| Summary: | 碩士 === 元智大學 === 工業工程研究所 === 87 === In this research, we use color machine vision to detect defects in homogenous texture surfaces. In order to prevent the noise interference in the spatial domain, we employ Gabor transform method in the frequency domain to detect defects. The traditional Gabor transform method is based on gray-scale image processing, which utilizes single gray-scale information to analyze textures, thus it is fairly easy to lose important color information and fails in defect detection. In this research the Gabor transform method in the frequency domain is incorporated with two color features derived from color spaces to detect defects in colored texture surfaces.
Gabor transform converts the image information from spatial domain into frequency domain, and represents the texture features in a sliding window to reduce the noise interference. Basically, Gabor transform method is a non-linear sinusoidal function. It contains three parameters in the transform process, namely, frequency, orientation and bandwidth, and uses these three parameters as the texture features. In this research, two brightness-invariant color features obtained from color models are used to form a complex number, which replaces single gray-scale in the gray image, for colored texture representation. The proposed Color Gabor transform convolutes the two-color-feature complex number with the Gabor filter in a sliding window. A homogeneous region will generate zero-energy response in the convoluted image, whereas a defective region will yield large energy response. Experimented on knitted cloth, wood, checkered cloth and weave have shown that the proposed Color Gabor transform can accurately detect the defect which traditional Gabor transform can not perform in gray-scale images.
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