Keynote
Speakers at PSIVT 2011
|
Prof. Masayuki
Tanimoto (Nagoya
University) Title:
Free-viewpoint
TV (FTV) Date:
Monday, November 21, 09:00~10:00 |
Abstract:
Television realized a
human dream of seeing a distant world without actually going there. However, it
allows the users to see only a single view of a 3D scene they want to see.
Furthermore, the viewpoint can’t be changed by the users. Stereoscopic 3DTV also
has the same limitation although it transmits 2 views for right and left eyes
and gives us 3D sensation. Free-viewpoint Television (FTV) is an innovative
visual media that breaks down this limitation of the current visual media and
allows the users to see a 3D scene by freely changing the viewpoint. FTV is the
ultimate 3DTV and ranked as the top of visual media since it has infinite
number of viewpoints.
We proposed the concept of FTV and
constructed the world’s first real-time FTV system including the complete chain
of operation from image capture to display. FTV captures rays sparsely by using
multi-cameras set discretely in the 3D space and generates the remaining rays. For this purpose, we have developed new video
technologies such as technology that treats many cameras as if they are a
single camera, ray integration and interpolation technologies and so on. We
have also developed an all-around dense ray capture method and an efficient ray
capture method that captures rays efficiently with reduced number of pixel
data. At present, FTV is available on a single PC or a mobile player.
All-around ray-reproducing 3DTV and FTV with free listening-point audio are
also realized.
We proposed FTV
to MPEG in 2001. MPEG
regarded FTV as the most challenging 3D media and started the international
standardization activities of FTV in 2004.
The first phase of FTV was
Multi-view Video Coding (MVC) and the second phase of FTV is 3D Video (3DV). MVC enables the
efficient coding of multiple camera views and was completed in 2009. MVC has been
adopted by Blu-ray 3D. 3DV
is a standard that targets serving a variety of 3D displays. “Call for
Proposals on 3D Video Coding Technology” was issued in March 2011.
Biography:
Masayuki Tanimoto received his B.E., M.E., and Dr.E.
degrees in Electronic Engineering from the University
of Tokyo in 1970, 1972, and 1976, respectively. He joined Nagoya University in
1976. Since 1991, he has been a Professor at Graduate School of Engineering,
Nagoya University. He has been engaged in the research of image coding, image
processing, 3D imaging, FTV and ITS.
He was the president of
the Institute of Image Information and Television Engineers (ITE), and a fellow
of the Institute of Electronics, Information, and Communication Engineers
(IEICE) and ITE. He received the ITE Distinguished Achievement and
Contributions Award, the IEICE Achievement Award, and the Commendation for
Science and Technology by the Minister of Education, Culture, Sports, Science,
and Technology.
|
Prof. InSo Kweon (KAIST) Title:
Robust 3-D Vision Techniques - Algorithms and Applications Date:
Tuesday, November 22, 09:00~10:00 |
Abstract:
For the
3D reconstruction of static and dynamic scenes, we have developed several
robust vision methods ranging from low-level edge detection to the design of
novel camera systems.
In this
talk, we first present a physics based edge detection
and a dense stereo matching method based on the characteristics of the human
vision system. Specifically, a novel image noise model based on the Skellam distribution is very effective to the edge
detection problem.
The
second part of the talk introduces new camera systems to capture the 3D
information accurately and efficiently. The camera systems include (1) a
bi-prism stereo camera, (2) “camera + depth” fusion camera systems, (3) a fast
bundle adjustment based approach for large-scale datasets, (4)
a novel coded light for dynamic scene. Specifically, we present a
hand-held fusion sensor system, consisting of four cameras and two 2D laser
scanners, to capture 3D information of large-scale scenes. This new approach
allows boosting the advantages of two sensor systems and complements the
weakness of the two.
As
an important application of 3D vision techniques, we demonstrate the robustness
of the methods by automatically synthesizing high-quality novel stereoscopic
views from video and/or 3D information.
Biography:
Prof. InSo Kweon
received his B.S. and M.S. degrees in Mechanical Design and Production
Engineering from Seoul National University, Seoul, Korea, in 1981 and 1983,
respectively, and the M.S. and Ph.D. degree in Robotics from the Robotics
Institute at Carnegie Mellon University, Pittsburgh, U.S.A, in 1986 and 1990, respectively.
He worked for Toshiba R&D Center, Japan, and joined the Department of
Automation and Design Engineering at KAIST in 1992. He is now a Professor in
the Department of Electrical Engineering at KAIST. His research interests are in computer
vision, robotics, pattern recognition, and automation. Specific research topics
include invariant based visions for recognition and assembly, 3D sensors and
range data analysis, color modeling and analysis, robust edge detection, and
moving object segmentation and tracking. He is a member of ICASE, IEEE, and
ACM.
|
Prof. Heung-No
Lee (GIST) Title:
Overview of Compressed Sensing Theory Date:
Wednesday, November 23, 09:00~10:00 |
Abstract:
In this presentation, we aim to provide an overview of the
mathematical theory of Compressed Sensing (CS), an emerging field in Signal
Processing and Information Theory. CS is deemed to have provided a
new signal acquisition framework with which the samples of a given signal of
interest can be taken while being compressed simultaneously and the given
signal can be regenerated perfectly with much smaller number of measured
samples than what would be needed in the classical sampling approach. Thanks to
this new capability, the idea of CS has been applied and shown successful in
quite a number of engineering problems, where sample taking is expensive or
time consuming, and where the number of sensors is limited perhaps due to space
limitation, including but not limited to wideband radars, ultra wideband
spectrum sensing, functional MRIs, and Terahertz imaging. As such,
understanding the mathematics of the CS theory becomes quite important and
interesting. In CS, sample taking is done via linear projection of a given
signal against a prescribed set of kernels, i.e., one linearly projected sample
per kernel; the canonical approach to recover the original signal from the
projected samples in CS is to use the so-called Basis Pursuit algorithm, a
linear programming based L1 minimization algorithm. Today, we have many kernels
and recovery algorithms available. It is not difficult to see that the
performance of a CS system depends heavily on the choice of the kernel and the
recovery algorithm. Our overview will include discussion of kernels and
recovery algorithms, and certain theoretical tools with which one can determine
the number of measurements needed for perfect recovery given a kernel.
Biography:
Prof. Heung-No Lee graduated from UCLA obtaining his Ph.D. degree in
Electrical Engineering in 1999. He received B.S. and M.S. degrees in Electrical
Engineering at UCLA as well, in 1993 and 1994, respectively. He then moved to
HRL Laboratory, Malibu, California, and worked there as a Research Staff Member
from 1999 to 2002. He was then appointed as Assistant Professor at the
University of Pittsburgh, Pittsburgh, Pennsylvania, in 2002 where he stayed
till 2008. He founded Communications Research Lab in the Electrical and
Computer Engineering Department. He obtained funding from various sources,
including National Science Foundation, the Technology Collaborative, the US
Army, and private companies. Three Ph.D. students and four M.S. students have
graduated from his research programs at the University of Pittsburgh. He then
moved to Gwangju Institute of Science and Technology (GIST) in Jan. 2009. The
general areas of his research lie in Signal Processing Theory, Communications,
and Information Theory, and their application to Wireless Communications and
Networking, Compressive Sensing, Future Internet, and Brain Computer Interface.