quality and model complexity. Testing against baseline methods on a variety of datasets shows the clear potential of GP-tSNE to allow deeper insight into data than that provided by existing visualization methods. We further highlight the benefits of a multiobjective approach through an in-depth analysis of a candidate front, which shows how multiple models can be analyzed jointly to give increased insight into the dataset.

scholarly journals Genetic Programming for Evolving a Front of Interpretable Models for Data Visualization

2020 ◽  
Author(s):  
Andrew Lensen ◽  
Bing Xue ◽  
Mengjie Zhang
Keyword(s):  
Genetic Programming ◽  
Data Visualization ◽  
State Of The Art ◽  
Visual Quality ◽  
Model Complexity ◽  
High Quality ◽  
Depth Analysis ◽  
Multiobjective Approach ◽  
Interpretable Models ◽  
Insight Into

Data visualization is a key tool in data mining for understanding big datasets. Many visualization methods have been proposed, including the well-regarded state-of-the-art method t-distributed stochastic neighbor embedding. However, the most powerful visualization methods have a significant limitation: the manner in which they create their visualization from the original features of the dataset is completely opaque. Many domains require an understanding of the data in terms of the original features; there is hence a need for powerful visualization methods which use understandable models. In this article, we propose a genetic programming (GP) approach called GP-tSNE for evolving interpretable mappings from the dataset to high-quality visualizations. A multiobjective approach is designed that produces a variety of visualizations in a single run which gives different tradeoffs between visual quality and model complexity. Testing against baseline methods on a variety of datasets shows the clear potential of GP-tSNE to allow deeper insight into data than that provided by existing visualization methods. We further highlight the benefits of a multiobjective approach through an in-depth analysis of a candidate front, which shows how multiple models can be analyzed jointly to give increased insight into the dataset.

scholarly journals REAL-TIME BLOB-WISE SUGAR BEETS VS WEEDS CLASSIFICATION FOR MONITORING FIELDS USING CONVOLUTIONAL NEURAL NETWORKS

2017 ◽  
Vol IV-2/W3 ◽  
pp. 41-48 ◽  
Author(s):  
A. Milioto ◽  
P. Lottes ◽  
C. Stachniss
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Sugar Beet ◽  
Real Time ◽  
Convolutional Neural Networks ◽  
Image Data ◽  
High Quality ◽  
Sugar Beets ◽  
Quality Classification

UAVs are becoming an important tool for field monitoring and precision farming. A prerequisite for observing and analyzing fields is the ability to identify crops and weeds from image data. In this paper, we address the problem of detecting the sugar beet plants and weeds in the field based solely on image data. We propose a system that combines vegetation detection and deep learning to obtain a high-quality classification of the vegetation in the field into value crops and weeds. We implemented and thoroughly evaluated our system on image data collected from different sugar beet fields and illustrate that our approach allows for accurately identifying the weeds on the field.

scholarly journals Genetic Programming for Evolving a Front of Interpretable Models for Data Visualization

2020 ◽  
Author(s):  
Andrew Lensen ◽  
Bing Xue ◽  
Mengjie Zhang
Keyword(s):  
Genetic Programming ◽  
Data Visualization ◽  
State Of The Art ◽  
Visual Quality ◽  
Model Complexity ◽  
High Quality ◽  
Depth Analysis ◽  
Multiobjective Approach ◽  
Interpretable Models ◽  
Insight Into

Data visualization is a key tool in data mining for understanding big datasets. Many visualization methods have been proposed, including the well-regarded state-of-the-art method t-distributed stochastic neighbor embedding. However, the most powerful visualization methods have a significant limitation: the manner in which they create their visualization from the original features of the dataset is completely opaque. Many domains require an understanding of the data in terms of the original features; there is hence a need for powerful visualization methods which use understandable models. In this article, we propose a genetic programming (GP) approach called GP-tSNE for evolving interpretable mappings from the dataset to high-quality visualizations. A multiobjective approach is designed that produces a variety of visualizations in a single run which gives different tradeoffs between visual quality and model complexity. Testing against baseline methods on a variety of datasets shows the clear potential of GP-tSNE to allow deeper insight into data than that provided by existing visualization methods. We further highlight the benefits of a multiobjective approach through an in-depth analysis of a candidate front, which shows how multiple models can be analyzed jointly to give increased insight into the dataset.

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