Abstract book (pdf) - ICPR 2010

09:00-11:10, Paper TuAT9.58
Profile Lip Reading for Vowel and Word Recognition
Saitoh, Takeshi, Kyushu Inst. of Tech.
Konishi, Ryosuke, Tottori Univ.
This paper focuses on the profile view, which is the second most typical angle after the frontal face, and proposes a profile
view lip reading method. We applied the normalized cost method to detect profile contour. Five feature points, the tip of the
nose, upper lip, lip corner, lower lip, and chin, were detected from the contour, and eight features obtained from the five
feature points were defined. We gathered two types of utterance scenes, five Japanese vowels and 20 Japanese words. We
selected 20 combinations based on the eight features and carried out recognition experiments. Recognition rates of 99% for
vowel recognition and 86% for word recognition were obtained with five features: two lip heights, two protrusion lengths,
and one lip angle.
11:10-12:10, TuPL1 Anadolu Auditorium
Computational Cameras: Redefining the Image
Shree Nayar Plenary Session
Columbia University, USA
Shree K. Nayar received his PhD degree in Electrical and Computer Engineering from the Robotics Institute at Carnegie
Mellon University in 1990. He is currently the T. C. Chang Professor of Computer Science at Columbia University. He
co-directs the Columbia Vision and Graphics Center. He also heads the Columbia Computer Vision Laboratory (CAVE),
which is dedicated to the development of advanced computer vision systems. His research is focused on three areas; the
creation of novel cameras, the design of physics based models for vision, and the development of algorithms for scene
understanding. His work is motivated by applications in the fields of digital imaging, computer graphics, and robotics.
He has received best paper awards at ICCV 1990, ICPR 1994, CVPR 1994, ICCV 1995, CVPR 2000 and CVPR 2004.
He is the recipient of the David Marr Prize (1990 and 1995), the David and Lucile Packard Fellowship (1992), the National
Young Investigator Award (1993), the NTT Distinguished Scientific Achievement Award (1994), the Keck Foundation
Award for Excellence in Teaching (1995) and the Columbia Great Teacher Award (2006). In February 2008, he was elected
to the National Academy of Engineering.
The computational camera embodies the convergence of the camera and the computer. It uses new optics to select rays
from the scene in unusual ways, and an appropriate algorithm to process the selected rays. This as eline to manipulate
images before they are recorded and process the recorded images before they are presented is a powerful one. It enables
us to experience our visual world in rich and compelling ways.
TuBT1 Anadolu Auditorium
Image Analysis – IV Regular Session
Session chair: Hlavac, Vaclav (Czech Technical Univ.)
13:30-13:50, Paper TuBT1.1
Joint Image GMM and Shading MAP Estimation
Shekhovtsov, Alexander, Czech Tech. Univ. in Prague
Hlavac, Vaclav, Czech Tech. Univ.
We consider a simple statistical model of the image, in which the image is represented as a sum of two parts: one part is
explained by an i.i.d. color Gaussian mixture and the other part by a (piecewise) smooth gray scale shading function. The
smoothness is ensured by a quadratic (Tikhonov) or total variation regularization. We derive an EM algorithm to estimate
simultaneously the parameters of the mixture model and the shading. Our algorithms for both kinds of the regularization
solve for shading and mean parameters of the mixture model jointly.
13:50-14:10, Paper TuBT1.2
Continuous Markov Random Field Optimization using Fusion Move Driven Markov Chain Monte Carlo Technique
Kim, Wonsik, Seoul National Univ.
Lee, Kyoung Mu, Seoul National Univ.
Many vision applications have been formulated as Markov Random Field (MRF) problems. Although many of them are
discrete labeling problems, continuous formulation often achieves great improvement on the qualities of the solutions in
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