Image analysis – Applications – Textiles or clothing
Reexamination Certificate
2001-04-18
2004-10-12
Johns, Andrew W. (Department: 2621)
Image analysis
Applications
Textiles or clothing
C382S260000, C356S430000, C356S238200
Reexamination Certificate
active
06804381
ABSTRACT:
FIELD OF THE INVENTION
The present invention relates generally to a method of and device for inspecting images to detect defects. In particular, the present invention relates to a method and device for inspecting textured materials to detect defects therein.
DESCRIPTION OF THE PRIOR ART
Automated manufacture requires automated inspection of industrial materials, such as textile, paper, and plastic. Automated inspection of industrial materials needs adaptive solutions that can be executed in real time. Currently, a key element of quality assurance in production lines is manual inspection. Manual inspection is labor intensive and insufficient to maintain quality standards at high-speed production. For example in textile industry only about 70% of defects are being detected by manual inspection even with highly trained inspectors. Therefore automation of visual inspection task is desired to increase the efficiency of production lines and to improve quality of product as well.
Industrial inspection has extremely high requirements and is most challenging as compared to other inspection problems. A typical web is 6-10 feet wide and is processed at the speed of 20-60 m/min. Consequently the throughput for 100% inspection is tremendous, e.g., 10-15 MB image data per second when using line-scan camera. Therefore most feasible solutions require additional hardware components and reduction in calculation complexity.
Defect detection in industrial materials has been a topic of considerable research using different approaches. Researchers have frequently used fabric samples to model the general problem of defect detection in various textured materials. Various approaches that use mean and standard deviation of sub blocks, gray level co-occurrence matrix, and autocorrelation of images have been used for characterization of fabric defects. At microscopic level, broad spectrum of different material inspection problems reduce to texture analysis problems. Several researchers have tried to address this problem with various approaches ranging from Gauss Markov Random Field (GMRF) modeling, Karhunen-Loève decomposition, Gabor filter, wavelet transform to neural networks.
Periodicity of yarns in textile webs results in Fourier domain features and has been used to explore fabric defects. U.S. Pat. No. 4,124,300 issued to Mead et al. on Nov. 7, 1978 discusses such an approach. Fourier transform based techniques are suitable for defects that cause global distortion of basic structure but unsuccessful for local defects that usually occur in small area of images. Consequently, detection of local fabric defects requires simultaneous measurements in spatial and spatial frequency domain. Accordingly, texture features based on Multiscale Wavelet Representation (MSWAR) has been used to detect local fabric defects. U.S. Pat. No. 5,815,198 issued to Vachtsevanos, et al. Sep. 29, 1998 discloses such an approach.
Ultrasonic transducers for inspecting industrial materials are also known. For example, U.S. Pat. No. 5,665,907 issued to Sheen et al. on Sep. 9, 1997 discloses an ultrasonic system for detecting fabric defects. U.S. Pat. No. 6,023,334 issued to Itagaki et al. on Feb. 8, 2000 discloses an approach of using brightness information to inspect homogenous surfaces such as plain aluminum sheets or plain glass.
The drawback of conventional approaches is that they are not sensitive enough to detect defect that produces subtle intensity transitions and consequently can not guarantee 100% inspection. Further, conventional approaches require statistical computations (e.g., mean and standard deviation) for their on-line implementation. Such computations are complex and require additional hardware.
Therefore, it is desirable to provide an inspection system that requires no on-line statistical computations. It is also desirable to provide a system which is capable of detecting defects that produce very subtle intensity transitions in acquired images. The present invention provides an inspection system that overcomes shortcomings of existing methods for defect detection.
SUMMARY OF THE INVENTION
The present invention relates to a method of inspecting a web material to detect defects. According to the present invention, the values of pixels in a defect-free region can be greatly attenuated relative to those in a defect region in various manners. For example, the energy of pixels in a defect-free region and in a defect region can be obtained to segment defects. In one embodiment, a finite impulse response filter (FIR) can be used to select those frequencies, which can discriminate the energy of a local defect-free region from that of a local defect region to thereby detect defects.
The present invention also relates to a filter device for inspecting a web material to detect defects. The filter device can be designed by first obtaining the correlation matrices from the fabric samples and the eigenvectors. Then, the eigenvector yielding the maximum object function can be selected. The optimal filter h
op
(x, y) can be obtained, such as by inversing lexicographical reordering.
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patent: 5815198 (1998-09-01), Vachtsevanos et al.
patent: 5825501 (1998-10-01), Mee et al.
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Kumar Ajay
Pang Kwok-Hung Grantham
Dickstein Shapiro Morin & Oshinsky LLP.
Johns Andrew W.
The University of Hong Kong
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