Prof. Gui Yun Tian Profile

Prof. Gui Yun Tian

Chair/Sensor Technologies at Newcastle Univ

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Publications (7)

Proceedings Article | 6 March 2015 Paper

Study on the detection of a cylindrical surface-breaking bore-hole reflector based on laser ultrasonic technology

Haitao Wang, Wei Zeng, Xianming Yang, GuiYun Tian, Ruipeng Guo, Guoxing Hu, Min Wan

Proceedings Volume 9446, 94460P (2015) https://doi.org/10.1117/12.2180596

KEYWORDS: Ultrasonics, Reflectors, Laser applications, Ultrasonography, Visualization, Transducers, Laser systems engineering, Pulsed laser operation, Signal detection, Inspection

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Proceedings Article | 31 January 2013 Paper

Visual detection of subsurface defects using enhanced magneto-optic imaging system

Yu Hua Cheng, Xingmake Liu, Gui Tian, Libing Bai

Proceedings Volume 8759, 87591K (2013) https://doi.org/10.1117/12.2014858

KEYWORDS: Molybdenum, Imaging systems, Magneto-optics, Image processing, Image quality, Visualization, Magnetism, Nondestructive evaluation, Image segmentation, Defect detection

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Proceedings Article | 31 January 2013 Paper

Electromagnetic methods for corrosion under paint coating measurement

Hong Zhang, Gui Yun Tian, Anthony Simm, Mohammed Alamin

Proceedings Volume 8759, 875919 (2013) https://doi.org/10.1117/12.2014567

KEYWORDS: Corrosion, Dielectrics, Coating, Phase measurement, Reflection, Microwave radiation, Electromagnetism, Waveguides, Network security, Free space

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Proceedings Article | 16 November 2011 Paper

Pulsed eddy current systems for defect and geometrical profile measurement

Gui Yun Tian, Yunze He, Anthony Simm

Proceedings Volume 8321, 832143 (2011) https://doi.org/10.1117/12.912719

KEYWORDS: Sensors, Magnetism, Nondestructive evaluation, Defect detection, Feature extraction, Electromagnetism, Data fusion, Material characterization, Defect inspection, Analog electronics

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Proceedings Article | 2 August 2010 Paper

The key technologies research on the large field-of-view and high-resolution optical synthesis telescope

Haitao Wang, Qiufeng Luo, Yongkai Zhu, Wantai Ma, Yajing Zhang, Gui Yun Tian

Proceedings Volume 7791, 77910M (2010) https://doi.org/10.1117/12.860386

KEYWORDS: Telescopes, Sensors, Satellites, Mirrors, Space telescopes, Optical design, Actuators, Ultraviolet radiation, Remote sensing, Earth observing sensors

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Proceedings Article | 2 October 2008 Paper

A microwave measurement system for metallic object detection using swept-frequency radar

Yong Li, Gui Tian, Nicholas Bowring, Nacer Rezgui

Proceedings Volume 7117, 71170K (2008) https://doi.org/10.1117/12.801671

KEYWORDS: Microwave radiation, Radar, Metals, Fourier transforms, Sensors, Reflectors, X-rays, Imaging systems, Antennas, Dielectrics

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Proceedings Article | 16 September 1999 Paper

Perfect color constancy vs. color normalization for object recognition

Graham Finlayson, Gui Tian

Proceedings Volume 3826, (1999) https://doi.org/10.1117/12.364324

KEYWORDS: RGB color model, Detection and tracking algorithms, Object recognition, Databases, Light sources and illumination, Sensors, Light sources, Reflectivity, Calibration, Cameras

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Colors recorded in an image depend on the color of the capture illuminant. As such image colors are not stable features for object recognition but we wish they were stable since perceived colors (the colors we see) are illuminant independent and do correlate with object identity. Color constancy algorithms attempt to infer and remove the illuminant color through image analysis. Over the last two decades, various models for color constancy have been developed. Unfortunately, color constancy algorithms are still not good enough to support object recognition. In this paper, we evaluate optimal color constancy procedures against color normalization. Two perfect color constancy algorithms are described. One is perfect color constancy by the scene, which arrives at an estimate of the illuminant not through algorithmic inference, but through measurement: the light source is measured using a spectraradiometer, assuming the reflectances of object surface are known. The other is perfect color color constancy by the illuminant, which arrives at an estimate of the illuminant through measurement, assuming the reflectances of object surface are unknown. Instead of color constancy, color normalization normalizes color images in terms of the context to remove illumination. To remove dependency due to illumination, images in a calibrated dataset are preprocessed using either the color constancy or color invariant normalization. Two experiments are reported in the paper. In the first experiment, the optimal algorithms of perfect color constancy based on measurement were tested using a calibrated image dataset. In the second experiment, the performances of the optimal color constancy algorithms are compared with color invariant normalization. Unfortunately, measurement driven color constancy by the illuminant does not support perfect recognition. However, color constancy preprocessing based on a scene dependent 'effective illuminant' facilitates near-perfect recognition. In comparison the color invariant normalization also deliver near-perfect recognition. The failure of color constancy by the illuminant is understandable because the measured illuminant doesn't correspond to the actual effective illuminant. Rather, we found illumination to depend both on the light source and characteristics of the scene.

Showing 5 of 7 publications

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