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The paper presents an automated method for the assessment and classificationof puckering defects detected during the preproduction control stage of thesewing machine or product inspection. In this respect, we have presented thepossible causes and remedies of the wrinkle nonconformities. Subjective factorsrelated to the control environment and operators during the seams evaluationcan be reduced using an automated system whose operation is based on imageprocessing. Our implementation involves spectral image analysis using Fouriertransform and an unsupervised neural network, the Kohonen Map, employed toclassify material specimens, the input images, into five discrete degrees ofquality, from grade 5 (best) to grade 1 (the worst).

Seam Puckering Objective Evaluation Method for Sewing Process