Detection of defects in fabrics using topothesy fractal dimension features
During the manufacturing of textiles, several types of defects occur in the fabrics. This paper explores the characterization of the fabric textures using the conventional approaches such as Gabor filter, Gabor wavelet and Gauss Markov random field (MRF) and the well-known method for surface roughness measurement in the mechanical engineering called topothesy. The topothesy and fractal dimension known as fractal parameters represent not only the roughness but also the affine self-similarity in fabric textures. The fabric texture features are tested on the database of four types of defective fabric samples, viz., torn fabric, oil stain, miss pick and interlacing of two webs, collected from the cloth mills of Berhampur. A comparison of the results of defect detection in fabrics indicates that the topothesy fractal dimension features outperform those of Gabor filter, Gabor wavelets and Gauss MRF.
topothesy fractal dimension gabor filter fabric defects transformed structure function shannon transform