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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.


(a) Original image; (b) after Canny edge detection; (c) randomized algorithm for detecting circles; (d) the image after hole-filling operation; (e) ROI image with the AND logic operator for (a,d).
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sensors-15-15326-f007: (a) Original image; (b) after Canny edge detection; (c) randomized algorithm for detecting circles; (d) the image after hole-filling operation; (e) ROI image with the AND logic operator for (a,d).

Mentions: Canny edge detection for the original image (Figure 7a), shown as Figure 7b.


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

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

(a) Original image; (b) after Canny edge detection; (c) randomized algorithm for detecting circles; (d) the image after hole-filling operation; (e) ROI image with the AND logic operator for (a,d).
© Copyright Policy
Related In: Results  -  Collection

License
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getmorefigures.php?uid=PMC4541833&req=5

sensors-15-15326-f007: (a) Original image; (b) after Canny edge detection; (c) randomized algorithm for detecting circles; (d) the image after hole-filling operation; (e) ROI image with the AND logic operator for (a,d).
Mentions: Canny edge detection for the original image (Figure 7a), shown as Figure 7b.

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.