Automatic Camera Control for Tracking a Presenter during a Talk

2012 ◽  
Author(s):  
Michael Bjorn Winkler ◽  
Kai Michael Hover ◽  
Aristotelis Hadjakos ◽  
Max Muhlhauser
Keyword(s):  
Camera Control

Video-Enhanced Microscopy--Better Visibility of Plant Cell Structures

1982 ◽  
Vol 40 ◽  
pp. 182-185
Author(s):  
N.S. Allen ◽  
R.D. Allen
Keyword(s):  
Diffraction Pattern ◽  
Theoretical Basis ◽  
Numerical Aperture ◽  
Gain Control ◽  
Control Unit ◽  
Camera Control ◽  
Variable Gain ◽  
Contrast Method ◽  
Fine Detail ◽  
Cell Structures

Various methods of video-enhanced microscopy combine TV cameras with light microscopes creating images with improved resolution, contrast and visibility of fine detail, which can be recorded rapidly and relatively inexpensively. The AVEC (Allen Video-enhanced Contrast) method avoids polarizing rectifiers, since the microscope is operated at retardations of λ/9- λ/4, where no anomaly is seen in the Airy diffraction pattern. The iris diaphram is opened fully to match the numerical aperture of the condenser to that of the objective. Under these conditions, no image can be realized either by eye or photographically. Yet the image becomes visible using the Hamamatsu C-1000-01 binary camera, if the camera control unit is equipped with variable gain control and an offset knob (which sets a clamp voltage of a D.C. restoration circuit). The theoretical basis for these improvements has been described.

scholarly journals A learning robot for cognitive camera control in minimally invasive surgery

2021 ◽  
Author(s):  
Martin Wagner ◽  
Andreas Bihlmaier ◽  
Hannes Götz Kenngott ◽  
Patrick Mietkowski ◽  
Paul Maria Scheikl ◽  
...  
Keyword(s):  
Minimally Invasive Surgery ◽  
Minimally Invasive ◽  
Invasive Surgery ◽  
Autonomous Systems ◽  
Robot Kinematics ◽  
Learning Context ◽  
Surgical Robots ◽  
Camera Control ◽  
Context Sensitive ◽  
Robotic Camera

Abstract Background We demonstrate the first self-learning, context-sensitive, autonomous camera-guiding robot applicable to minimally invasive surgery. The majority of surgical robots nowadays are telemanipulators without autonomous capabilities. Autonomous systems have been developed for laparoscopic camera guidance, however following simple rules and not adapting their behavior to specific tasks, procedures, or surgeons. Methods The herein presented methodology allows different robot kinematics to perceive their environment, interpret it according to a knowledge base and perform context-aware actions. For training, twenty operations were conducted with human camera guidance by a single surgeon. Subsequently, we experimentally evaluated the cognitive robotic camera control. A VIKY EP system and a KUKA LWR 4 robot were trained on data from manual camera guidance after completion of the surgeon’s learning curve. Second, only data from VIKY EP were used to train the LWR and finally data from training with the LWR were used to re-train the LWR. Results The duration of each operation decreased with the robot’s increasing experience from 1704 s ± 244 s to 1406 s ± 112 s, and 1197 s. Camera guidance quality (good/neutral/poor) improved from 38.6/53.4/7.9 to 49.4/46.3/4.1% and 56.2/41.0/2.8%. Conclusions The cognitive camera robot improved its performance with experience, laying the foundation for a new generation of cognitive surgical robots that adapt to a surgeon’s needs.

scholarly journals Topology-aware Camera Control for Real-time Applications

2020 ◽  
Author(s):  
Alberto Jovane ◽  
Amaury Louarn ◽  
Marc Christie
Keyword(s):  
Real Time ◽  
Camera Control ◽  
Real Time Applications

scholarly journals Reducing simulator sickness with perceptual camera control

2019 ◽  
Vol 38 (6) ◽  
pp. 1-12 ◽  
Author(s):  
Ping Hu ◽  
Qi Sun ◽  
Piotr Didyk ◽  
Li-Yi Wei ◽  
Arie E. Kaufman
Keyword(s):  
Simulator Sickness ◽  
Camera Control

scholarly journals Object Recognition Based on Camera Control

1998 ◽  
Vol 118 (2) ◽  
pp. 210-216
Author(s):  
Noboru Nishikawa ◽  
Masaki Onishi ◽  
Takuya Matsumoto ◽  
Masao Izumi ◽  
Kunio Fukunaga
Keyword(s):  
Object Recognition ◽  
Camera Control

scholarly journals Collecting large datasets of rotational electron diffraction with ParallEM and SerialEM

2020 ◽  
Author(s):  
Kiyofumi Takaba ◽  
Saori Maki-Yonekura ◽  
Koji Yonekura
Keyword(s):  
Electron Diffraction ◽  
Data Collection ◽  
Large Scale ◽  
General Purpose ◽  
Task Specialization ◽  
Camera Control ◽  
Combined Use ◽  
Electron Microscopes ◽  
Diffraction Patterns ◽  
Automated Protocol

AbstractA semi-automated protocol has been developed for rotational data collection of electron diffraction patterns by combined use of SerialEM and ParallEM, where SerialEM is used for positioning of sample crystals and ParallEM for rotational data collection. ParallEM calls standard camera control software through an AutoIt script, which adapts to software operational changes and to new GUI programs guiding other cameras. Development included periodic flashing and pausing of data collection during overnight or day-long recording with a cold field-emission beam. The protocol proved to be efficient and accurate in data collection of large-scale rotational series from two JEOL electron microscopes, a general-purpose JEM-2100 and a high-end CRYO ARM 300. Efficiency resulted from simpler steps and task specialization. It is possible to collect 12–20 rotational series from ∼ −68º to ∼ 68º at a rotation speed of 1º /s in one hour without human supervision.

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