Abstract book (pdf) - ICPR 2010

09:00-11:10, Paper WeAT8.25
An Improved Method for Cirrhosis Detection using Liver’s Ultrasound Images
Fujita, Yusuke, Yamaguchi Univ.
Hamamoto, Yoshihiko, Yamaguchi Univ.
Segawa, Makoto, Yamaguchi Univ.
Terai, Shuji, Yamaguchi Univ.
Sakaida, Isao, Yamaguchi Univ.
This paper describes an improved method for cirrhosis detection in the liver using Gabor features from ultrasound images.
There are three main contributions of our cirrhosis detection method. The first contribution of this method is to combine
weak classifiers using the AdaBoost algorithm. The second one is to use an artificial dataset to avoid the problem of over
fitting the limited training dataset. The third one is to apply a voting classification with use of multiple regions of interest
(ROIs). Although the accuracy rate of a single classifier designed with only original dataset was 56%, that of the proposed
method was 80% in cross-validation.
09:00-11:10, Paper WeAT8.26
A Dual Pass Video Stabilization System using Iterative Motion Estimation and Adaptive Motion Smoothing
Pan, Pan, Fujitsu R&D Center Co., Ltd.
Minagawa, Akihiro, Fujitsu Lab. LTD
Sun, Jun, Fujitsu R&D Center Co., LTD
Hotta, Yoshinobu, Fujitsu Lab. LTD.
Naoi, Satoshi, Fujitsu R&D Center Co., LTD
In this paper, we propose a novel dual pass video stabilization system using iterative motion estimation and adaptive
motion smoothing. In the first pass, the transformation matrix to stabilize each frame is returned. The global motion estimation
is carried out by a novel iterative method. The intentional motion is estimated using adaptive window smoothing.
Before the beginning of the second pass, we obtain the optimal trim size for a specific video based on the statistics of the
transformation parameters. In the second pass, the stabilized video is composed according to the optimal trim size. Experimental
results show the superior performance of the proposed method in comparison to other existing methods.
09:00-11:10, Paper WeAT8.27
A Modified Particle Swarm Optimization Applied in Image Registration
Niazi, Muhammad Khalid Khan, Uppsala Univ.
Nystrom, Ingela, Uppsala Univ.
We report a modified version of the particle swarm optimization (PSO) algorithm and its application to image registration.
The modified version utilizes benefits from the Gaussian and the uniform distribution, when updating the velocity equation
in the PSO algorithm. Which one of the distributions is selected depends on the direction of the cognitive and social components
in the velocity equation. This direction checking and selection of the appropriate distribution provide the particles
with an ability to jump out of local minima. The registration results achieved by this new version proves the robustness
and its ability to find a global minimum.
09:00-11:10, Paper WeAT8.28
Image Segmentation based on Adaptive Fuzzy-C-Means Clustering
Ayech, Mohamed Walid, Pol. de Recherche Informatique Du CEntre
El Kalti, Karim, Faculty of Science of Monastir Tunisia
El Ayeb, Bechir, Pol. de Recherche Informatique Du CEntre
The clustering method Fuzzy-C-Means (FCM) is widely used in image segmentation. However, the major drawback of
this method is its sensitivity to the noise. In this paper, we propose a variant of this method which aims at resolving this
problem. Our approach is based on an adaptive distance which is calculated according to the spatial position of the pixel
in the image. The obtained results have shown a significant improvement of our approach performance compared to the
standard version of the FCM, especially regarding the robustness face to noise and the accuracy of the edges between regions.
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