Abstract book (pdf) - ICPR 2010

13:30-16:30, Paper ThBCT9.36
Fence Removal from Multi-Focus Images
Yamashita, Atsushi, Shizuoka Univ.
Matsui, Akiyoshi, Shizuoka Univ.
Kaneko, Toru, Shizuoka Univ.
When an image of a scene is captured by a camera through a fence, a blurred fence image interrupts objects in the scene.
In this paper, we propose a method for a fence removal from the image using multiple focusing. Most of previous methods
interpolate the interrupted regions by using information of surrounding textures. However, these methods fail when information
of surrounding textures is not rich. On the other hand, there are methods that acquire multiple images for image
restoration and composite them to generate a new clear image. The latter approach is adopted because it is robust and accurate.
Multi-focus images are acquired and ``defocusing’’ information is utilized to generate a clear image. Experimental
results show the effectiveness of the proposed method.
13:30-16:30, Paper ThBCT9.37
Information Theoretic Expectation Maximization based Gaussian Mixture Modeling for Speaker Verification
Memon, Sheeraz, RMIT Univ.
Lech, Margaret, RMIT Univ.
Namunu, Maddage, RMIT Univ.
The expectation maximization (EM) algorithm is widely used in the Gaussian mixture model (GMM) as the state-of-art
statistical modeling technique. Like the classical EM method, the proposed EM-Information Theoretic algorithm (EM-
IT) adapts means, covariances and weights, however this process is not conducted directly on feature vectors but on a
smaller set of centroids derived by the information theoretic procedure, which simultaneously minimizes the divergence
between the Parzen estimates of the feature vector’s distribution within a given Gaussian component and the centroid’s
distribution within the same Gaussian component. The EM-IT algorithm was applied to the speaker verification problem
using NIST 2004 speech corpus and the MFCC with dynamic features. The results showed an improvement of the equal
error rate (ERR) by 1.5% over the classical EM approach. The EM-IT also showed higher convergence rates compare to
the EM method.
13:30-16:30, Paper ThBCT9.38
A Gaussian Process Regression Framework for Spatial Error Concealment with Adaptive Kernels
Asheri, Hadi, Sharif Univ. of Tech.
Rabiee, Hamid Reza, Sharif Univ. of Tech.
Pourdamghani, Nima, Sharif Univ. of Tech.
Rohban, Mohammad H., Sharif Univ. of Tech.
We have developed a Gaussian Process Regression method with adaptive kernels for concealment of the missing macroblocks
of block-based video compression schemes in a packet video system. Despite promising results, the proposed algorithm
introduces a solid framework for further improvements. In this paper, the problem of estimating lost macro-blocks
will be solved by estimating the proper covariance function of the Gaussian process defined over a region around the missing
macro-blocks (i.e. its kernel function). In order to preserve block edges, the kernel is constructed adaptively by using
the local edge related information. Moreover, we can achieve more improvement by local estimation of the kernel parameters.
While restoring the prominent edges of the missing macro-blocks, the proposed method produces perceptually
smooth concealed frames. Objective and subjective evaluations verify the effectiveness of the proposed method.
13:30-16:30, Paper ThBCT9.39
Colour Constant Image Shapening
Alsam, Ali, Sør-Trøndelag Univ. Coll.
In this paper, we introduce a new sharpening method which guarantees colour constancy and resolves the problem of equiluminance
colours. The algorithm is similar to unsharp masking in that the gradients are calculated at different scales by
blurring the original with a variable size kernel. The main difference is in the blurring stage where we calculate the average
of an n times n neighborhood by projecting each colour vector onto the space of the center pixel before averaging. Thus
starting with the center pixel we define a projection matrix onto the space of that vector. Each neighboring colour is then
projected onto the center and the result is summed up. The projection step results in an average vector which shares the
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