Exploiting the Richness of Digital Photographs and Videos

Speaker:	Dr. Sylvain PARIS
		Computer Graphics Group
		Computer Science and Artificial Intelligence Lab.
		Massachusetts Institute of Technology

Title:		"Exploiting the Richness of Digital Photographs
		 and Videos"

Date:		Friday, 25 May 2007

Time:		4:00pm - 5:00pm

Venue:		Room 4204 (CSE Graphic Lab, via. 19)
		HKUST

Abstract:

Cameras have become popular with the recent development of digital
equipment. Today, it is extremely simple to take high-resolution pictures
and movies, thereby giving easy access to an abundance of information.
However, the size and number of acquired images challenge most existing
algorithms. In this context, I will present my work on computational
photography and low-level image processing. I will first expose a method
to transfer the visual qualities of an artist's photograph to another
picture. This method makes it easy for novice photographers to achieve
compelling renditions and can also improve the workflow of professionals.
In the second part, I will describe the bilateral filter, a nonlinear
edge-preserving process which is becoming ubiquitous in computational
photography. I will reformulate this filter in a higher-dimensional
homogeneous space and show that, using signal-processing arguments, it is
possible to very quickly compute an approximation visually similar to the
exact result. Finally, I will briefly present my on-going projects that
extend these ideas to image segmentation and video processing.


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Biography:

Sylvain PARIS is currently working with Frmdo Durand as a post-doc in the
Computer Graphics Group in the Computer Science and Artificial
Intelligence Laboratory at the Massachusetts Institute of Technology.

His work mainly focuses on extracting information from real images. The
goal is to obtain data useful for Computer Graphics, i.e. suitable for
rendering new images. During his PhD with Francois Sillion, he developed
new solutions to face relighting, shape reconstruction from image
sequences, and for recovery of the 3D geometry of hair. The main results
of this work are an optimal complexity algorithm for acquiring precise 3D
models from several photographs, and a technique that exploits a video
sequence to build a dense set of 3D lines that match someone's hairstyle.