Measurement in Machine Vision Editorial Paper
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In: Measurement: Journal of the International Measurement Confederation, Vol. 236, 15.08.2024.
Research output: Journal contributions › Other (editorial matter etc.) › Research
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TY - JOUR
T1 - Measurement in Machine Vision Editorial Paper
AU - Sergiyenko, Oleg
AU - Flores-Fuentes, Wendy
AU - Rodríguez-Quiñonez, Julio C.
AU - Mercorelli, Paolo
AU - Kawabe, Tohru
AU - Bhateja, Vikrant
N1 - Publisher Copyright: © 2023
PY - 2024/8/15
Y1 - 2024/8/15
N2 - Measurement related to different machine vision functions is the base for developing of cyber-physical systems able to see and make decisions. These kinds of systems are emerging in all areas of our daily lives. They can be found in the medical area, in industry, in the agriculture, in all those interconnected cloud computing-based systems related to flying/terrestrial robotics, navigation, automated surgery, smart cities, smart health monitoring, etc. All of them are extremely dependent on the same: adequate coordinates measurement, properly selected data processing and data fusion algorithms, evaluation procedures for performance analysis of measurement within Machine Vision systems, processes and algorithms (both traditional and artificial intelligence), mathematical models for 3D-measurement purposes (measurement of displacements, surface profiles, deformations, data augmentation/interpolation, etc.), and distributed visual measurement systems, as well as distributed memory and sensory part. Cyber-physical systems can be implemented on almost any application, especially on those dotted by robots and automated guided devices (from aerospace applications to domestic cleaners). The success of the measurement process depends on the kind of sensors and their optoelectronics characteristics and intrinsic parameters, as well as their respective operating and processing. The correct approach selection for the application, the data acquisition and collection efficiency, the data processing algorithms, the hardware processors response time, and the intelligent auto adaptability to changing environments or conditions. Recently, the emergence of artificial intelligence algorithms and the internet of things have powerful development of such systems, highlighting the importance and the impact of the measurement accuracy related to machine vision performance.
AB - Measurement related to different machine vision functions is the base for developing of cyber-physical systems able to see and make decisions. These kinds of systems are emerging in all areas of our daily lives. They can be found in the medical area, in industry, in the agriculture, in all those interconnected cloud computing-based systems related to flying/terrestrial robotics, navigation, automated surgery, smart cities, smart health monitoring, etc. All of them are extremely dependent on the same: adequate coordinates measurement, properly selected data processing and data fusion algorithms, evaluation procedures for performance analysis of measurement within Machine Vision systems, processes and algorithms (both traditional and artificial intelligence), mathematical models for 3D-measurement purposes (measurement of displacements, surface profiles, deformations, data augmentation/interpolation, etc.), and distributed visual measurement systems, as well as distributed memory and sensory part. Cyber-physical systems can be implemented on almost any application, especially on those dotted by robots and automated guided devices (from aerospace applications to domestic cleaners). The success of the measurement process depends on the kind of sensors and their optoelectronics characteristics and intrinsic parameters, as well as their respective operating and processing. The correct approach selection for the application, the data acquisition and collection efficiency, the data processing algorithms, the hardware processors response time, and the intelligent auto adaptability to changing environments or conditions. Recently, the emergence of artificial intelligence algorithms and the internet of things have powerful development of such systems, highlighting the importance and the impact of the measurement accuracy related to machine vision performance.
KW - 3D Spatial Coordinates
KW - Artificial Intelligence
KW - Automatic Measurements
KW - Machine Vision
KW - Measurement
KW - Optical Computing
KW - Remote Sensing
KW - Technical Vision Systems
KW - Engineering
UR - http://www.scopus.com/inward/record.url?scp=85197097473&partnerID=8YFLogxK
UR - https://www.mendeley.com/catalogue/c6cfd7f6-4280-357f-9922-3ea82afb268e/
U2 - 10.1016/j.measurement.2023.114062
DO - 10.1016/j.measurement.2023.114062
M3 - Other (editorial matter etc.)
AN - SCOPUS:85197097473
VL - 236
JO - Measurement: Journal of the International Measurement Confederation
JF - Measurement: Journal of the International Measurement Confederation
SN - 0263-2241
ER -