Machine Vision and Automation in Secondary Metabolite Bioprocess Control

Author(s):  
Mary Ann Lila Smith ◽  
J. F. Reid
Author(s):  
Wesley E. Snyder ◽  
Hairong Qi
Keyword(s):  

Planta Medica ◽  
2008 ◽  
Vol 74 (09) ◽  
Author(s):  
SA Van der Sar ◽  
KM Fisch ◽  
C Gurgui ◽  
TA Nguyen ◽  
J Piel ◽  
...  

2018 ◽  
pp. 143-149 ◽  
Author(s):  
Ruijie CHENG

In order to further improve the energy efficiency of classroom lighting, a classroom lighting energy saving control system based on machine vision technology is proposed. Firstly, according to the characteristics of machine vision design technology, a quantum image storage model algorithm is proposed, and the Back Propagation neural network algorithm is used to analyze the technology, and a multi­feedback model for energy­saving control of classroom lighting is constructed. Finally, the algorithm and lighting model are simulated. The test results show that the design of this paper can achieve the optimization of the classroom lighting control system, different number of signals can comprehensively control the light and dark degree of the classroom lights, reduce the waste of resources of classroom lighting, and achieve the purpose of energy saving and emission reduction. Technology is worth further popularizing in practice.


1997 ◽  
Vol 117 (10) ◽  
pp. 1339-1344
Author(s):  
Katsuhiko Sakaue ◽  
Hiroyasu Koshimizu
Keyword(s):  

2005 ◽  
Vol 125 (11) ◽  
pp. 692-695
Author(s):  
Kazunori UMEDA ◽  
Yoshimitsu AOKI
Keyword(s):  

Fast track article for IS&T International Symposium on Electronic Imaging 2020: Stereoscopic Displays and Applications proceedings.


2020 ◽  
Vol 64 (5) ◽  
pp. 50411-1-50411-8
Author(s):  
Hoda Aghaei ◽  
Brian Funt

Abstract For research in the field of illumination estimation and color constancy, there is a need for ground-truth measurement of the illumination color at many locations within multi-illuminant scenes. A practical approach to obtaining such ground-truth illumination data is presented here. The proposed method involves using a drone to carry a gray ball of known percent surface spectral reflectance throughout a scene while photographing it frequently during the flight using a calibrated camera. The captured images are then post-processed. In the post-processing step, machine vision techniques are used to detect the gray ball within each frame. The camera RGB of light reflected from the gray ball provides a measure of the illumination color at that location. In total, the dataset contains 30 scenes with 100 illumination measurements on average per scene. The dataset is available for download free of charge.


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