Abstract book (pdf) - ICPR 2010

tection data indicate that combining the responses from multiple HMMs with IBC can achieve a significantly higher level
of performance than with the AND and OR combinations, especially when training data is limited and imbalanced.
13:30-16:30, Paper ThBCT8.38
Stereo-Based Multi-Person Tracking using Overlapping Silhouette Templates
Satake, Junji, Toyohashi Univ. of Tech.
Miura, Jun, Toyohashi Univ. of Tech.
This paper describes a stereo-based person tracking method for a person following robot. Many previous works on person
tracking use laser range finders which can provide very accurate range measurements. Stereo-based systems have also
been popular, but most of them are not used for controlling a real robot. We previously developed a tracking method which
uses depth templates of person shape applied to a dense depth image. The method, however, sometimes failed when complex
occlusions occurred. In this paper, we propose an accurate, stable tracking method using overlapping silhouette templates
which consider how persons overlap in the image. Experimental results show the effectiveness of the proposed
method.
13:30-16:30, Paper ThBCT8.40
Characterising Facial Gender Difference using Fisher-Rao Metric
Ceolin, Simone Regina, Univ. of York
Hancock, Edwin, Univ. of York
The aim in this paper is to explore whether the Fisher-Rao metric can be used to measure different facets of facial shape
estimated from fields of surface normals using the von-Mises Fisher distribution. In particular we aim to characterise the
shape changes due to differences in gender. We make use of the von-Mises Fisher distribution since we are dealing with
surface normal data over the sphere R^2. Finally, we show the results achieved using EAR and Max Planck datasets.
13:30-16:30, Paper ThBCT8.41
On-Line FMRI Data Classification using Linear and Ensemble Classifiers
Plumpton, Catrin Oliver, Bangor Univ.
Kuncheva, Ludmila I., Bangor Univ.
Linden, David E. J., Bangor Univ.
Johnston, Stephen Jaye, Bangor Univ.
The advent of real-time fMRI pattern classification opens many avenues for interactive self-regulation where the brain’s
response is better modelled by multivariate, rather than univariate techniques. Here we test three on-line linear classifiers,
applied to a real fMRI dataset, collected as part of an experiment on the cortical response to emotional stimuli. We propose
a random subspace ensemble as a fast and more accurate alternative to component classifiers. The on-line linear discriminant
classifier (O-LDC) was found to be a better base classifier than the on-line versions of the perceptron and the balanced
winnow.
13:30-16:30, Paper ThBCT8.42
Adaptive Feature and Score Level Fusion Strategy using Genetic Algorithms
Ben Soltana, Wael, Ec. Centrale de Lyon
Ardabilian, Mohsen, Ec. Centrale de Lyon
Chen, Liming, Ec. Centrale de Lyon
Ben Amar, Chokri, Res. Group on Intelligent Machines
Classifier fusion is considered as one of the best strategies for improving performance of general purpose classification systems.
On the other hand, fusion strategy space strongly depends on classifiers, features and data spaces. As the cardinality
of this space is exponential, one needs to resort to a heuristic to find a sub-optimal fusion strategy. In this work, we present
a new adaptive feature and score level fusion strategy (AFSFS) based on adaptive genetic algorithm. AFSFS tunes itself between
feature and matching score level, and improves the final performance over the original on both levels, and as a fusion
method, it does not only contain fusion strategy to combine the most relevant features so as to achieve adequate and optimized
results, but also has the extensive ability to select the most discriminative features. Experiments are provided on the FRGC
database showing that the proposed method produces significantly better results than the baseline fusion methods.
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