scholarly journals Multi-Shape Free-Form Deformation Framework for Efficient Data Transmission in AR-Based Medical Training Simulators

2021 ◽  
Vol 11 (21) ◽  
pp. 9925
Author(s):  
Myeongjin Kim ◽  
Fernando Bello
Keyword(s):  
Real World ◽  
Medical Training ◽  
Free Form ◽  
Control Points ◽  
Surface Mesh ◽  
The Real ◽  
Free Form Deformation ◽  
Simulation Scenario ◽  
3D Scene ◽  
Global Deformation

Augmented reality medical training simulators can provide a realistic and immersive experience by overlapping the virtual scene on to the real world. Latency in augmented reality (AR) medical training simulators is an important issue as it can lead to motion sickness for users. This paper proposes a framework that can achieve real-time rendering of the 3D scene aligned to the real world using a head-mounted display (HMD). Model deformation in the 3D scene is categorised into local deformation derived from user interaction and global deformation determined by the simulation scenario. Target shapes are predefined by a simulation scenario, and control points are placed to embed the predefined shapes. Free-form deformation (FFD) is applied to multiple shapes to efficiently transfer the simulated model to the HMD. Global deformation is computed by blending a mapping matrix of each FFD with an assigned weighting value. The local and global deformation are then transferred through the control points updated from a deformed surface mesh and its corresponding weighting value. The proposed framework is verified in terms of latency caused by data transmission and the accuracy of a transmitted surface mesh in a vaginal examination (VE) training simulation. The average latency is reduced to 7 ms, less than the latency causing motion sickness in virtual reality simulations. The maximum relative error is less than 3%. Our framework allows seamless rendering of a virtual scene to the real world with substantially reduced latency and without the need for an external tracking system.

A Method of Interactive Surface Free-Form Deformation Based on Control Point and Neighborhood Constraints

2011 ◽  
Vol 338 ◽  
pp. 277-281
Author(s):  
Chao Hua Peng ◽  
Fei Liu ◽  
Li Li
Keyword(s):  
Control Point ◽  
Three Dimensional ◽  
Free Form ◽  
Control Points ◽  
Deformation Method ◽  
Face Modeling ◽  
Free Form Deformation ◽  
Local Modification ◽  
Interactive Surface ◽  
Three Dimensional Models

In view of the problem that it’s hard to determine the control points and morphing is not intuitionistic in traditional surface free-form deformation technology, an interactive surface free-form deformation method is proposed in this paper. Using this method, the user no longer needs to dynamically set constraint points outside the model. The point picked up by the user according to the desired deformation is used as a control point, and the neighborhood range of deformation or the deformation shape is controlled by deformation function. The designer can interactively deform the model simply by setting control parameters. The experiment results by applying the method to face modeling show that the proposed method is feasible and effective, providing a convenient way for the local modification of three-dimensional models.

scholarly journals Structured priors in human forecasting

2018 ◽  
Author(s):  
Francisco Quiroga ◽  
Eric Schulz ◽  
Maarten Speekenbrink ◽  
Nigel Harvey
Keyword(s):  
Real World ◽  
Systematic Errors ◽  
Free Form ◽  
Real World Data ◽  
Data Set ◽  
Daily Lives ◽  
Situational Context ◽  
The Real ◽  
Systematic Biases ◽  
How People Learn

AbstractForecasting is an increasingly important part of our daily lives. Many studies on how people produce forecasts frame their behavior as prone to systematic errors. Based on recent evidence on how people learn about functions, we propose that participants’ forecasts are not irrational but rather driven by structured priors, i.e. situationally induced expectations of structure derived from experience with the real world. To test this, we extract participants’ priors over various contexts using a free-form forecasting paradigm. Instead of exhibiting systematic biases, our results show that participants’ priors match well with structure found in real-world data. Moreover, given the same data set, structured priors induce predictably different posterior forecasts depending on the evoked situational context.

scholarly journals Structured priors in human forecasting

2018 ◽  
Author(s):  
Francisco Quiroga ◽  
Eric Schulz ◽  
Maarten Speekenbrink ◽  
Nigel Harvey
Keyword(s):  
Real World ◽  
Systematic Errors ◽  
Free Form ◽  
Real World Data ◽  
Data Set ◽  
Daily Lives ◽  
Situational Context ◽  
The Real ◽  
Systematic Biases ◽  
How People Learn

Forecasting is an increasingly important part of our daily lives. Many studies on how people produce forecasts frame their behavior as prone to systematic errors. Based on recent evidence on how people learn about functions, we propose that participants' forecasts are not irrational but rather driven by structured priors, i.e. situationally induced expectations of structure derived from experience with the real world. To test this, we extract participants' priors over various contexts using a free-form forecasting paradigm. Instead of exhibiting systematic biases, our results show that participants' priors match well with structure found in real-world data. Moreover, given the same data set, structured priors induce predictably different posterior forecasts depending on the evoked situational context.

