Fusion of Visible and Thermal Infrared
Images for Robust Face Recognition
Selected Publications:
■ C. Ryu, S. G. Kong,
and H. Kim, “Enhancement of Feature Extraction
for Low-Quality Fingerprint Images using Stochastic Resonance,” Pattern Recognition Letters, Vol. 32,
Issue 2, pp.107-113, January 2011.
■ I. S. Kim, H. S.
Choi, K. M. Yi, J. Y. Choi, and S. G. Kong, “Intelligent Visual Surveillance – A Survey,”
International Journal of Control,
Automation, and Systems, Vol. 8, No. 5, pp.926-939, October 2010.
■ S. G. Kong, J. Heo, F.
Boughorbel, Y. Zheng, B. R.
Abidi, A. Koschan, M. Yi,
and M. A. Abidi, “Multi-scale
Fusion of Visible and Thermal
IR Images for Illumination-Invariant Face Recognition,” International
Journal of Computer Vision, Vol. 71, No.
2, pp.215-233, Feb. 2007.
■ S.
Moon and S. G. Kong, “Adaptive Fusion of Visible and Thermal Images based on
Multiscale Analysis for Face Recognition,” Proc. IEEE Int’l Conf. on
Computational Intelligence for Homeland Security and Personal Safety
(CIHSPS’06), Alexandria, VA, Oct. 2006.
■ S.
G. Kong, J. Heo, B. R. Abidi, J. Paik, and M. A. Abidi, “Recent Advances in Visual and Infrared Face Recognition - A
Review,” Computer
Vision and Image Understanding, Vol. 97, No. 1, pp.103-135, January 2005. (Most Cited Paper Award)
■ J.
Heo, S. G. Kong, B. Abidi, and M. Abidi, “Fusion of Visual and Thermal
Signatures with Eyeglass Removal for Robust Face Recognition,”
Proc. Workshop on Object Tracking and Classification Beyond the Visible
Spectrum (OTCBVS’04), Washington, DC, July 2004. (Best Paper Award)
UP
Signal Restoration in Terahertz
Imaging and Spectroscopy
The part of the electromagnetic
spectrum (0.1?0 THz) between microwaves and far
infrared refers to Terahertz radiation. Terahertz beams easily penetrate
dielectric substances such as paper, plastics and ceramics, which are opaque at
optical frequencies and provide very low contrast for X-rays. These materials
are relatively non-absorbing in this frequency range, yet different materials
may be easily discriminated on the basis of their refractive index, which is
extracted from the THz phase information. Unlike X-rays, Terahertz beams can be
focused and are capable of producing phase-sensitive spectroscopic images with
signature capability. The spectral information of the transmitted and/or
scattered radiation could be used to detect hazardous chemicals, threat objects
in luggage or concealed weapons, and defects in semiconductor wafers and
packages. Low photon energy level of Terahertz spectrum is particularly
attractive for the imaging of biological tissues with no harmful ionizing
radiation. THz source can be used for high-resolution remote subsurface
imaging, with spatial and depth resolution better than 1 mm, enabling
differentiation of skin or breast cancers from normal tissues and tooth
cavities. This research covers enhancement of THz waveforms and denoising, spectral signal classification, high-resolution
image reconstruction based on the fusion of peak intensity and phase
information, and segmentation techniques for the visualization and
classification of terahertz images.
Selected Publications:
■
C. Ryu and S. G. Kong, “Boosting Terahertz Radiation in THz-TDS using
Continuous-Wave Laser,” Electronics Letters, Vol.
46, No. 5, pp.359-360, March 4, 2010.
■
C. Ryu and S. G. Kong, “Atmospheric Degradation Correction of Terahertz Beams
using Multiscale Signal Restoration,” Applied
Optics, Vol. 49, No. 5, pp.927-935, February 2010.
■
S. G. Kong and D. H. Wu, "Signal
Restoration from Atmospheric Degradation in Terahertz Spectroscopy," Journal of Applied Physics, Vol. 103, No. 11, 113105 (6 pages),
June 2008.
■
S. G. Kong and D. H. Wu, “Terahertz
Time-Domain Spectroscopy for Explosive Trace Detection,” Proc. IEEE Int’l Conf. on Computational Intelligence for Homeland
Security and Personal Safety (CIHSPS’06), Alexandria, VA, Oct. 2006.
UP
Hyperspectral Image Analysis for Biomedical Diagnostics and Target
Detection
This research aims at developing hyperspectral imaging and signature classification
techniques for real-time, non-invasive diagnosis of skin cancers. The application
focus involves the study and correlation of reflectance/fluorescence image
signals with neoplastic properties of normal and tumor tissues. A technical
innovation of this project is the combination of an advanced computing
algorithm based on the support vector machine with a real-time hyperspectral imaging system for detecting small
differences in reflectance and fluorescence profiles of normal and malignant
tumor tissues. This approach leads to significant advances in effective and
rapid detection of tumors over large areas of organs and in the understanding
of cancer in general. The developed skin cancer detection technique will be
translatable to other diseases in other organ sites as well changing the future
of diagnostic medicine. Early phase of this research has been successfully
applied to the detection of skin tumors on poultry carcasses for food safety
inspection.
