scholarly journals Evaluation of Advanced Bimanual Skills in Novices Using the Wrist-Like FlexDex™ Articulating Laparoscopic Needle Holder in 2D and 3D Vision: A Randomised Trial

2021 ◽  
Vol Volume 14 ◽  
pp. 469-480
Author(s):  
Nima Motahariasl ◽  
Sayed Borna Farzaneh ◽  
Sina Motahariasl ◽  
Ilya Kokotkin ◽  
Sara Sousi ◽  
...  
Keyword(s):  
Randomised Trial ◽  
Needle Holder ◽  
3D Vision ◽  
2D And 3D

scholarly journals Comparison of 2D and 3D Vision Gaze with Simultaneous Measurements of Accommodation and Convergence

Forma ◽  
2014 ◽  
Author(s):  
Hiroki Hori ◽  
Tomoki Shiomi ◽  
Satoshi Hasegawa ◽  
Hiroki Takada ◽  
Masako Omori ◽  
...  
Keyword(s):  
3D Vision ◽  
Simultaneous Measurements ◽  
2D And 3D

scholarly journals Detection of 2D and 3D Video Transitions Based on EEG Power

2020 ◽  
Author(s):  
Negin Manshouri ◽  
Mesut Melek ◽  
Temel Kayikcioglu
Keyword(s):  
Steady State ◽  
Support Vector ◽  
Success Rates ◽  
K Nearest Neighbors ◽  
3D Vision ◽  
Linear Discriminant ◽  
Parietal Lobes ◽  
Brain Signals ◽  
Sudden Transition ◽  
2D And 3D

Despite the long and extensive history of 3D technology, it has recently attracted the attention of researchers. This technology has become the center of interest of young people because of the real feelings and sensations it creates. People see their environment as 3D because of their eye structure. In this study, it is hypothesized that people lose their perception of depth during sleepy moments and that there is a sudden transition from 3D vision to 2D vision. Regarding these transitions, the EEG signal analysis method was used for deep and comprehensive analysis of 2D and 3D brain signals. In this study, a single-stream anaglyph video of random 2D and 3D segments was prepared. After watching this single video, the obtained EEG recordings were considered for two different analyses: the part involving the critical transition (transition-state) and the state analysis of only the 2D versus 3D or 3D versus 2D parts (steady-state). The main objective of this study is to see the behavioral changes of brain signals in 2D and 3D transitions. To clarify the impacts of the human brain’s power spectral density (PSD) in 2D-to-3D (2D_3D) and 3D-to-2D (3D_2D) transitions of anaglyph video, 9 visual healthy individuals were prepared for testing in this pioneering study. Spectrogram graphs based on Short Time Fourier transform (STFT) were considered to evaluate the power spectrum analysis in each EEG channel of transition or steady-state. Thus, in 2D and 3D transition scenarios, important channels representing EEG frequency bands and brain lobes will be identified. To classify the 2D and 3D transitions, the dominant bands and time intervals representing the maximum difference of PSD were selected. Afterward, effective features were selected by applying statistical methods such as standard deviation (SD), maximum (max), and Hjorth parameters to epochs indicating transition intervals. Ultimately, k-Nearest Neighbors (k-NN), Support Vector Machine (SVM), and Linear Discriminant Analysis (LDA) algorithms were applied to classify 2D_3D and 3D_2D transitions. The frontal, temporal, and partially parietal lobes show 2D_3D and 3D_2D transitions with a good classification success rate. Overall, it was found that Hjorth parameters and LDA algorithms have 71.11% and 77.78% classification success rates for transition and steady-state, respectively.

