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The Media Lab at the City College of
New York (CCNYM) was founded in 2008, and Dr. YingLi Tian was appointed as
the lab director. Our research group, CCNYM is dedicated to both
fundamental and applied research in the areas of computer vision,
image/video processing, multimedia, multi-sensor fusion, human-computer
interaction, and remote sensing. Our research topics include:
Wearable Audio Video Eye System
(WAVES): Based on the 2002 world
population, there are more than 161 million visually impaired people in the
world today, of which 37 million are blind. The goal of WAVES project is to
explore and develop computer vision-based assistive technologies for
visually impaired persons to understand the surrounding environment and to
form mental representations of that environment by using wearable sensors.
Our research
efforts focus on two threads:
computer
vision-based technology for scene understanding including context
information extraction and recognition, stationary objects detection and
recognition, moving object detection and recognition, and dynamic environment
change adaption; and 2) user
interface and usability studies including auditory display and
spatial updating of object location, orientation, and distance, and
environment changes.
Automatic Target Detection and Recognition by
Hyperspectral Imagery: Automatic target detection, tracking, and recognition in
multispectral/hyperspectral imagery is a challenging problem and involves
several technologies such as optical sensor design, signal/image
processing, pattern recognition, and computer vision algorithms. There are
many applications of civil and military applications include surveillance
of ground, ocean, air and space. In this project, we will also investigate
how to extract reliable information from various sensing situations and
design cost-effective sensors.
Intelligent Video Activity Analysis: There are large amount
data of events and activities for intelligent video surveillance. The
research will exploit the composite event detection, association mining, pattern discovery and
unusual pattern detection by using data mining.
Moving Object Detection and Tracking in Challenge
Environments:
There are many research about moving object detection and tracking.
It is hard to achieve satisfied results in challenge environments such as
in crowed, with lighting changes, or in bad weather. Our research will
focus on propose more robust and efficient algorithms for video understanding.
Facial Expression Analysis in Naturalistic Environments: The research of facial
expression analysis in naturalistic environments will have significant
impact across a range of theoretical and applied topics. Real-life facial
expression analysis must handle head motion (both in-plane and
out-of-plane), occlusion, lighting change, low intensity expressions, low
resolution input images, absence of a neutral face for comparison, and
facial actions due to speech.
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