Haptic Interaction with Elastic Volumetric Structures

2012 ◽  
Vol 3 (1) ◽  
pp. 63-73 ◽  
Author(s):  
Francisco J. R. Prados ◽  
Alejandro León Salas ◽  
Juan Carlos Torres
Keyword(s):  
Haptic Feedback ◽  
Medical Training ◽  
Free Form ◽  
Ct Scanner ◽  
3D Images ◽  
Simulation Scenario ◽  
Deformable Bodies ◽  
Open Issue ◽  
Visualization Algorithms ◽  
Deform 3D

Considerable efforts have been done to produce realistic results when simulating interaction with elastic materials. Many applications such as surgery planning, medical training, or virtual sculpting would benefit from a plausible simulation scenario. However, even though many works have proposed very satisfactory results, realistic simulation of deformable bodies is still an open issue. One of the challenges when designing a realistic elastic body simulation is the huge amount of data that needs to be processed. For the inner properties of the material are crucial when it comes to reproduce the elastic problem, the simulation naturally calls for volumetric information. In this paper the authors propose a technique to interactively deform 3D images, such as those acquired by a CT scanner. While producing a physically plausible haptic feedback, deformation and visualization algorithms produce an efficient and natural feeling. Using a free form deformation structure as a wrapper, it is possible to deform complex structures at high frame rates, independently of the size of the volume.

scholarly journals A COMPARATIVE ANALYSIS OF DIFFERENT DEM INTERPOLATION METHODS IN GIS: CASE STUDY OF RAHOVEC, KOSOVO

2019 ◽  
Vol 45 (5) ◽  
pp. 43-48 ◽  
Author(s):  
Besim Ajvazi ◽  
Kornél Czimber
Keyword(s):  
Comparative Analysis ◽  
Real World ◽  
Spatial Interpolation ◽  
Kriging Interpolation ◽  
Surface Model ◽  
Control Points ◽  
Data Set ◽  
The Real ◽  
Interpolation Methods ◽  
Spatial Interpolation Methods

Geographic Information System (GIS) uses geospatial databases as a model of the real world. Since we are speaking of the real world this entails that in many cases the information about the Earth’s surface is highly important. Therefore, the generation of a surface model is significant. Basically, the quality of the Digital Elevation Model (DEM) depends on the source data or techniques used to obtain them. However, different spatial interpolation methods used for the same data may provide different results. This paper compares the accuracy of different spatial interpolation methods such as IDW, Kriging, Natural Neighbor and Spline. Since interpolation is essential in DEM generation, then is important to do a comparative analysis of such methods to find out which one provides more accurate results. The DEM data set used is from an aero photogrammetric surveying. According to this data set, three scenarios are performed for each of the methods. Selected random control points are derived from the base data set. The first example includes 10% of randomly selected control points, the second example includes 20%, and the third example includes 30%. The Mean Absolute Error (MAE) and the Root Mean Square Error (RMSE) are calculated. We find out that results do not have much difference; however, the most accurate results are derived from the Spline and Kriging interpolation methods.

Current Issues: Evidence-Based Practice for the Real World: Thoughts on Clinical Education in Communication Disorders

2010 ◽  
Vol 20 (3) ◽  
pp. 100-105 ◽  
Author(s):  
Anne K. Bothe
Keyword(s):  
Clinical Education ◽  
Real World ◽  
Evidence Based Practice ◽  
Communication Disorders ◽  
University Faculty ◽  
Evidence Based ◽  
Speech Language Pathology ◽  
Basic Principles ◽  
The Real ◽  
Language Pathology

This article presents some streamlined and intentionally oversimplified ideas about educating future communication disorders professionals to use some of the most basic principles of evidence-based practice. Working from a popular five-step approach, modifications are suggested that may make the ideas more accessible, and therefore more useful, for university faculty, other supervisors, and future professionals in speech-language pathology, audiology, and related fields.

The Real-World Fellowship

2006 ◽  
Vol 40 (7) ◽  
pp. 47
Author(s):  
LEE SAVIO BEERS
Keyword(s):  
Real World ◽  
The Real
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