Dr. Bosoon Park Profile

Dr. Bosoon Park

Lead Scientist at USDA Agricultural Research Service

SPIE Involvement:

Conference Program Committee | Author

Publications (37)

PROCEEDINGS ARTICLE | June 6, 2017

Big data analytics in hyperspectral imaging for detection of microbial colonies on agar plates (Conference Presentation)

Proc. SPIE. 10217, Sensing for Agriculture and Food Quality and Safety IX

KEYWORDS: Optical imaging, Hyperspectral imaging, Analytics, Agriculture, Light scattering, Reflectivity, Pathogens, Mining, Machine learning, Algorithm development

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SPIE Journal Paper | August 10, 2015

Hyperspectral imaging using RGB color for foodborne pathogen detection

Yoon, Seung-Chul , Shin, Tae-Sung , Lawrence, Kurt , Heitschmidt, Gerald , Park, Bosoon , Gamble, Gary

JEI Vol. 24 Issue 04

KEYWORDS: Hyperspectral imaging, RGB color model, Reflectivity, Pathogens, Image classification, Cameras, Performance modeling, Reconstruction algorithms, Algorithm development, Statistical modeling

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PROCEEDINGS ARTICLE | March 13, 2015

Hyperspectral imaging using a color camera and its application for pathogen detection

Proc. SPIE. 9405, Image Processing: Machine Vision Applications VIII

KEYWORDS: Hyperspectral imaging, Cameras, Calibration, Reflectivity, Xenon, Pathogens, Image classification, Reconstruction algorithms, Algorithm development, RGB color model

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This paper reports the results of a feasibility study for the development of a hyperspectral image recovery (reconstruction) technique using a RGB color camera and regression analysis in order to detect and classify colonies of foodborne pathogens. The target bacterial pathogens were the six representative non-O157 Shiga-toxin producing Escherichia coli (STEC) serogroups (O26, O45, O103, O111, O121, and O145) grown in Petri dishes of Rainbow agar. The purpose of the feasibility study was to evaluate whether a DSLR camera (Nikon D700) could be used to predict hyperspectral images in the wavelength range from 400 to 1,000 nm and even to predict the types of pathogens using a hyperspectral STEC classification algorithm that was previously developed. Unlike many other studies using color charts with known and noise-free spectra for training reconstruction models, this work used hyperspectral and color images, separately measured by a hyperspectral imaging spectrometer and the DSLR color camera. The color images were calibrated (i.e. normalized) to relative reflectance, subsampled and spatially registered to match with counterpart pixels in hyperspectral images that were also calibrated to relative reflectance. Polynomial multivariate least-squares regression (PMLR) was previously developed with simulated color images. In this study, partial least squares regression (PLSR) was also evaluated as a spectral recovery technique to minimize multicollinearity and overfitting. The two spectral recovery models (PMLR and PLSR) and their parameters were evaluated by cross-validation. The QR decomposition was used to find a numerically more stable solution of the regression equation. The preliminary results showed that PLSR was more effective especially with higher order polynomial regressions than PMLR. The best classification accuracy measured with an independent test set was about 90%. The results suggest the potential of cost-effective color imaging using hyperspectral image classification algorithms for rapidly differentiating pathogens in agar plates.

PROCEEDINGS ARTICLE | March 7, 2014

Hyperspectral image reconstruction using RGB color for foodborne pathogen detection on agar plates

Proc. SPIE. 9024, Image Processing: Machine Vision Applications VII

KEYWORDS: Hyperspectral imaging, Data modeling, Cameras, Reflectivity, Image restoration, Pathogens, Image classification, Statistical modeling, Performance modeling, RGB color model

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PROCEEDINGS ARTICLE | May 29, 2013

Development of classification models to detect Salmonella Enteritidis and Salmonella Typhimurium found in poultry carcass rinses by visible-near infrared hyperspectral imaging

Proc. SPIE. 8721, Sensing for Agriculture and Food Quality and Safety V

KEYWORDS: Hyperspectral imaging, Infrared imaging, Principal component analysis, Reflectivity, Microorganisms, Image classification, Plating, Neodymium, Thermal modeling, Data analysis

