Abstract book (pdf) - ICPR 2010

them to discriminate between cognitive load levels in order to identify the individual contribution of each for cognitive
load measurement. Voice source-related features are then used to improve the performance of current cognitive load classification
systems, using adapted Gaussian mixture models. Our experimental result shows that the use of voice source
feature could yield around 12% reduction in relative error rate compared with the baseline system based on MFCCs, intensity,
and pitch contour.
13:30-16:30, Paper ThBCT9.32
Adaptive Enhancement with Speckle Reduction for SAR Images using Mirror-Extended Curvelet and PSO
Li, Ying, Northwestern Pol. Univ.
Hongli, Gong, Northwestern Pol. Univ.
Wang, Qing, Northwestern Pol. Univ.
Speckle and low contrast can cause image degradation, which reduces the detectability of targets and impedes further investigation
of synthetic aperture radar (SAR) images. This paper presents an adaptive enhancement method with speckle
reduction for SAR images using mirror-extended curve let (ME-curve let) transform and particle swarm optimization
(PSO). First, an improved enhancement function is proposed to nonlinearly shrink and stretch the curve let coefficients.
Then, a novel objective evaluation criterion is introduced to adaptively obtain the optimal parameters in the enhancement
function. Finally, a PSO algorithm with two improvements is used as a global search strategy for the best enhanced image.
Experimental results indicate that the proposed method can reduce the speckle and enhance the edge features and the contrast
of SAR images better with comparison to the wavelet-based and curve let-based non-adaptive enhancement methods.
13:30-16:30, Paper ThBCT9.33
Recursive Video Matting and Denoising
Prabhu, Sahana, Indian Inst. of Tech. Madras
Ambasamudram, Rajagopalan, Indian Inst. of Tech. Madras
In this paper, we propose a video matting method with simultaneous noise reduction based on the Unscented Kalman filter
(UKF). This recursive approach extracts the alpha mattes and denoised foregrounds from noisy videos, in a unified framework.
No assumptions are made about the type of motion of the camera or of the foreground object in the video. Moreover,
user-specified trimaps are required only once every ten frames. In order to accurately extract information at the borders
between the foreground and the background, we include a discontinuity-adaptive Markov random field (MRF) prior. It
incorporates spatio-temporal information from the current and previous frame during estimation of the alpha matte as well
as the foreground. Results are given on videos with real film-grain noise.
13:30-16:30, Paper ThBCT9.35
The Effects of Radiometry on the Accuracy of Intensity based Registration
Selby, Boris Peter, Medcom GmbH
Sakas, Georgios, Fraunhofer IGD
Walter, Stefan, Medcom GmbH
Groch, Wolf-Dieter, Univ. of Applied Sciences Darmstadt
Stilla, Uwe, Tech. Univ. Muenchen
Besides several other factors, radiometric differences between a reference and a floating image greatly influence the achievable
accuracy of image registration. In this work we derive the magnitude of registration inaccuracy coming from changes
in radiometric properties. This is done for the example of medical X-ray image registration. We therefore estimate the
change of image intensity with respect to object shape, X-ray attenuation of the object material and the initial X-ray energy
by modeling a simplified image formation process. The change in intensity is then used to determine a closed form estimation
of the resulting registration error, independent from a specific registration algorithm. Finally the theoretical calculations
are compared to the accuracy of intensity based registration performed on X-ray images with different radiometric
properties. Results show that the herewith derived accuracy estimation is well suited to predict the achievable accuracy of
a registration for images with radiometric differences.
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