This paper describes the investigation and implementation of image processing techniques converted into precision measurement techniques for feature inspection of mesoscale artefacts. The method resulted in a rapid, low-cost, and precise inspection technique. Better performances are obtained with the combination of the pixel uncertainty edge detector and the subpixel uncertainty detector along with camera calibration. This procedure revealed that the subpixel moment-based edge detector presents smooth edge boundaries that are closer to the true contour of the specimen compared to edges detected by Canny edge detector. In addition, it was observed that this smoothness is closely related to the mask size of the moments' calculation. The considered sources of error are the quality of the reference target and the accuracy of the camera calibration. A comparison with the results of the other instruments reveals that the uncertainty of the proposed inspection system could reach more than 5 mm with a much lower resolution and an absolute average difference of about 6 mm between measurements.

SUMMARYThis paper considers intelligent image clustering and proposes a clustering method based on the self-organizing mechanism, which is an adaptive and autonomous algorithm. The proposed method solves the problem of initial value setting, which has been a difficulty in the conventional clustering method, and has the feature that the effective clusters are always self-organized. A method is realized which is robust and does not require empirical setting of the initial value. Thus, the effective initial cluster can always be selected for various kinds of data sets, and an excellent clustering is obtained. A method is proposed in which these features are utilized in image segmentation.

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