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Wyszukujesz frazę "vision measurement" wg kryterium: Temat


Wyświetlanie 1-2 z 2
Tytuł:
A super depth of field height measurement based on local disparity
Autorzy:
Cui, L.
Liu, Y.
Yu, M.
Jiang, G.
Fan, S.
Wang, Y.
Powiązania:
https://bibliotekanauki.pl/articles/173522.pdf
Data publikacji:
2015
Wydawca:
Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej
Tematy:
microscopes
binocular vision
local disparity map
depth of field (DOF)
height measurement
Opis:
A super depth of field height measurement method is proposed to measure the object height with the optical stereoscopic microscope. The quasi-Euclidean epipolar rectification algorithm is utilized on the original stereoimage to obtain rectified stereoimages and calibrate two camera parameters. Then, feature points are obtained by the SURF (speed up robust feature) algorithm and their corresponding disparities are calculated. The disparity-depth of field curve is fitted by combining the step height values of a stepper motor. Moreover, through local disparity value got from feature points on the object, the relative shift height is calculated through regression analysis. Finally, according to binocular vision geometry, the thickness of the object can be calculated. Experimental results show that the measurement error in Z direction is from 1.51% to 7.71%, which indicates that the proposed method is able to measure the height of a microobject beyond depth of field within a tolerant error.
Źródło:
Optica Applicata; 2015, 45, 2; 205-214
0078-5466
1899-7015
Pojawia się w:
Optica Applicata
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Neural network model for phase-height relationship of each image pixel in 3D shape measurement by machine vision
Autorzy:
Chung, B
Powiązania:
https://bibliotekanauki.pl/articles/173298.pdf
Data publikacji:
2014
Wydawca:
Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej
Tematy:
machine vision
shape measurement
fringe pattern projection
phase-height relationship
neural network
Opis:
In a three-dimensional measurement system based on a digital light processing projector and a camera, a height estimating function is very important. Sinusoidal fringe patterns of the projector are projected onto the object, and the phase of the measuring point is calculated from the camera image. Then, the height of the measuring point is inferred by the phase. The phase-to-height relationship is unique at each image point. However it is nonlinearly different according to the image coordinates. It is also difficult to obtain the geometrical model because of lens distortion. Even though some studies have been performed on neural network models to find the height from the phase and the related coordinates, the results are not good because of the complex relationship. Therefore, this paper proposes a hybrid method that combines a geometric analysis and a neural network model. The proposed method first finds the phase-to-height relationship from a geometric analysis for each image pixel, and then uses a neural network model to find the related parameters for the relationship. The experimental results show that the proposed method is superior to previous neural network methods.
Źródło:
Optica Applicata; 2014, 44, 4; 587-599
0078-5466
1899-7015
Pojawia się w:
Optica Applicata
Dostawca treści:
Biblioteka Nauki
Artykuł
    Wyświetlanie 1-2 z 2

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