Event Location: NSH 1507
Bio: Hongwen Henry Kang is currently a PhD student in the Robotics Institute
of Carnegie Mellon, co-advised by Takeo Kanade and Martial Hebert, he
also works closely with Alexei A. Efros. His research interests are in
the intersection of Computer Vision, Machine Learning, Data Mining and
Computer Graphics, with specific interest in large image database and
high dimensional features.

Abstract: Indoor environment poses substantial challenges for Computer Vision
algorithms, due to the combined patterns that are either highly
repetitive (e.g. doors), textureless (e.g. white walls), or temporally
changing (e.g. posters, pedestrians). The fundamental challenge we want
to tackle is the robust image matching. We proposed two approaches to
address this problem, one is an iterative algorithm that combines
global/local weighting strategies under bag-of-features model, the other
data-mines distinctive feature vectors and uses high dimensional
features directly for image matching, without quantization. Both of the
approaches demonstrate significant improvements compared to
straightforward image retrieval approaches, in highly confusing indoor
environment. The proposed image matching techniques have broad
applications. We selectively demonstrate two of them for this talk,
specifically for vision impaired users living in the office
environments. One application is data-driven zoomin; the other
application is image composition for object pop-out.