Monday, Wednesday, Friday 10:00 - 11:00
Dr. Seong Kong is an Associate Professor and Graduate Program Coordinator in the Department of Electrical and Computer Engineering at Temple University, and Director of the Imaging and Pattern Recognition Laboratory. Dr. Kong is internationally recognized for his work on image processing beyond the visible spectrum. He received the Most Cited Paper Awards from the journal Computer Vision and Image Understanding in 2007 and 2008. He published over 100 journal and conference papers in the fields of image processing and pattern recognition.
- PhD. Electrical Engineering, University of Southern California, Los Angeles, CA
- M.S. Electrical Engineering, Seoul National University, Korea
- B.S. Electrical Engineering, Seoul National University, Korea
- Associate Professor, Electrical and Computer Engineering Department, Temple University, Philadelphia, PA
- Associate Professor, Electrical and Computer Engineering Department, University of Tennessee, Knoxville, TN
- Visiting Professor, School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN
- R. Oyini Mbouna, S. G. Kong, and M. G. Chun, “Visual Analysis of Eye State and Head Pose for Driver Alertness Monitoring,” in print, IEEE Transactions on Intelligent Transportation Systems, 2013.
- Y. Zhao and S. G. Kong, “Automated Classification of Touching or Overlapping M-FISH Chromosomes by Region Fusion and Homolog Pairing,” Pattern Analysis and Applications, Vol. 16, Issue 1, pp.31-39, February 2013.
- J. Lee, S. G. Kong, Y. Lee, J. Kim, and N. Jung, “Detection of Transcribed Seal Impressions using 3-D Pressure Traces,” Journal of Forensic Sciences, Vol. 57, No. 6, pp.1531-1536, 2012.
- R. Oyini Mbouna and S. G. Kong, “Pupil Center Detection with a Single Webcam for Gaze Tracking,” Journal of Measurement Science and Instrumentation, Vol. 3, No. 2, pp.133-136, June 2012.
- 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,” Applied Intelligence, Vol. 36, No. 3, pp.542-552, 2012.
- Image Processing
- Pattern Recognition