On-Line Multi-Damage Scanning Spatial-Wavenumber Filter Based Imaging Method for Aircraft Composite Structure

Abstract Heartwood detection for Scotch pine (Pinus sylvestris) is possible via several different methods, such as X-ray and infrared cameras. X-ray systems are often very expensive and need huge modifications of sawmill equipment for implementation. Infrared cameras are more affordable but can only be applied for fresh samples with high water content. This article describes a novel imaging method for heartwood detection for Scotch pine, which is based on the fluorescence information emitted of pinosylvin type stilbene and its monomethyl ether. Fluorescence imaging is a low-cost method for measuring heartwood content in real time on an industrial line. The method is applicable for fresh and dried samples. This paper will show the results of fluorescence matrices of Scotch pine. Measurements were made with a bispectrometer system to obtain suitable wavelengths for the excitation and detection of fluorescence. A reliable measuring system is described, which is suited for on-line measurements on an industrial line. An algorithm will also be presented, which is useful for quantification of heartwood and sapwood.

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A laser imaging method for machine vision detection of white contaminants in cotton

Textile Research Journal ◽

10.1177/0040517514530027 ◽

2014 ◽

Vol 84 (18) ◽

pp. 1987-1994 ◽

Cited By ~ 6

Author(s):

Feng Liu ◽

Zhenwei Su ◽

Xiangcheng He ◽

Chaoyong Zhang ◽

Mouqin Chen ◽

...

Keyword(s):

Machine Vision ◽

Detection Rate ◽

Optical Characteristic ◽

Experimental Results ◽

Imaging Method ◽

Vision Systems ◽

Laser Imaging ◽

On Line ◽

The Mean ◽

Vision Detection

The existing machine vision systems cannot efficiently detect white contaminants in cotton under the illumination of visible lights, because their color is the same or very close. To solve the problem, this article proposes an imaging method based on line lasers. Under the illumination of a line laser, the white contaminants and cotton showed the differences in the optical characteristic of their surface. Then, according to the features of the intensity of their reflected lights or the distribution of the fluff around their surfaces in the images, an example algorithm for identification of white contaminants from cotton was suggested. The experimental results indicated that, using our method, the mean successful detection rate of the typical white contaminants in cotton was over 87%.

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A Four-Channel Millimeter-Wave On-Line Imaging Method

Inverse Methods in Electromagnetic Imaging ◽

10.1007/978-94-009-5271-3_19 ◽

1985 ◽

pp. 1023-1032

Author(s):

Jörgen Detlefsen

Keyword(s):

Millimeter Wave ◽

Imaging Method ◽

On Line ◽

Line Imaging

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A New Rapid On-Line Imaging Method to Determine Particle Size Distribution of Granules

AAPS PharmSciTech ◽

10.1208/s12249-008-9043-y ◽

2008 ◽

Vol 9 (1) ◽

pp. 282-287 ◽

Cited By ~ 30

Author(s):

Tero Närvänen ◽

Kari Seppälä ◽

Osmo Antikainen ◽

Jouko Yliruusi

Keyword(s):

Particle Size ◽

Particle Size Distribution ◽

Size Distribution ◽

Imaging Method ◽

On Line ◽

Line Imaging

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Anisotropy compensated MUSIC algorithm based composite structure damage imaging method

Composite Structures ◽

10.1016/j.compstruct.2019.02.036 ◽

2019 ◽

Vol 214 ◽

pp. 293-303 ◽

Cited By ~ 5

Author(s):

Qiao Bao ◽

Shenfang Yuan ◽

Yanwen Wang ◽

Lei Qiu

Keyword(s):

Composite Structure ◽

Imaging Method ◽

Music Algorithm ◽

Structure Damage ◽

Damage Imaging

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Weak Beam Imaging and Diffraction Studies of Stacking Faults in Silicon

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100092918 ◽

1976 ◽

Vol 34 ◽

pp. 590-591

Author(s):

T. Y. Tan ◽

W. K. Tice

Keyword(s):

Fast Neutron ◽

Rapid Determination ◽

Stacking Faults ◽

Imaging Method ◽

Large Reduction ◽

Dislocation Loops ◽

Fringe Spacing ◽

Weak Beam ◽

Kinematical Theory

In studying ion implanted semiconductors and fast neutron irradiated metals, the need for characterizing small dislocation loops having diameters of a few hundred angstrom units usually arises. The weak beam imaging method is a powerful technique for analyzing these loops. Because of the large reduction in stacking fault (SF) fringe spacing at large sg, this method allows for a rapid determination of whether the loop is faulted, and, hence, whether it is a perfect or a Frank partial loop. This method was first used by Bicknell to image small faulted loops in boron implanted silicon. He explained the fringe spacing by kinematical theory, i.e., ≃l/(Sg) in the fault fringe in depth oscillation. The fault image contrast formation mechanism is, however, really more complicated.

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The on-line use of histogram data for high-speed correction and alignment of electron microscope images

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100112713 ◽

1984 ◽

Vol 42 ◽

pp. 556-557

Author(s):

William Krakow

Keyword(s):

Power Spectrum ◽

High Speed ◽

Direct Memory Access ◽

Digital Television ◽

Contrast Transfer Function ◽

The Past ◽

High Resolution Tem ◽

On Line ◽

Correction Of Astigmatism ◽

The Fourier Transform

In the past few years on-line digital television frame store devices coupled to computers have been employed to attempt to measure the microscope parameters of defocus and astigmatism. The ultimate goal of such tasks is to fully adjust the operating parameters of the microscope and obtain an optimum image for viewing in terms of its information content. The initial approach to this problem, for high resolution TEM imaging, was to obtain the power spectrum from the Fourier transform of an image, find the contrast transfer function oscillation maxima, and subsequently correct the image. This technique requires a fast computer, a direct memory access device and even an array processor to accomplish these tasks on limited size arrays in a few seconds per image. It is not clear that the power spectrum could be used for more than defocus correction since the correction of astigmatism is a formidable problem of pattern recognition.

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