Intelligent Information Media Lab
Established in October, 2016.
Research overview
Our research themes conver a wide range of basic technologies and
applications for intelligent information-media systems that support
people in daily life.
As the basic technologies, computer vision, image understanding, and
other multi-media sensing and recognition techniques are widely
studied.
In addition, machine learning including deep neural networks for the
big multi-media analysis is also our focus.
These basic techniques are employed for various real-world systems for
human sensing, human modeling, and human support.
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Image enhancement such as Super resolution:
Images taken with a camera include various defects such as distant objects
appearing small (difficult to recognize), blurring when the camera or subject
moves, and objects that you do not want to see. These deficiencies interfere
both when a person sees an image and when a machine sees the image and
recognizes something. We study image enhancement technology to solve these
problems.
Online demo of Single-image Super-resolution

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Human activity recognition with big data learning:
For human pose, motion, and activity recognition, we utilize not only training data
given by manual annotations but also unsupervised data (e.g., images,
videos, and texts in the Internet) in
order to improve the performance.

-
Human group and crowd analysis:
Activities of human group and crowd are analyzed for recognizing highly-contextual events in the real world.

-
Human motion sensing and estimation:
Natural motions of a human body are measured
by optical sensors (e.g., cameras). These
accurately-measured motions are also modeled by machine learning in order to explore
individual characteristics and skills. Human
motions are also observed by cameras and
estimated in observed images and
videos. These estimated motions are useful
for monitoring, HCI, HRI, and other applications.

-
Human-like Motion Generation and Prediction:
Human and robot motions are generated and predicted for a variety of applications such as human motion animation, human trajectory prediction, and robot motion planning.

-
Video recognition for inside and outside vehicles:
For smart vehicle control, we develop activity and condition recognition methods for
passengers and pedestrians.

For more detailed information, see
here.
Image enhancement such as Super resolution:
Images taken with a camera include various defects such as distant objects
appearing small (difficult to recognize), blurring when the camera or subject
moves, and objects that you do not want to see. These deficiencies interfere
both when a person sees an image and when a machine sees the image and
recognizes something. We study image enhancement technology to solve these
problems.
Online demo of Single-image Super-resolution
Human activity recognition with big data learning:
For human pose, motion, and activity recognition, we utilize not only training data
given by manual annotations but also unsupervised data (e.g., images,
videos, and texts in the Internet) in
order to improve the performance.
Human group and crowd analysis:
Activities of human group and crowd are analyzed for recognizing highly-contextual events in the real world.
Human motion sensing and 3D animation synthesis:
Natural motions of a human body are measured by optical sensors (i.e.,
cameras). These motion are modeled by machine learning in order to explore
individual characteristics and skills. The model is applied to 3D human
animation synthesis.
Video recognition for inside and outside vehicles:
For smart vehicle control, we develop activity and condition recognition methods for
passengers and pedestrians.
Multi-media modeling and its applications to intelligent robotics and
Quality of Life technologies:
Various physiological data and physical body data are mutually analyzed.
For example, we are interested in temporal smooth motions in human
gait patterns and human cognitive and physical changes for a long term.

For more detailed information, see
here.
Intelligent Information Lab, Toyota Technological Institute (TTI-J)
Norimichi Ukita
ukita at toyota-ti.ac.jp
phone: 052-809-1832
2-12-1 Hisakata
Tempaku
Nagoya
468-8511 Japan
Office:
C3-31
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