Measuring Tremor—A Comparison of Automated Video Analysis, Neurophysiology, and Clinical Rating

2021 ◽  
Author(s):  
Alessandro F. Fois ◽  
Neil Mahant ◽  
Steve Vucic ◽  
Victor S.C. Fung
Keyword(s):  
Video Analysis ◽  
Clinical Rating ◽  
Automated Video Analysis

scholarly journals Use of automated video analysis for the evaluation of bicycle movement and interaction

2014 ◽  
Author(s):  
Heather Twaddle ◽  
Tobias Schendzielorz ◽  
Oliver Fakler ◽  
Sasan Amini
Keyword(s):  
Video Analysis ◽  
Automated Video Analysis

Automated video analysis of pig activity at pen level highly correlates to human observations of behavioural activities

2014 ◽  
Vol 160 ◽  
pp. 132-137 ◽  
Author(s):  
S. Ott ◽  
C.P.H. Moons ◽  
M.A. Kashiha ◽  
C. Bahr ◽  
F.A.M. Tuyttens ◽  
...  
Keyword(s):  
Video Analysis ◽  
Automated Video Analysis

Effects of acute methamphetamine administration on spacing in paired rats: Investigation with an automated video-analysis method

1996 ◽  
Vol 20 (6) ◽  
pp. 1037-1049 ◽  
Author(s):  
Toshikazu Shinba ◽  
Ken-Ichi Yamamoto ◽  
Gong-Min Cao ◽  
G.O. Mugishima ◽  
Yoshinori Andow ◽  
...  
Keyword(s):  
Video Analysis ◽  
Analysis Method ◽  
Automated Video Analysis

Using automated video analysis to study fish escapement through escape panels in active fishing gears: Application to the effect of net colour

Marine Policy ◽  
2020 ◽  
Vol 116 ◽  
pp. 103785
Author(s):  
Julien Simon ◽  
Dorothée Kopp ◽  
Pascal Larnaud ◽  
Jean-Philippe Vacherot ◽  
Fabien Morandeau ◽  
...  
Keyword(s):  
Video Analysis ◽  
Fishing Gears ◽  
Automated Video Analysis

scholarly journals Automated video analysis system reveals distinct diurnal behaviors in C57BL/6 and C3H/HeN mice

2013 ◽  
Vol 243 ◽  
pp. 306-312 ◽  
Author(s):  
E.B. Adamah-Biassi ◽  
I. Stepien ◽  
R.L. Hudson ◽  
M.L. Dubocovich
Keyword(s):  
Video Analysis ◽  
Analysis System ◽  
Automated Video Analysis

Pulsatile Non-Newtonian Flow in a Three-Stream Coaxial Airblast Injector

2012 ◽  
Author(s):  
Wayne Strasser
Keyword(s):  
Flow Rate ◽  
Video Analysis ◽  
Gas Flow ◽  
Frequency Domain Analysis ◽  
Computational Method ◽  
Nozzle Geometry ◽  
Mixed Effect ◽  
Driving Mechanisms ◽  
Analysis Technique ◽  
Automated Video Analysis

Previously published efforts regarding the unsteady nature of a self-exciting air/water (AW) coaxial three-stream airblast injector considered first the effects of feed stream flow rate changes and then the effects of nozzle geometric permutations. Frequency domain analysis, automated video analysis, and spray profile assessments were used to draw conclusions about spray quality and character. The computational method was validated using an AW test stand (AWTS). Here, the focus of the work shifts to the use of slurry and a high-density gas (SH). Again, the effects of flow rate and nozzle geometry are considered. It was found that the nature of the SH flow changed dramatically in comparison with its AW counterpart. As a result, the video analysis technique had to be revamped, and therefore direct comparisons are limited. As with its AW counterpart, inner nozzle retraction and stream meeting angle proved to be the most influential geometry variables. A flushed nozzle showed a wider spray with a strongly trimodal character. Increasing the relative inner gas flow rate had a pronounced, but mixed, effect on the considered metrics. In general, the transient signatures of the pressure and video analysis metrics were similar enough to indicate that the unsteady driving mechanisms were consistent for each. Lastly, attempts to further stimulate the spray via modulating the inner gas proved futile for both sets of flowing materials for the measures considered.

scholarly journals On Image Classification in Video Analysis of Omnidirectional Apis Mellifera Traffic: Random Reinforced Forests vs. Shallow Convolutional Networks

2021 ◽  
Vol 11 (17) ◽  
pp. 8141
Author(s):  
Vladimir Kulyukin ◽  
Nikhil Ganta ◽  
Anastasiia Tkachenko
Keyword(s):  
Image Classification ◽  
Video Analysis ◽  
Motion Detection ◽  
Health Assessment ◽  
Tier 2 ◽  
Convolutional Networks ◽  
Theoretical Comparison ◽  
Tier 1 ◽  
Close Proximity ◽  
Automated Video Analysis

Omnidirectional honeybee traffic is the number of bees moving in arbitrary directions in close proximity to the landing pad of a beehive over a period of time. Automated video analysis of such traffic is critical for continuous colony health assessment. In our previous research, we proposed a two-tier algorithm to measure omnidirectional bee traffic in videos. Our algorithm combines motion detection with image classification: in tier 1, motion detection functions as class-agnostic object location to generate regions with possible objects; in tier 2, each region from tier 1 is classified by a class-specific classifier. In this article, we present an empirical and theoretical comparison of random reinforced forests and shallow convolutional networks as tier 2 classifiers. A random reinforced forest is a random forest trained on a dataset with reinforcement learning. We present several methods of training random reinforced forests and compare their performance with shallow convolutional networks on seven image datasets. We develop a theoretical framework to assess the complexity of image classification by a image classifier. We formulate and prove three theorems on finding optimal random reinforced forests. Our conclusion is that, despite their limitations, random reinforced forests are a reasonable alternative to convolutional networks when memory footprints and classification and energy efficiencies are important factors. We outline several ways in which the performance of random reinforced forests may be improved.

Sign in / Sign up

Export Citation Format

Share Document