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Salmonella is a major cause of foodborne disease outbreaks resulting from the consumption of contaminated food products in the United States. This paper reports the development of a hyperspectral imaging technique for detecting and differentiating two of the most common Salmonella serotypes, Salmonella Enteritidis (SE) and Salmonella Typhimurium (ST), from background microflora that are often found in poultry carcass rinse. Presumptive positive screening of colonies with a traditional direct plating method is a labor intensive and time consuming task. Thus, this paper is concerned with the detection of differences in spectral characteristics among the pure SE, ST, and background microflora grown on brilliant green sulfa (BGS) and xylose lysine tergitol 4 (XLT4) agar media with a spread plating technique. Visible near-infrared hyperspectral imaging, providing the spectral and spatial information unique to each microorganism, was utilized to differentiate SE and ST from the background microflora. A total of 10 classification models, including five machine learning algorithms, each without and with principal component analysis (PCA), were validated and compared to find the best model in classification accuracy. The five machine learning (classification) algorithms used in this study were Mahalanobis distance (MD), k-nearest neighbor (kNN), linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), and support vector machine (SVM). The average classification accuracy of all 10 models on a calibration (or training) set of the pure cultures on BGS agar plates was 98% (Kappa coefficient = 0.95) in determining the presence of SE and/or ST although it was difficult to differentiate between SE and ST. The average classification accuracy of all 10 models on a training set for ST detection on XLT4 agar was over 99% (Kappa coefficient = 0.99) although SE colonies on XLT4 agar were difficult to differentiate from background microflora. The average classification accuracy of all 10 models on a validation set of chicken carcass rinses spiked with SE or ST and incubated on BGS agar plates was 94.45% and 83.73%, without and with PCA for classification, respectively. The best performing classification model on the validation set was QDA without PCA by achieving the classification accuracy of 98.65% (Kappa coefficient=0.98). The overall best performing classification model regardless of using PCA was MD with the classification accuracy of 94.84% (Kappa coefficient=0.88) on the validation set.

PROCEEDINGS ARTICLE | May 16, 2012

Hyperspectral imaging for detection of non-O157 Shiga-toxin producing Escherichia coli (STEC) serogroups on spread plates of mixed cultures

Proc. SPIE. 8369, Sensing for Agriculture and Food Quality and Safety IV

KEYWORDS: Hyperspectral imaging, Data modeling, Imaging systems, Image segmentation, Reflectivity, Image analysis, Pathogens, Absorbance, Image classification, Statistical modeling

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We investigated the feasibility of visible and near-infrared (VNIR) hyperspectral imaging for rapid presumptive-positive screening of six representative non-O157 Shiga-toxin producing Escherichia coli (STEC) serogroups (O26, O45, O103, O111, O121, and O145) on spread plates of mixed cultures. Although the traditional culture method is still the "gold standard" for presumptive-positive pathogen screening, it is time-consuming, labor-intensive, not effective in testing large amount of food samples, and cannot completely prevent unwanted background microflora from growing together with target microorganisms on agar media. A previous study was performed using the data obtained from pure cultures individually inoculated on spot and/or spread plates in order to develop multivariate classification models differentiating each colony of the six non-O157 STEC serogroups and to optimize the models in terms of parameters. This study dealt with the validation of the trained and optimized models with a test set of new independent samples obtained from colonies on spread plates of mixed cultures. A new validation protocol appropriate to a hyperspectral imaging study for mixed cultures was developed. One imaging experiment with colonies obtained from two serial dilutions was performed. A total of six agar plates were prepared, where O45, O111 and O121 serogroups were inoculated into all six plates and each of O45, O103 and O145 serogroups was added into the mixture of the three common bacterial cultures. The number of colonies grown after 24-h incubation was 331 and the number of pixels associated with the grown colonies was 16,379. The best model found from this validation study was based on pre-processing with standard normal variate and detrending (SNVD), first derivative, spectral smoothing, and k-nearest neighbor classification (kNN, k=3) of scores in the principal component subspace spanned by 6 principal components. The independent testing results showed 95% overall detection accuracy at pixel level and 97% at colony level. The developed model was proven to be still valid even for the independent samples although the size of a test set was small and only one experiment was performed. This study was an important first step in validating and updating multivariate classification models for rapid screening of ground beef samples contaminated by non-O157 STEC pathogens using hyperspectral imaging.

