Email :
Tel : +82-42-350-3494

Professor Yong Man Ro received Ph.D. degree from the department of electrical engineering at KAIST. He was a researcher at Columbia University, a visiting researcher at the University of California, Irvine, and a research fellow at the department of electrical engineering and computer Sciences in the University of California, Berkeley. He was a visiting professor in The Edward S. Rogers Sr. department of electrical and computer engineering at the University of Toronto. He is currently a full professor and the chair of signals and systems group of the department of electrical engineering in KAIST.
Prof. Ro is a senior member of IEEE, a member of ISMRM and SPIE. He received the young investigator finalist award of ISMRM in 1992 and the year scientist award (Korea) in 2003. He served as an associate editor for IEEE signal processing letters. He serves as an associate editor in transitions on data hiding and multimedia security (Springer-Verlag). He served as a TPC in many international conferences including the program chair of PCM2015 and IWDW 2004. He organized many special sessions including "Digital Photo Album Technology" in AIRS 2005, "Social Media" in DSP 2009 and "Human 3D Perception and 3D Video Assessments" in DSP 2011.

Lab Introduction  

Image and video systems (IVY) Lab at KAIST, was founded in 1997 and has been led by Prof. Ro since its establishment. Among the years IVY Lab has been conducting research in a wide spectrum of image and video systems research topics. Among those topics; image processing, computer vision, visual recognition, deep learning and machine learning, medical image processing and video representation/compression. IVY Lab has produced about 110 journal papers and 250 conference papers over the last years. The collaborative lab environment and the enthusiasm of its members have made it be in touch with the latest developments of standards and industry. For example the lab has developed the homogeneous texture descriptor for the MPEG standard, ROI descriptor in SVC and various description schemes in user characteristics as a part of the MPEG standard. In addition, in recent years, the lab has accomplished several outstanding research achievements: Deep learning based visual recognition, Face recognition and face expression recognition, Color face recognition for degraded images, Visual discomfort prediction and reduction of stereoscopic 3D contents, Semantic concept based Near-duplicated video clip detection, and Computer-aided detection (CAD) system for digital mammogram.
The lab is continuously working hand to hand with industry to be able to innovate and challenge the state of the art in multiple aspects of the image and video systems. Currently the lab is interested in the following research topics:
- Deep learning and machine learning on Image processing and computer vision
- High-performance face recognition, Emotion recognition
- Automatic object and action detection/recognition.
- Medical image processing and Computer Aided Diagnostic (CAD) systems.
- 3D rendering/processing, S3D quality measurement.
- Video signature/video analysis
- Large scale image/video retrieval.

More details can be found at the following link: