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A Novel Machine Vision System for the Inspection of Micro-Spray Nozzle.

Huang KY, Ye YT - Sensors (Basel) (2015)

Bottom Line: These texture features (contrast, entropy, energy), color features (mean and variance of gray level) and geometric features (distance variance, mean diameter and diameter ratio) were used in the classification procedures.A back-propagation neural network classifier was employed to detect the defects of micro-spray nozzles.The methodology presented herein effectively works for detecting micro-spray nozzle defects to an accuracy of 90.71%.

View Article: PubMed Central - PubMed

Affiliation: Department of Bio-Industrial Mechatronics Engineering, National Chung Hsing University, Tai-Chung 402, Taiwan. kuoyi@nchu.edu.tw.

ABSTRACT
In this study, we present an application of neural network and image processing techniques for detecting the defects of an internal micro-spray nozzle. The defect regions were segmented by Canny edge detection, a randomized algorithm for detecting circles and a circle inspection (CI) algorithm. The gray level co-occurrence matrix (GLCM) was further used to evaluate the texture features of the segmented region. These texture features (contrast, entropy, energy), color features (mean and variance of gray level) and geometric features (distance variance, mean diameter and diameter ratio) were used in the classification procedures. A back-propagation neural network classifier was employed to detect the defects of micro-spray nozzles. The methodology presented herein effectively works for detecting micro-spray nozzle defects to an accuracy of 90.71%.

No MeSH data available.


Outlet image segmentation. (a) Orignal image; (b) Outlet image.
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sensors-15-15326-f005: Outlet image segmentation. (a) Orignal image; (b) Outlet image.

Mentions: Segmentation of the ROI image is an essential procedure once the features of the micro-spray nozzle have been extracted. Firstly, the outlet image (as shown in Figure 5) is segmented using Otsu’s auto-thresholding method and hole-filling operations [13]. By assuming that the binary image of the outlet is(where i = 1, 2, …, m and the total number of pixels is m), the centroid is obtained as,. The covariance matrix is defined as, in which is the i-th coordinate vector of the image and is the mean vector. T indicates vector transposition. A pair of orthogonal eigenvectors of the covariance matrix is then calculated. The geometric features—principle axis length (Lp), secondary axis (Ls), centroid, area (A), perimeter (P), compactness (P2/4πA), diameter ratio (min./max. diameter, Dmm) and mean diameter (Dmean) of the outlet—are computed using eigenvectors. Secondly, two geometric features—diameter ratio and mean diameter—are employed in the micro-spray nozzle classification process.


A Novel Machine Vision System for the Inspection of Micro-Spray Nozzle.

Huang KY, Ye YT - Sensors (Basel) (2015)

Outlet image segmentation. (a) Orignal image; (b) Outlet image.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4541833&req=5

sensors-15-15326-f005: Outlet image segmentation. (a) Orignal image; (b) Outlet image.
Mentions: Segmentation of the ROI image is an essential procedure once the features of the micro-spray nozzle have been extracted. Firstly, the outlet image (as shown in Figure 5) is segmented using Otsu’s auto-thresholding method and hole-filling operations [13]. By assuming that the binary image of the outlet is(where i = 1, 2, …, m and the total number of pixels is m), the centroid is obtained as,. The covariance matrix is defined as, in which is the i-th coordinate vector of the image and is the mean vector. T indicates vector transposition. A pair of orthogonal eigenvectors of the covariance matrix is then calculated. The geometric features—principle axis length (Lp), secondary axis (Ls), centroid, area (A), perimeter (P), compactness (P2/4πA), diameter ratio (min./max. diameter, Dmm) and mean diameter (Dmean) of the outlet—are computed using eigenvectors. Secondly, two geometric features—diameter ratio and mean diameter—are employed in the micro-spray nozzle classification process.

Bottom Line: These texture features (contrast, entropy, energy), color features (mean and variance of gray level) and geometric features (distance variance, mean diameter and diameter ratio) were used in the classification procedures.A back-propagation neural network classifier was employed to detect the defects of micro-spray nozzles.The methodology presented herein effectively works for detecting micro-spray nozzle defects to an accuracy of 90.71%.

View Article: PubMed Central - PubMed

Affiliation: Department of Bio-Industrial Mechatronics Engineering, National Chung Hsing University, Tai-Chung 402, Taiwan. kuoyi@nchu.edu.tw.

ABSTRACT
In this study, we present an application of neural network and image processing techniques for detecting the defects of an internal micro-spray nozzle. The defect regions were segmented by Canny edge detection, a randomized algorithm for detecting circles and a circle inspection (CI) algorithm. The gray level co-occurrence matrix (GLCM) was further used to evaluate the texture features of the segmented region. These texture features (contrast, entropy, energy), color features (mean and variance of gray level) and geometric features (distance variance, mean diameter and diameter ratio) were used in the classification procedures. A back-propagation neural network classifier was employed to detect the defects of micro-spray nozzles. The methodology presented herein effectively works for detecting micro-spray nozzle defects to an accuracy of 90.71%.

No MeSH data available.