PROCEEDINGS ARTICLE | May 5, 2012

Identification and characterization of Salmonella serotypes using DNA spectral characteristics by fourier transform infrared

Proc. SPIE. 8369, Sensing for Agriculture and Food Quality and Safety IV

KEYWORDS: Diamond, FT-IR spectroscopy, Proteins, Principal component analysis, Statistical analysis, Spectroscopy, Crystals, Macromolecules, Absorption, Bacteria

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PROCEEDINGS ARTICLE | May 5, 2012

Classification of Shiga toxin-producing escherichia coli (STEC) serotypes with hyperspectral microscope imagery

Proc. SPIE. 8369, Sensing for Agriculture and Food Quality and Safety IV

KEYWORDS: Hyperspectral imaging, Microscopes, Imaging systems, Scattering, Glasses, Crystals, Image acquisition, Pathogens, Image classification, Bacteria

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PROCEEDINGS ARTICLE | June 3, 2011

Improved egg crack detection algorithm for modified pressure imaging system

Proc. SPIE. 8027, Sensing for Agriculture and Food Quality and Safety III

KEYWORDS: Agriculture, Image compression, Detection and tracking algorithms, Imaging systems, Cameras, Image segmentation, Image processing, Control systems, Algorithm development, Motion estimation

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PROCEEDINGS ARTICLE | June 3, 2011

Rapid detection of salmonella using SERS with silver nano-substrate

Proc. SPIE. 8027, Sensing for Agriculture and Food Quality and Safety III

KEYWORDS: Data modeling, Calibration, Metals, Silver, Surface enhanced Raman spectroscopy, Raman spectroscopy, Pathogens, Biopolymers, Raman scattering, Bacteria

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PROCEEDINGS ARTICLE | June 3, 2011

AOTF hyperspectral microscopic imaging for foodborne pathogenic bacteria detection

Proc. SPIE. 8027, Sensing for Agriculture and Food Quality and Safety III

KEYWORDS: Hyperspectral imaging, Microscopes, Safety, Tumors, Imaging systems, Luminescence, Composites, Image acquisition, Image quality, Pathogens

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PROCEEDINGS ARTICLE | April 20, 2010

Line-scan hyperspectral imaging for real-time poultry fecal detection

Proc. SPIE. 7676, Sensing for Agriculture and Food Quality and Safety II

KEYWORDS: Hyperspectral imaging, Light emitting diodes, Safety, Imaging systems, Cameras, Image processing, Inspection, Image acquisition, Multispectral imaging, Line scan image sensors

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PROCEEDINGS ARTICLE | April 20, 2010

Development of real-time line-scan hyperspectral imaging system for online agricultural and food product inspection

Proc. SPIE. 7676, Sensing for Agriculture and Food Quality and Safety II

KEYWORDS: Hyperspectral imaging, Detection and tracking algorithms, Imaging systems, Cameras, Image processing, Reflectivity, Multispectral imaging, Algorithm development, Forward error correction, Line scan image sensors

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This paper reports a recent development of a line-scan hyperspectral imaging system for real-time multispectral imaging applications in agricultural and food industries. The hyperspectral imaging system consisted of a spectrograph, an EMCCD camera, and application software. The real-time multispectral imaging with the developed system was possible due to (1) data binning, especially a unique feature of the EMCCD sensor allowing the access to non-contiguous multispectral bands, (2) an image processing algorithm designed for real-time multispectral imaging, and (3) the design and implementation of the real-time application software. The imaging system was developed as a poultry inspection instrument determining the presence of surface feces on poultry carcasses moving at commercial poultry processing line speeds up to 180 birds per minute. The imaging system can be easily modifiable to solve other real-time inspection/sorting problems. Three wavelengths at 517 nm, 565 nm and 802 nm were selected for real-time fecal detection imaging. The fecal detection algorithm was based on dual band ratios of 565nm/517nm and 802nm/517nm followed by thresholding. The software architecture was based on a ping pong memory and a circular buffer for the multitasking of image grabbing and processing. The software was written in Microsoft Visual C++. An image-based internal triggering (i.e. polling) algorithm was developed to determine the start and end positions of birds. Twelve chickens were used for testing the imaging system at two different speeds (140 birds per minute and 180 bird per minute) in a pilot-scale processing line. Four types of fecal materials (duodenum, ceca, colon and ingesta) were used for the evaluation of the detection algorithm. The software grabbed and processed multispectral images of the dimension 118 (line scans) x 512 (height) x 3 (bands) pixels obtained from chicken carcasses moving at the speed up to 180 birds per minute (a frame rate 286 Hz). Intensity calibration, detection algorithm, displaying and saving were performed within the real-time deadlines.

