In this work a machine vision system for inspecting pharmaceutical color
tablets is presented and implemented. Nonparametric clustering based
segmentation is faster and thus more appropriate for real-time applications.
Two nonparametric clustering based methods, Nearest Neighbor algorithm
and MaxShift algorithm are worked in RGB and HSV color spaces as the
segmentation step. The implemented algorithm allows the system to detect
the missing and broken tablets, tablet fragments, and the color, size, and
shape of individual tablets in pharmaceutical blisters, in real-time. System
has two operation modes called & / #8216 / & / #8216 / training& / #8217 / & / #8217 / and & / #8216 / & / #8216 / inspection& / #8217 / & / #8217 / mode,
respectively. Operator selects one point on any tablet in a defect-free training
captured image in the & / #8216 / & / #8216 / training& / #8217 / & / #8217 / mode. In the correction step an optimization
algorithm is required, for which Powell and Downhill Simplex methods are
used. Captured image is then corrected for spatial color nonuniformity, segmented, and the position, size, shape, and color of each tablet are
extracted in the training mode. The correction and segmentation models / the extracted features generated in the training mode is saved with the user
defined values to form the model. Each acquired image in the inspection
mode is corrected and segmented according to the blister model and then
the blisters are classified as & / #8216 / & / #8216 / good& / #8217 / & / #8217 / or & / #8216 / & / #8216 / bad& / #8217 / & / #8217 / by comparing the extracted
feature values with the user defined tolerances stored in the blister model.
Identifer | oai:union.ndltd.org:METU/oai:etd.lib.metu.edu.tr:http://etd.lib.metu.edu.tr/upload/12607235/index.pdf |
Date | 01 May 2006 |
Creators | Akturk, Deniz |
Contributors | Demirbas, Kerim |
Publisher | METU |
Source Sets | Middle East Technical Univ. |
Language | English |
Detected Language | English |
Type | M.S. Thesis |
Format | text/pdf |
Rights | To liberate the content for public access |
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