Dr. Chi N. Thai Profile

Dr. Chi N. Thai

at Univ of Georgia

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Publications (2)

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Proceedings Article | 23 October 2006 Paper

Real-time multispectral imaging system for online poultry fecal inspection using UML

Bosoon Park, Michio Kise, Kurt Lawrence, William Windham, Douglas Smith, Chi Thai

Proceedings Volume 6381, 63810W (2006) https://doi.org/10.1117/12.686379

KEYWORDS: Image processing, Imaging systems, Cameras, Multispectral imaging, Inspection, Real time image processing, Computer architecture, RGB color model, Systems modeling, Calibration

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A prototype real-time multispectral imaging system for fecal and ingesta contaminant detection on broiler carcasses has been developed. The prototype system includes a common aperture camera with three optical trim filters (517, 565 and 802-nm wavelength), which were selected by visible/NIR spectroscopy and validated by a hyperspectral imaging system with decision tree algorithm. The on-line testing results showed that the multispectral imaging technique can be used effectively for detecting feces (from duodenum, ceca, and colon) and ingesta on the surface of poultry carcasses with a processing speed of 140 birds per minute. This paper demonstrated both multispectral imaging hardware and real-time image processing software. For the software development, the Unified Modeling Language (UML) design approach was used for on-line application. The UML models included class, object, activity, sequence, and collaboration diagram. User interface model included seventeen inputs and six outputs. A window based real-time image processing software composed of eleven components, which represented class, architecture, and activity. Both hardware and software for a real-time fecal detection were tested at the pilot-scale poultry processing plant. The run-time of the software including online calibration was fast enough to inspect carcasses on-line with an industry requirement. Based on the preliminary test at the pilot-scale processing line, the system was able to acquire poultry images in real-time. According to the test results, the imaging system is reliable for the harsh environments and UML based image processing software is flexible and easy to be updated when additional parameters are needed for in-plant trials.

Proceedings Article | 14 January 1999 Paper

Development of short-wavelength near-infrared spectral imaging for grain color classification

Douglas Archibald, Chi Thai, Floyd Dowell

Proceedings Volume 3543, (1999) https://doi.org/10.1117/12.336882

KEYWORDS: Near infrared, Reflectivity, Imaging spectroscopy, Imaging systems, Spectroscopy, Agriculture, Image segmentation, Statistical analysis, Visible radiation, Tungsten

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Color class of wheat is an important attribute for the identification of cultivars and the marketing of wheat, but is not always easy to measure in the visible spectral range because of variation in vitreosity and surface structure of the kernels. This work examines whether short-wavelength near IR imaging in the range 632-1098 nm can be used to distinguish different cultivars. The spectral characteristics of six hard white winter and hard red spring wheats were first studied by bulk-sample SW-NIR reflectance spectroscopy using regression analysis to select appropriate wavelengths and sets of wavelengths. Prediction of percent red wheat was better if C-H or O-H vibrational overtones were included in the models in addition to the tail from the visible chromophore absorbance, apparently because the vibrational bands make it possible to normalize the color measurement to the dry matter content of the samples. Next, a reflectance spectral image of 640 X 480 spatial pixels and 11 wavelengths was acquired for a mixture of the two contrasting wheat samples using a CCD camera and a liquid crystal tunable filter. The cultivars were distinguished in the image of principal component (PC) score number two that was calculated from the spectral image. The discrimination is due to the tail from the absorbance band that peaks in the visible. PC images 3 and 6 seem to arise mainly from O-H and C-H bands, respectively, and it is speculated that these spectral features will be important for generating multivariate models to predict the color class of grain. It is shown that the contrast between the red-wheat, white- wheat and background can be increased by applying histogram equalization and segmentation of the kernels in the images.

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