PROCEEDINGS ARTICLE | April 15, 2008

Modified pressure system for imaging egg cracks

Proc. SPIE. 6983, Defense and Security 2008: Special Sessions on Food Safety, Visual Analytics, Resource Restricted Embedded and Sensor Networks, and 3D Imaging and Display

KEYWORDS: Optical filters, Light emitting diodes, Imaging systems, Cameras, Sensors, Image processing, Digital filtering, CCD cameras, Image filtering, Image enhancement

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PROCEEDINGS ARTICLE | October 12, 2007

Hyperspectral imaging for detecting pathogens grown on agar plates

Proc. SPIE. 6761, Optics for Natural Resources, Agriculture, and Foods II

KEYWORDS: Hyperspectral imaging, Imaging systems, Cameras, Calibration, Blood, Organisms, Lamps, Reflectivity, Pathogens, Algorithm development

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PROCEEDINGS ARTICLE | September 25, 2007

Bio-functional Au/Si nanorods for pathogen detection

Proc. SPIE. 6769, Nanosensing: Materials, Devices, and Systems III

KEYWORDS: Gold, Nanotechnology, Safety, Luminescence, Molecules, Silicon, Pathogens, Nanolithography, Bacteria, Nanorods

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PROCEEDINGS ARTICLE | February 17, 2007

Compact multispectral imaging system for contaminant detection on poultry carcass

Proc. SPIE. 6503, Machine Vision Applications in Industrial Inspection XV

KEYWORDS: Contamination, Detection and tracking algorithms, Imaging systems, Cameras, Sensors, Reflectivity, Imaging spectroscopy, Image registration, Multispectral imaging, Bandpass filters

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PROCEEDINGS ARTICLE | October 23, 2006

Egg embryo development detection with hyperspectral imaging

Proc. SPIE. 6381, Optics for Natural Resources, Agriculture, and Foods

KEYWORDS: Mathematical modeling, Hyperspectral imaging, Spectrographs, Data modeling, Imaging systems, Cameras, Blood, Detector development, Transmittance, Mahalanobis distance

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In the U. S. egg industry, anywhere from 130 million to over one billion infertile eggs are incubated each year. Some of these infertile eggs explode in the hatching cabinet and can potentially spread molds or bacteria to all the eggs in the cabinet. A method to detect the embryo development of incubated eggs was developed. Twelve brown-shell hatching eggs from two replicates (n=24) were incubated and imaged to identify embryo development. A hyperspectral imaging system was used to collect transmission images from 420 to 840 nm of brown-shell eggs positioned with the air cell vertical and normal to the camera lens. Raw transmission images from about 400 to 900 nm were collected for every egg on days 0, 1, 2, and 3 of incubation. A total of 96 images were collected and eggs were broken out on day 6 to determine fertility. After breakout, all eggs were found to be fertile. Therefore, this paper presents results for egg embryo development, not fertility. The original hyperspectral data and spectral means for each egg were both used to create embryo development models. With the hyperspectral data range reduced to about 500 to 700 nm, a minimum noise fraction transformation was used, along with a Mahalanobis Distance classification model, to predict development. Days 2 and 3 were all correctly classified (100%), while day 0 and day 1 were classified at 95.8% and 91.7%, respectively. Alternatively, the mean spectra from each egg were used to develop a partial least squares regression (PLSR) model. First, a PLSR model was developed with all eggs and all days. The data were multiplicative scatter corrected, spectrally smoothed, and the wavelength range was reduced to 539 - 770 nm. With a one-out cross validation, all eggs for all days were correctly classified (100%). Second, a PLSR model was developed with data from day 0 and day 3, and the model was validated with data from day 1 and 2. For day 1, 22 of 24 eggs were correctly classified (91.7%) and for day 2, all eggs were correctly classified (100%). Although the results are based on relatively small sample sizes, they are encouraging. However, larger sample sizes, from multiple flocks, will be needed to fully validate and verify these models. Additionally, future experiments must also include non-fertile eggs so the fertile / non-fertile effect can be determined.

PROCEEDINGS ARTICLE | October 23, 2006

Two-port multispectral imaging system for contaminant detection on poultry carcasses

Proc. SPIE. 6381, Optics for Natural Resources, Agriculture, and Foods

KEYWORDS: Contamination, Detection and tracking algorithms, Imaging systems, Cameras, Image registration, Multispectral imaging, Algorithm development, Forward error correction, Prototyping, Bandpass filters

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PROCEEDINGS ARTICLE | October 23, 2006

Bone fragment detection in chicken breast fillets using diffuse scattering patterns of back-illuminated structured light

