24 February 2005 Inspection and measurement of a DWDM core with two stacked glass objects
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In this paper, a smart algorithm is presented to accurately recognize and measure three transparent objects, GRIN lens (circle), thin-film filter (square), and epoxy (random) which are essential parts of a DWDM core. After trial-and-errors, a specially arranged illumination and a black color metal coated 10x10 tray, which was applied to perfectly absorb the light traveled to the bottom surface of the core, were chosen to simplify the algorithm. During the image capturing procedure, the first image was focus on the filter’s top surface and the second images were taken after the stage was moved 0.7 mm lower onto the GRIN lens’ top surface. Threshold and filtering were then used to process images. Blob analysis, edge detection and Angle-of-sight (AOS) signature were utilized to find the exact centers of the lens and the filter. During the preliminary tests, the successful recognition rate was 95% (34 out of 36 pcs.). The two failures were due to the severely damaged edges of the filter. As shown in the results, the computer can easily perform the vision functions just like human beings as long as the human thinking had been considered in the algorithm. As mentioned above, a special light source can simplify the challenging task such as the separation of several transparent objects. By teaching the machine to analyze the AOS signature, it can recognize an object naturally. This is one kind of approaches for human to solve problems. The computer programs were initially developed using the script language provided by the Matrox’s Inspection 3 and ported into the Microsoft Visual Basic (VB) with the Matrox’s MIL image library for VB later. The entire hardware was designed as a plug-in module and the software used the object oriented (OO) concept which can be straightforwardly employed by other programmers.
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Steven Chanteh Kuo, Steven Chanteh Kuo, "Inspection and measurement of a DWDM core with two stacked glass objects", Proc. SPIE 5679, Machine Vision Applications in Industrial Inspection XIII, (24 February 2005); doi: 10.1117/12.586105; https://doi.org/10.1117/12.586105

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