Limits...
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.


Illustration of the machine vision system for micro-spray nozzle defect inspection.
© Copyright Policy
Related In: Results  -  Collection

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

sensors-15-15326-f004: Illustration of the machine vision system for micro-spray nozzle defect inspection.

Mentions: The machine vision system implemented to inspect the inner images of micro-spray nozzles is illustrated in Figure 4. This system includes an IEEE 1394 CCD color camera (DFK-31BF03, Imaging Source Inc., Bremen, Germany), a stereomicroscope (Stemi 2000-C, Zeiss Inc., Oberkochen, Germany), a front illuminating white light LED with a diffuse filter, a fixed table and a personal computer. Open Source Computer Vision Library (OpenCV1.0, Intel Corporation) and Visual C++ 6.0 programming are linked to the programs to grab images of 1024 × 768 pixels.


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

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

Illustration of the machine vision system for micro-spray nozzle defect inspection.
© Copyright Policy
Related In: Results  -  Collection

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

sensors-15-15326-f004: Illustration of the machine vision system for micro-spray nozzle defect inspection.
Mentions: The machine vision system implemented to inspect the inner images of micro-spray nozzles is illustrated in Figure 4. This system includes an IEEE 1394 CCD color camera (DFK-31BF03, Imaging Source Inc., Bremen, Germany), a stereomicroscope (Stemi 2000-C, Zeiss Inc., Oberkochen, Germany), a front illuminating white light LED with a diffuse filter, a fixed table and a personal computer. Open Source Computer Vision Library (OpenCV1.0, Intel Corporation) and Visual C++ 6.0 programming are linked to the programs to grab images of 1024 × 768 pixels.

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.