Proc. SPIE. 6381, Optics for Natural Resources, Agriculture, and Foods

KEYWORDS: Breast, Imaging systems, Scattering, Cameras, Image processing, Light scattering, Reflectivity, Bone, Transmittance, X-ray imaging

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PROCEEDINGS ARTICLE | October 23, 2006

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

Proc. SPIE. 6381, Optics for Natural Resources, Agriculture, and Foods

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

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PROCEEDINGS ARTICLE | February 9, 2006

Real-time multispectral imaging application for poultry safety inspection

Proc. SPIE. 6070, Machine Vision Applications in Industrial Inspection XIV

KEYWORDS: Imaging systems, Cameras, Sensors, Calibration, Image processing, Inspection, Reflectivity, Multispectral imaging, Image sensors, Real time image processing

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PROCEEDINGS ARTICLE | September 15, 2005

Thresholding for biological material detection in real-time multispectral imaging

Proc. SPIE. 5913, Signal and Data Processing of Small Targets 2005

KEYWORDS: Target detection, Hyperspectral imaging, Magnesium, Contamination, Statistical analysis, Detection and tracking algorithms, Imaging systems, Remote sensing, Multispectral imaging, Algorithm development

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PROCEEDINGS ARTICLE | November 19, 2004

Improved imaging system for fecal detection

Proc. SPIE. 5587, Nondestructive Sensing for Food Safety, Quality, and Natural Resources

KEYWORDS: Hyperspectral imaging, Spectrographs, Contamination, Agriculture, Imaging systems, Cameras, Image processing, Reflectivity, Light sources and illumination, Colon

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PROCEEDINGS ARTICLE | November 19, 2004

Real-time image processing for rapid contaminant detection on broiler carcasses

Proc. SPIE. 5587, Nondestructive Sensing for Food Safety, Quality, and Natural Resources

KEYWORDS: Optical filters, Prisms, Imaging systems, Cameras, Sensors, Calibration, Image processing, Reflectivity, Multispectral imaging, Real time image processing

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PROCEEDINGS ARTICLE | March 30, 2004

Classification of hyperspectral imagery for identifying fecal and ingesta contaminants

Proc. SPIE. 5271, Monitoring Food Safety, Agriculture, and Plant Health

KEYWORDS: Hyperspectral imaging, Safety, Imaging systems, Skin, Inspection, Image classification, Colon, Forward error correction, Binary data, Mahalanobis distance

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PROCEEDINGS ARTICLE | March 30, 2004

Comparison between visible/NIR spectroscopy and hyperspectral imaging for detecting surface contaminants on poultry carcasses

Proc. SPIE. 5271, Monitoring Food Safety, Agriculture, and Plant Health

KEYWORDS: Breast, Hyperspectral imaging, Agriculture, Data modeling, Imaging systems, Spectroscopy, Skin, Algorithm development, Forward error correction, Absorption

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The U. S. Department of Agriculture, Agricultural Research Service has been developing a method and system to detect fecal contamination on processed poultry carcasses with hyperspectral and multispectral imaging systems. The patented method utilizes a three step approach to contaminant detection. Spectra of homogenous samples of feces, ingesta (undigested food particles), and skin were first collected. Then those spectra were evaluated with multivariate analysis techniques to identify significant wavelength regions for further analysis. Hyperspectral data were then collected on contaminated poultry carcasses and information learned from the spectroscopic data was used to aide in hyperspectral data analysis. Finally, the results of the hyperspectral data were used to identify a few optimum wavelengths for use in a real-time multispectral imaging system. In this work, two techniques for developing spectral datasets and algorithms for classifying surface contaminants on poultry carcasses were explored. The first consisted of a scanning monochrometer that measured the average spectra of uncontaminated breast skin and fecal and ingesta contaminants. The second technique used regions of interest (ROI) from a hyperspectral image to collect spatially averaged spectra. Comparison of the spectra from each instrument showed variations in the spectra collected from similar samples. There was an offset of absorption values between the two instruments and the hyperspectral imaging system had better resolution at higher absorption wavelengths. Although both systems were calibrated prior to measuring, there was also a slight shift in absorption peaks between the two systems. Both techniques were able to classify contaminated skin from uncontaminated skin in a full cross-validated test set with better than 99% accuracy. However, when the classification model developed from the monochrometer spectra was applied to whole-carcass hyperspectral images, numerous common carcass features, such as exposed meat and wing-shadowed skin, were wrongly identified as false positives. Since spectra of entire poultry carcasses were available in the original hyperspectral dataset, the hyperspectral ROI technique allowed researchers to easily add the spectra of these false positives to the calibration dataset. New partial least squares regression models with meat and skin shadow spectra resulted in different principal component loadings and improved classification models. The classification model with the combined ROI spectra from skin, feces, ingesta, meat, and skin shadows gave a classification accuracy of 99.5%. When this model was compared to the original model developed from the monochrometer dataset on a few hyperspectral images of contaminated carcasses, fewer false positives were classified with the hyperspectral ROI model without sacrificing the accuracy of contaminant detection. Further research must be done to fully characterize the accuracy of the model.

