A high-resolution detecting system based on machine vision for defects on large aperture and super-smooth surface

2015 ◽  
Author(s):  
Yongying Yang ◽  
Limin Zhao ◽  
Shitong Wang ◽  
Pin Cao ◽  
Dong Liu ◽  
...  
Keyword(s):  
High Resolution ◽  
Machine Vision ◽  
Smooth Surface ◽  
Large Aperture

A conceptual design for a Cassegrain-mounted high-resolution optical spectrograph for large-aperture telescopes

2013 ◽  
Author(s):  
Cynthia S. Froning ◽  
Steven Osterman ◽  
Eric Burgh ◽  
Matthew Beasley ◽  
Paul Scowen ◽  
...  
Keyword(s):  
High Resolution ◽  
Conceptual Design ◽  
Large Aperture

Ultra-precision detection of surface defects of large aperture diffraction grating based on machine vision

2021 ◽  
Author(s):  
Haotian Wang ◽  
Chaoming Li ◽  
Xinrong Chen ◽  
Zhe Huang ◽  
Jiayao Pan ◽  
...  
Keyword(s):  
Machine Vision ◽  
Diffraction Grating ◽  
Surface Defects ◽  
Large Aperture ◽  
Ultra Precision ◽  
Aperture Diffraction

High-speed high-resolution color line scan camera for machine vision

1993 ◽  
Author(s):  
David J. Litwiller ◽  
Mike Miethig ◽  
Brian C. Doody ◽  
P. Tom Jenkins
Keyword(s):  
High Resolution ◽  
Machine Vision ◽  
High Speed ◽  
Color Line ◽  
Line Scan

High-speed high-resolution color line scan camera for machine vision

1994 ◽  
Author(s):  
David J. Litwiller ◽  
Mike Miethig ◽  
Brian C. Doody ◽  
P. Tom Jenkins
Keyword(s):  
High Resolution ◽  
Machine Vision ◽  
High Speed ◽  
Color Line ◽  
Line Scan

Integrated, dynamic machine vision system with virtual high resolution

1993 ◽  
Author(s):  
Peter Seitz ◽  
Martin Stalder ◽  
Jeffrey M. Raynor ◽  
Graham K. Lang ◽  
Cor L. Claeys ◽  
...  
Keyword(s):  
High Resolution ◽  
Machine Vision ◽  
Vision System ◽  
Machine Vision System

scholarly journals Optical Detection of Green Emission for Non-Uniformity Film in Flat Panel Displays

Crystals ◽  
2018 ◽  
Vol 8 (11) ◽  
pp. 421 ◽  
Author(s):  
Fu-Ming Tzu ◽  
Jung-Hua Chou
Keyword(s):  
High Resolution ◽  
Machine Vision ◽  
Quantitative Assessment ◽  
Green Emission ◽  
Human Vision ◽  
Just Noticeable Difference ◽  
Flat Panel Displays ◽  
Colour Difference ◽  
Flat Panel ◽  
Green Colour

Among colours, the green colour has the most sensitivity in human vision so that green colour defects on displays can be effortlessly perceived by a photopic eye with the most intensity in the wavelength 555 nm of the spectrum. With the market moving forward to high resolution, displays can have resolutions of 10 million pixels. Therefore, the method of detecting the appearance of the panel using ultra-high resolutions in TFT-LCD is important. The machine vision associated with transmission chromaticity spectrometer that quantises the defects are explored, such as blackening and whitening. The result shows the significant phenomena to recognize the non-uniformity of film-related chromatic variation. In contrast, the quantitative assessment illustrates that the just noticeable difference (JND) of chromaticity CIE xyY at 0.001 is the measuring sensitivity for the chromatic variables (x, y), whereas JND is a perceptible threshold for a colour difference metric. Moreover, an optical device associated with a 198Hg discharge lamp calibrates the spectrometer accuracy.

scholarly journals Automated crop plant counting from very high-resolution aerial imagery

2020 ◽  
Vol 21 (6) ◽  
pp. 1366-1384 ◽  
Author(s):  
João Valente ◽  
Bilal Sari ◽  
Lammert Kooistra ◽  
Henk Kramer ◽  
Sander Mücher
Keyword(s):  
High Resolution ◽  
Machine Vision ◽  
Surface Area ◽  
Spatial Resolution ◽  
Image Data ◽  
Validation Data ◽  
Counting Error ◽  
Use Of Resources ◽  
Automated Method ◽  
Very High

Abstract Knowing before harvesting how many plants have emerged and how they are growing is key in optimizing labour and efficient use of resources. Unmanned aerial vehicles (UAV) are a useful tool for fast and cost efficient data acquisition. However, imagery need to be converted into operational spatial products that can be further used by crop producers to have insight in the spatial distribution of the number of plants in the field. In this research, an automated method for counting plants from very high-resolution UAV imagery is addressed. The proposed method uses machine vision—Excess Green Index and Otsu’s method—and transfer learning using convolutional neural networks to identify and count plants. The integrated methods have been implemented to count 10 weeks old spinach plants in an experimental field with a surface area of 3.2 ha. Validation data of plant counts were available for 1/8 of the surface area. The results showed that the proposed methodology can count plants with an accuracy of 95% for a spatial resolution of 8 mm/pixel in an area up to 172 m2. Moreover, when the spatial resolution decreases with 50%, the maximum additional counting error achieved is 0.7%. Finally, a total amount of 170 000 plants in an area of 3.5 ha with an error of 42.5% was computed. The study shows that it is feasible to count individual plants using UAV-based off-the-shelf products and that via machine vision/learning algorithms it is possible to translate image data in non-expert practical information.

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