Detection of 2D and 3D Video Transitions Based on EEG Power

2020 ◽  
Author(s):  
Negin Manshouri ◽  
Mesut Melek ◽  
Temel Kayıkcıoglu
Keyword(s):  
Steady State ◽  
Support Vector ◽  
Success Rates ◽  
K Nearest Neighbors ◽  
3D Vision ◽  
Linear Discriminant ◽  
Parietal Lobes ◽  
Brain Signals ◽  
Sudden Transition ◽  
2D And 3D

Abstract Despite the long and extensive history of 3D technology, it has recently attracted the attention of researchers. This technology has become the center of interest of young people because of the real feelings and sensations it creates. People see their environment as 3D because of their eye structure. In this study, it is hypothesized that people lose their perception of depth during sleepy moments and that there is a sudden transition from 3D vision to 2D vision. Regarding these transitions, the EEG signal analysis method was used for deep and comprehensive analysis of 2D and 3D brain signals. In this study, a single-stream anaglyph video of random 2D and 3D segments was prepared. After watching this single video, the obtained EEG recordings were considered for two different analyses: the part involving the critical transition (transition state) and the state analysis of only the 2D versus 3D or 3D versus 2D parts (steady state). The main objective of this study is to see the behavioral changes of brain signals in 2D and 3D transitions. To clarify the impacts of the human brain’s power spectral density (PSD) in 2D-to-3D (2D_3D) and 3D-to-2D (3D_2D) transitions of anaglyph video, nine visual healthy individuals were prepared for testing in this pioneering study. Spectrogram graphs based on short time Fourier transform (STFT) were considered to evaluate the power spectrum analysis in each EEG channel of transition or steady state. Thus, in 2D and 3D transition scenarios, important channels representing EEG frequency bands and brain lobes will be identified. To classify the 2D and 3D transitions, the dominant bands and time intervals representing the maximum difference of PSD were selected. Afterward, effective features were selected by applying statistical methods such as standard deviation, maximum (max) and Hjorth parameters to epochs indicating transition intervals. Ultimately, k-nearest neighbors, support vector machine and linear discriminant analysis (LDA) algorithms were applied to classify 2D_3D and 3D_2D transitions. The frontal, temporal and partially parietal lobes show 2D_3D and 3D_2D transitions with a good classification success rate. Overall, it was found that Hjorth parameters and LDA algorithms have 71.11% and 77.78% classification success rates for transition and steady state, respectively.

scholarly journals Impact of 2D and 3D Vision on Performance of Novice Subjects Using da Vinci Robotic System

2006 ◽  
Vol 106 (6) ◽  
pp. 662-664 ◽  
Author(s):  
A. Blavier ◽  
Q. Gaudissart ◽  
G.-B. Cadiere ◽  
A.-S. Nyssen
Keyword(s):  
Da Vinci ◽  
Robotic System ◽  
3D Vision ◽  
Da Vinci Robotic System ◽  
2D And 3D

AN INTEGRATED VISION SYSTEM FOR ALV NAVIGATION

2000 ◽  
Vol 14 (07) ◽  
pp. 929-940 ◽  
Author(s):  
XIUQING YE ◽  
JILIN LIU ◽  
WEIKANG GU
Keyword(s):  
Information Fusion ◽  
Vision System ◽  
Feasible Region ◽  
Vehicle Navigation ◽  
3D Vision ◽  
Land Vehicle ◽  
3D Information ◽  
2D And 3D

In this paper an integrated vision system for autonomous land vehicle is described. The vision system includes 2D and 3D vision modules and information fusion module. The task of 2D vision is to provide the physical and geometry information of road, and the task of 3D vision system is to detect the obstacles in the surrounding. Fusion module combines 2D and 3D information to generate a feasible region provided for vehicle navigation.

Comparison of Accommodation and Convergence by Simultaneous Measurements during 2D and 3D Vision Gaze

2011 ◽  
pp. 306-314 ◽  
Author(s):  
Hiroki Hori ◽  
Tomoki Shiomi ◽  
Tetsuya Kanda ◽  
Akira Hasegawa ◽  
Hiromu Ishio ◽  
...  
Keyword(s):  
3D Vision ◽  
Simultaneous Measurements ◽  
2D And 3D

scholarly journals A randomised trial of observational learning from 2D and 3D models in robotically assisted surgery

2018 ◽  
Vol 32 (11) ◽  
pp. 4527-4532 ◽  
Author(s):  
David J. Harris ◽  
Samuel J. Vine ◽  
Mark R. Wilson ◽  
John S. McGrath ◽  
Marie-Eve LeBel ◽  
...  
Keyword(s):  
Observational Learning ◽  
Randomised Trial ◽  
3D Models ◽  
2D And 3D
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