Abstract book (pdf) - ICPR 2010

direction of the original center pixel. The difference between the center pixel and the average is by definition a vector
which is scalar away from the center pixel. Thus adding the average to the center pixel is guaranteed not to result in colour
shifts. This projection step is also shown to remedy the problem of equiluminance colours and can be used for m-dimensional
data. Finally, the results indicate that the new sharpening method results in better sharpening than that achieved
using unsharp masking with noticeably less halos around strong edges. The latter aspect of the algorithm is believed to be
due to the asymmetric nature of the projection step.
13:30-16:30, Paper ThBCT9.40
Maximally Stable Texture Regions
Güney, Mesut, Turkish Naval Academy
Arica, Nafiz, Turkish Naval Academy
In this study, we propose to detect interest regions based on texture information of images. For this purpose, Maximally
Stable Extremal Regions (MSER) approach is extended using the high dimensional texture features of image pixels. The
regions with different textures from their vicinity are detected using agglomerative clustering successively. The proposed
approach is evaluated in terms of repeatability and matching scores in an experimental setup used in the literature. It outperforms
the intensity and color based detectors, especially in the images containing textured regions. It succeeds better
in the transformations including viewpoint change, blurring, illumination and JPEG compression, while producing comparable
results in the other transformations tested in the experiments.
13:30-16:30, Paper ThBCT9.41
Combining the Likelihood and the Kullback-Leibler Distance in Estimating the Universal Background Model for
Speaker Verification using SVM
Lei, Zhenchun, JiangxiNormal Univ.
The state-of-the-art methods for speaker verification are based on the support vector machine. The Gaussian supervector
SVM is a typical method which uses the Gaussian mixture model for creating feature vectors for the discriminative SVM.
And all GMMs are adapted from the same universal background model, which is got by maximum likelihood estimation
on a large number of data sets. So the UBM should cover the feature space widely as possible. We propose a new method
to estimate the parameters of the UBM by combining the likelihood and the Kullback-Leibler distances in the UBM. Its
aim is to find the model parameters which get the high likelihood value and all Gaussian distributions are dispersed to
cover the feature space in a great measuring. Experiments on NIST 2001 task show that our method can improve the performance
obviously.
13:30-16:30, Paper ThBCT9.42
Asymmetric Generalized Gaussian Mixture Models and EM Algorithm for Image Segmentation
Nacereddine, Nafaa, LORIA
Tabbone, Salvatore, Univ. Nancy 2-LORIA
Ziou, Djemel, Sherbrooke Univ.
Hamami, Latifa, Ec. Nationale Pol.
In this paper, a parametric and unsupervised histogram-based image segmentation method is presented. The histogram is
assumed to be a mixture of asymmetric generalized Gaussian distributions. The mixture parameters are estimated by using
the Expectation Maximization algorithm. Histogram fitting and region uniformity measures on synthetic and real images
reveal the effectiveness of the proposed model compared to the generalized Gaussian mixture model.
13:30-16:30, Paper ThBCT9.43
Color Connectedness Degree for Mean-Shift Tracking
Gouiffès, Michèle, IEF Univ. Paris Sud 11
Laguzet, Florence, LRI Univ. Paris Sud 11
Lacassagne, Lionel, IEF Univ. Paris Sud 11
This paper proposes an extension to the mean shift tracking. We introduce the color connectedness degrees (CCD) which,
more than providing statistical information about the target to track, embeds information about the amount of connectedness
of the color intervals which compose the target. With a low increase of complexity, this approach provides a better robust-
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