Fabric Texture Analysis Using Computer Vision Techniques

Xin Wang

doi:10.1109/TIM.2010.2069850

2011-01-01

101

PRE

Xin Wang *

Nicolas D. Georganas

Emil M. Petriu

DOI：10.1109/TIM.2010.2069850

原文链接

原文求助

摘要

En 中文

This paper presents inexpensive computer vision techniques allowing to measure the texture characteristics of woven fabric, such as weave repeat and yarn counts, and the surface roughness. First, we discuss the automatic recognition of weave pattern and the accurate measurement of yarn counts by analyzing fabric sample images. We propose a surface roughness indicator FDFFT, which is the 3-D surface fractal dimension measurement calculated from the 2-D fast Fourier transform of high-resolution 3-D surface scan. The proposed weave pattern recognition method was validated by using computer-simulated woven samples and real woven fabric images. All weave patterns of the tested fabric samples were successfully recognized, and computed yarn counts were consistent with the manual counts. The rotation invariance and scale invariance of FDFFT were validated with fractal Brownian images. Moreover, to evaluate the correctness of FDFFT, we provide a method of calculating standard roughness parameters from the 3-D fabric surface. According to the test results, we demonstrated that FDFFT is a fast and reliable parameter for fabric roughness measurement based on 3-D surface data.

Keyword:

Computer vision

fractal dimension

fuzzy c-means clustering (FCM)

grey level cooccurrence matrix (GLCM)

principal component analysis (PCA)

surface roughness

texture analysis

woven fabric

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期刊

IEEE Transactions on Instrumentation and Measurement

IF:

5.9

论文数:

1.9W

被引数:

5.8W

机构

University of Ottawa

学者数:

3.5W

论文数: 3.1W

被引数: 3.8W

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