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As a prerequisite, the thread is segmented within the scannedtextile image and the extracted pixels form a mask. This partdoes not yet associate image pixels to individual stitch points.The subsequent process of finding the stitch positions in thesewn textile consists of three steps. First a rigid transformationpositions the model closely to the segmented thread pixels.Subsequently, a template matching procedure compares asimulation image patch of the region around each model stitchpoint with the actual segmentation mask. That step resultsin an evaluation score for a pixel being a possible candidatefor an individual stitch point. A path search algorithm finallyoptimizes a global energy criterion. This criterion is formedat one hand by the evaluation scores and on the other handby the mechanical elasticity of the textile material that cannotbe freely deformed without force.
A simple threshold procedure after the filtering makes itpossible to robustly determine in which image the thread iswell visible and thus produces a sufficient number of threadpixels that can simply be calculated from the model. The otherimage just contains noise and can be neglected. The advantageof this separation is an increased signal-to-noise ratio becausethe thread signal is just contained in one of the images, yetspurious noise is distributed over both of them and furtherreduced by clipping via themaximumoperation in (1) and(2). In the end, all pixels with a higher probability than agiven threshold value form a preliminary thread pixel maskwF Ras initial guess. Usually this mask can still contain somepixels not really belonging to the thread but the surroundingsubstrate.
