scholarly journals Implementation of a Thermal Image Processing System to Detect Possible Cases of Patients with COVID-19

2021 ◽  
Vol 11 (11) ◽  
pp. 130-139
Author(s):  
Brian Meneses Claudio ◽  
◽  
Luis Nuñez Tapia ◽  
Witman Alvarado Díaz ◽  
Alicia Alva Mantari
Keyword(s):  
Image Processing ◽  
Body Temperature ◽  
Sensitivity And Specificity ◽  
Processing System ◽  
Thermal Image ◽  
Free Movement ◽  
Infrared Thermometer ◽  
Image Processing System ◽  
Thermal Image Processing ◽  
The World

COVID-19 does not show signs of having disappeared, being a very contagious disease, the WHO recommended limiting the free movement of people, since from its appearance until May 1st, 2021, it caused the death of more than 3.2 million of people around the world. In Peru, it economically affected those people who generated income every day to survive, for this reason some activities were reactivated complying with the biosafety measures that are the use of mandatory mask and social distancing (more than 1 meter). Taking body temperature with an infrared thermometer is an optional measure, generating rejection by specialists, indicating that there is little evidence of its sensitivity and specificity and of its doubtful ability to detect fever. In view of this problem, this article will implement a thermal image processing system to detect possible cases of patients with COVID-19, in such a way that the system performs a more accurate measurement of body temperature, and it can be implemented in any place where such measurement is intended, helping to combat the spread of the virus that currently continues to affect many people. The system has a more accurate measurement of body temperature with an efficiency of 95% at 1 meter between the drone and the person, in such a way that if it presents a body temperature higher than 40°C it could be infected with COVID-19

scholarly journals Dynamic Analysis of Water Stress of Sunflower Leaves by Means of a Thermal Image Processing System

1984 ◽  
Vol 76 (1) ◽  
pp. 266-269 ◽  
Author(s):  
Yasushi Hashimoto ◽  
Taketoshi Ino ◽  
Paul J. Kramer ◽  
Aubrey W. Naylor ◽  
Boyd R. Strain
Keyword(s):  
Image Processing ◽  
Water Stress ◽  
Dynamic Analysis ◽  
Processing System ◽  
Thermal Image ◽  
Image Processing System ◽  
Thermal Image Processing

Software pattern recognition applied to nanodiffraction patterns from a STEM instrument

1986 ◽  
Vol 44 ◽  
pp. 694-695
Author(s):  
G.Y. Fan ◽  
J.M. Cowley
Keyword(s):  
Image Processing ◽  
Pattern Recognition ◽  
Computer Software ◽  
Processing System ◽  
Light Level ◽  
Host Computer ◽  
Low Light ◽  
Image Processing System ◽  
Recent Developments ◽  
On Line

In recent developments, the ASU HB5 has been modified so that the timing, positioning, and scanning of the finely focused electron probe can be entirely controlled by a host computer. This made the asynchronized handshake possible between the HB5 STEM and the image processing system which consists of host computer (PDP 11/34), DeAnza image processor (IP 5000) which is interfaced with a low-light level TV camera, array processor (AP 400) and various peripheral devices. This greatly facilitates the pattern recognition technique initiated by Monosmith and Cowley. Software called NANHB5 is under development which, instead of employing a set of photo-diodes to detect strong spots on a TV screen, uses various software techniques including on-line fast Fourier transform (FFT) to recognize patterns of greater complexity, taking advantage of the sophistication of our image processing system and the flexibility of computer software.

Massive Image Treatment System Based on Cloud Computing Platform

2014 ◽  
Vol 687-691 ◽  
pp. 3733-3737
Author(s):  
Dan Wu ◽  
Ming Quan Zhou ◽  
Rong Fang Bie
Keyword(s):  
Image Processing ◽  
Cloud Computing ◽  
High Performance ◽  
Large Scale ◽  
Processing System ◽  
Virtual Space ◽  
Image Processing System ◽  
Computing Platform ◽  
Simulation Calculation ◽  
Computer Resources

Massive image processing technology requires high requirements of processor and memory, and it needs to adopt high performance of processor and the large capacity memory. While the single or single core processing and traditional memory can’t satisfy the need of image processing. This paper introduces the cloud computing function into the massive image processing system. Through the cloud computing function it expands the virtual space of the system, saves computer resources and improves the efficiency of image processing. The system processor uses multi-core DSP parallel processor, and develops visualization parameter setting window and output results using VC software settings. Through simulation calculation we get the image processing speed curve and the system image adaptive curve. It provides the technical reference for the design of large-scale image processing system.

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