PROCEEDINGS ARTICLE | March 17, 2003

Reflectance calibration of focal plane array hyperspectral imaging system for agricultural and food safety applications

Proc. SPIE. 4879, Remote Sensing for Agriculture, Ecosystems, and Hydrology IV

KEYWORDS: Staring arrays, Hyperspectral imaging, Spectrographs, Safety, Agriculture, Imaging systems, Sensors, Calibration, Lamps, Reflectivity

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PROCEEDINGS ARTICLE | March 17, 2003

Assessment of hyperspectral imaging system for poultry safety inspection

Proc. SPIE. 4879, Remote Sensing for Agriculture, Ecosystems, and Hydrology IV

KEYWORDS: Hyperspectral imaging, Safety, Imaging systems, Calibration, Image processing, Skin, Reflectivity, Imaging spectroscopy, Spectral calibration, Forward error correction

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PROCEEDINGS ARTICLE | November 8, 2002

Analysis of reflectance spectra from hyperspectral images of poultry carcasses for fecal and ingesta detection

Proc. SPIE. 4816, Imaging Spectrometry VIII

KEYWORDS: Breast, Hyperspectral imaging, Contamination, Imaging systems, Calibration, Skin, Reflectivity, Absorbance, Colon, Forward error correction

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PROCEEDINGS ARTICLE | November 8, 2002

Hyperspectral imaging for food processing automation

Proc. SPIE. 4816, Imaging Spectrometry VIII

KEYWORDS: Hyperspectral imaging, Optical filters, Prisms, Contamination, Imaging systems, Cameras, Image processing, Multispectral imaging, Image filtering, Forward error correction

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PROCEEDINGS ARTICLE | January 12, 1999

Multispectral imaging for detecting contamination in poultry carcasses

Proc. SPIE. 3544, Pathogen Detection and Remediation for Safe Eating

KEYWORDS: Contamination, Data modeling, Imaging systems, Image segmentation, Matrices, Image processing, Inspection, Image analysis, Multispectral imaging, Image classification

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PROCEEDINGS ARTICLE | January 12, 1999

Color image classification systems for poultry viscera inspection

Proc. SPIE. 3544, Pathogen Detection and Remediation for Safe Eating

KEYWORDS: Liver, Image segmentation, Image processing, Composites, Inspection, Heart, Image classification, Fuzzy logic, Classification systems, RGB color model

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PROCEEDINGS ARTICLE | August 21, 1996

Instrumental system for online inspection of poultry carcasses

Proc. SPIE. 2786, Vision Systems: Applications

KEYWORDS: Optical filters, Imaging systems, Cameras, Calibration, Skin, Lamps, Inspection, Computing systems, Imaging spectroscopy, Spectrophotometry

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PROCEEDINGS ARTICLE | February 21, 1996

Hybrid image processing for robust extraction of lean tissue on beef cut surface

Proc. SPIE. 2665, Machine Vision Applications in Industrial Inspection IV

KEYWORDS: Tissues, Image segmentation, Image processing, Image restoration, Bone, Neural networks, Image enhancement, Image contrast enhancement, Natural surfaces, Binary data

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PROCEEDINGS ARTICLE | January 6, 1995

Integration of visible/NIR spectroscopy and multispectral imaging for poultry carcass inspection

Proc. SPIE. 2345, Optics in Agriculture, Forestry, and Biological Processing

KEYWORDS: Imaging systems, Image segmentation, Spectroscopy, Inspection, Reflectivity, Imaging spectroscopy, Multispectral imaging, Image filtering, Neural networks, System integration

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PROCEEDINGS ARTICLE | May 12, 1993

Ultrasonic image analysis for beef tenderness

Proc. SPIE. 1836, Optics in Agriculture and Forestry

KEYWORDS: Statistical analysis, Forestry, Agriculture, Image segmentation, Matrices, Image analysis, Collagen, Ultrasonics, Image enhancement, Connective tissue

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