Date

2006

Author

Aktürk, Deniz

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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 ‘‘training’’ and ‘‘inspection’’ mode, respectively. Operator selects one point on any tablet in a defect-free training captured image in the ‘‘training’’ 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 ‘‘good’’ or ‘‘bad’’ by comparing the extracted feature values with the user defined tolerances stored in the blister model.

Subject Keywords

Electrical Engineering., Electronics., Nuclear Engineering.

URI

http://etd.lib.metu.edu.tr/upload/12607235/index.pdf
https://hdl.handle.net/11511/15997

Collections

Graduate School of Natural and Applied Sciences, Thesis