Abstract book (pdf) - ICPR 2010

A multi-scale approach is proposed for polygonal representation of a digital curve by using the notion of blurred segment
and a split-and-merge strategy. Its main idea is to decompose the curve into meaningful parts that are represented by detected
dominant points at the appropriate scale. The method uses no threshold and can automatically decompose the curve
into meaningful parts.
09:00-11:10, Paper TuAT8.46
A Memetic Algorithm for Selection of 3D Clustered Features with Applications in Neuroscience
Björnsdotter, Malin, Univ. of Gothenburg
Wessberg, Johan, Univ. of Gothenburg
We propose a Memetic algorithm for feature selection in volumetric data containing spatially distributed clusters of informative
features, typically encountered in neuroscience applications. The proposed method complements a conventional genetic
algorithm with a local search utilizing inherent spatial relationships to efficiently identify informative feature clusters across
multiple regions of the search volume. First, we demonstrate the utility of the algorithm on simulated data containing informative
feature clusters of varying contrast-to-noise-ratios. The Memetic algorithm identified a majority of the relevant
features whereas a conventional genetic algorithm detected only a subset sufficient for fitness maximization. Second, we
applied the algorithm to authentic functional magnetic resonance imaging (fMRI) brain activity data from a motor task study,
where the Memetic algorithm identified expected brain regions and subsequent brain activity prediction in new individuals
was accurate at an average of 76% correct classification. The proposed algorithm constitutes a novel method for efficient
volumetric feature selection and is applicable in any 3D data scenario. In particular, the algorithm is a promising alternative
for sensitive brain activity mapping and decoding.
09:00-11:10, Paper TuAT8.47
Pose Estimation of Known Objects by Efficient Silhouette Matching
Reinbacher, Christian, Graz Tech. Univ.
Ruether, Matthias, Graz Univ. of Tech.
Bischof, Horst, Graz Univ. of Tech.
Pose estimation is essential for automated handling of objects. In many computer vision applications only the object silhouettes
can be acquired reliably, because untextured or slightly transparent objects do not allow for other features. We propose
a pose estimation method for known objects, based on hierarchical silhouette matching and unsupervised clustering. The
search hierarchy is created by an unsupervised clustering scheme, which makes the method less sensitive to parametrization,
and still exploits spatial neighborhood for efficient hierarchy generation. Our evaluation shows a decrease in matching time
of 80% compared to an exhaustive matching and scalability to large models.
09:00-11:10, Paper TuAT8.48
Learning Non-Linear Dynamical Systems by Alignment of Local Linear Models
Joko, Masao, The Univ. of Tokyo
Kawahara, Yoshinobu, Osaka Univ.
Yairi, Takehisa, Univ. of Tokyo
Learning dynamical systems is one of the important problems in many fields. In this paper, we present an algorithm for
learning non-linear dynamical systems which works by aligning local linear models, based on a probabilistic formulation of
subspace identification. Because the procedure for constructing a state sequence in subspace identification can be interpreted
as the CCA between past and future observation sequences, we can derive a latent variable representation for this problem.
Therefore, as in a similar manner to the recent works on learning a mixture of probabilistic models, we obtain a framework
for constructing a state space by aligning local linear coordinates. This leads to a prominent algorithm for learning non-linear
dynamical systems. Finally, we apply our method to motion capture data and show how our algorithm works well.
09:00-11:10, Paper TuAT8.49
A Column Generation Approach for the Graph Matching Problem
Silva, Freire, Alexandre, Univ. of Sao Paulo
Jr., R. M. Cesar, Univ. of Sao Paulo
Ferreira, C.E., Univ. of Sao Paulo
Graph matching plays a central role in different problems for structural pattern recognition. Examples of applications include
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