Selected Publications:
■
Z.
Du, Y. Jeong, M. K. Jeong,
and S. G. Kong, “Multidimensional Local Spatial Autocorrelation Measure
for Integrating Spatial and Spectral Information in Hyperspectral
Image Band Selection,” in print, Applied Intelligence, 2011
■
Y. Zhao, L. Zhang, and S. G. Kong, “Band Subset Based Clustering and Fusion for Hyperspectral
Imagery Classification,” IEEE
Transactions on Geoscience and Remote Sensing, Vol. 49, No. 2, pp.747-756, February
2011.
■
S. G. Kong and
L. J. Park, "Hyperspectral Image Analysis for Skin Tumor Detection," Applied
Perception in Thermal Infrared Imagery, R. I. Hammoud
(Ed.), Springer: London, 2008.
■
Z. Du, M. K. Jeong, and S. G. Kong, “Band
Selection of Hyperspectral Images for Automatic Detection of Poultry Skin
Tumors,” IEEE Transactions on Automation
Science and Engineering, Vol. 4, No. 3, pp.332-339, 2007.
■
S. G. Kong, M. Martin, and T. Vo-Dinh, “Hyperspectral
Fluorescence Imaging for Mouse Skin Tumor Detection,” ETRI Journal, Vol. 28, No. 6, pp.770-776, December 2006.
■
S. G. Kong, Z. Du, M. Martin, and T.
Vo-Dinh, “Hyperspectral
Fluorescence Image Analysis for Use in Medical Analysis,” Proc. of SPIE Conf. on Biomedical Optics, San Jose, CA, 2005.
■
I. Kim, Y. R. Chen, M. S. Kim, and S.
G. Kong, “Detection
of Skin Tumors on Chicken Carcasses using Hyperspectral Fluorescence Imaging,” Transactions of the American Society of Agricultural Engineers,
Vol. 47, No. 5, pp.1785-1792, 2004. (ASABE Honorable Mention
Paper Award)
■
S. G. Kong, Y. R. Chen, I. Kim, and
M. S. Kim, “Analysis
of Hyperspectral Fluorescence Images for Poultry Skin Tumor Inspection,” Applied Optics, Vol. 43, No. 4, pp.824-833, February 2004.
UP
Multisensor Image Registration and Fusion
The goal of this research is to
develop a multisensor image registration technique based
on the combination of mutual information computed from intensity and gradient
magnitude of an image pair. Image registration refers to the process of
aligning multiple images of the same scene taken by different imaging sensors,
at different times, and/or from different viewpoint. Multiple images obtained
from different types of imaging sensors (e.g. anatomical (CT) and functional
(PET) medical images or radar and optical target images) may contain
complementary information on the object of interest. Multisensor
image data must be integrated to reveal the information useful for medical
diagnosis or target identification. Maximizing the mutual information has been
widely used as an effective method in various image registration applications.
However, individual mutual information using either image intensity or gradient
magnitude often demonstrates limitations in the registration of multisensor images. The image registration technique
proposed in this effort synergistically combines the mutual information of
intensity and gradient magnitude as robust measure of multisensor
image registration.
Selected Publications:
■
O. Kwon and S. G. Kong, “Multimodality Image Registration using Combined
Mutual Information of Intensity and Gradient,” World Congress on Medical Physics and Biomedical Engineering,
Seoul, Aug. 2006.
UP
Evolvable Block-based Neural
Networks for ECG Signal Monitoring
This work aims at developing
evolvable neural networks that reconfigure their structures and connection
weights autonomously in dynamic operating environments. The block-based neural
network model consist of a 2-D array of basic blocks that can modify network
structure and connection weights using evolutionary algorithms to be
implemented on reconfigurable digital hardware. Block-based neural networks
demonstrated the potential for analyzing electrocardiogram (ECG) signals to
monitor human health conditions online that are insensitive to variations over
individuals, time of day, and under different body conditions. People working
in dangerous environments (e.g. military personnel, firemen, and truck drivers)
as well as older people will benefit from constant monitoring of their health
conditions for prediction of various dangerous states such as detection of
losing consciousness and heart infarct.
Selected Publications:
■
S. W. Moon and S. G. Kong, “Block-based Neural Networks,” IEEE Transactions on Neural
Networks, Vol. 12, No. 2, pp.307-317, March 2001.
■
W. Jiang, S.
G. Kong, and G. Peterson, “ECG Signal Classification using
Block-based Neural Networks,” Proc.
International Joint Conf. on Neural Networks, Montreal, Canada, 2005.
■
S. Merchant, G. D. Peterson, S. Park, and S. G. Kong,
“FPGA Implementation of Evolvable Block-based Neural Networks,” Proc. Congress on Evolutionary Computation,
Vancouver, July 2006.
■
W. Jiang and S. G. Kong, “A Least-Squares Learning for
Block-based Neural Networks,” Dynamics of Continuous, Discrete and
Impulsive Systems, Vol. 14, No. S1, pp.242-247, 2007.
■
W. Jiang and
S. G. Kong, “Block-based Neural Networks for Personalized ECG Signal
Classification,” IEEE
Transactions on Neural Networks, Vol. 18, No. 6, pp.1750-1761, November
2007.
UP
