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The proposed method works without training data and in afully unsupervised way. It only makes use of the seam patternmodel described in the software for the CNC device that isdirectly read in and transformed into the sequence of stitchpoint coordinates. The huge possible variability of substratematerials and colors together with those of the thread wouldit make difficult to cover that in a training procedure wherethe training data must be the result of actual sewing or atleast a realistic simulation. Therefore, the adaptive parts ofthe algorithm, e.g. theExpectation Maximizationor thePathSearch, utilize just the scanned image and the model of thetest data.The paper presents in Section II the implemented algorithmand in Section III the results achieved on a database of sewntextiles .
There is a large variety of different algorithms to registera model onto an image scan. In order toaccount for image regions where the target thread is badly ornot at all visible, or where noise in the scan forms structureslooking like a thread, a very robust algorithm is necessary.Hence, we chose an approach that takes advantage of theCNC model consisting of the sequence of the intended stitchcoordinates. It can conveniently be generated from the samesoftware program that is also exploited by the CNC sewingdevice for sewing the seam. In addition to these coordinates,both the thread thickness and stitch diameter are importantparameters that must be preserved in a software simulationof the desired seam pattern used to generate filter patches.
