Segmentation of textile prints with contour-stable color masks for industrial stencil printing using artificial intelligence
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2025
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This paper presents an AI-driven approach for segmenting textile prints to obtain contour-stable binary or multi-class masks suitable for industrial stencil printing and color separation. The method combines neural network segmentation with minimalistic, topologically motivated post-processing to ensure accurate, reproducible boundaries under moderate variations in lighting, shooting conditions, and prepress transformations. Unlike traditional planar metrics, the proposed evaluation prioritizes boundary stability, contour integrity, and technological suitability for cliché preparation. Experimental results on complex textile textures demonstrate consistent mask quality, reduced manual refinement, lower ink consumption, and fewer defects in serial printing. The solution supports sustainable textile production by decreasing prepress energy and material waste while increasing process repeatability and printing accuracy. Further work will expand the dataset, refine boundary corrections for specific fabrics, and formalize end-to-end evaluation protocols from digital segmentation to control print.
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Lianskorunskyi K.O., Klimenko V.I., Sobko O.V. Segmentation of textile prints with contour-stable color masks for industrial stencil printing using artificial intelligence. Resource-Saving Technologies of Apparel, Textile & Food Industry. Proceedings of International Scientific and Practical Conference. November 20, 2025. Khmelnytskyi, Ukraine. Pp. 304-308