Abstract book (pdf) - ICPR 2010

17:00-17:20, Paper TuCT3.5
Multi-View based Estimation of Human Upper-Body Orientation
Rybok, Lukas, Karlsruhe Inst. of Tech.
Voit, Michael, Fraunhofer Inst. of Optronics
Ekenel, Hazim Kemal, Karlsruhe Inst. of Tech.
Stiefelhagen, Rainer, Karlsruhe Inst. of Tech. & Fraunhofer IITB
The knowledge about the body orientation of humans can improve speed and performance of many service components
of a smart-room. Since many of such components run in parallel, an estimator to acquire this knowledge needs a very low
computational complexity. In this paper we address these two points with a fast and efficient algorithm using the smartroom’s
multiple camera output. The estimation is based on silhouette information only and is performed for each camera
view separately. The single view results are fused within a Bayesian filter framework. We evaluate our system on a subset
of videos from the CLEAR 2007 dataset and achieve an average correct classification rate of 87.8%, while the estimation
itself just takes 12 ms when four cameras are used.
TuCT4 Dolmabahçe Hall A
Structural Methods Regular Session
Session chair: Ghosh, Joydeep (Univ. of Texas)
15:40-16:00, Paper TuCT4.1
An Iterative Algorithm for Approximate Median Graph Computation
Ferrer, Miquel, Univ. Pol. De Catalunuya
Bunke, Horst, Univ. of Bern
Recently, the median graph has been shown to be a good choice to obtain a representative of a given set of graphs. It has
been successfully applied to graph-based classification and clustering. In this paper we exploit a theoretical property of
the median, which has not yet been utilized in the past, to derive a new iterative algorithm for approximate median graph
computation. Experiments done using five different graph databases show that the proposed approach yields, in four out
of these five datasets, better medians than two of the previous existing methods.
16:00-16:20, Paper TuCT4.2
A Supergraph-Based Generative Model
Han, Lin, Univ. of York
Wilson, Richard, Univ. of York
Hancock, Edwin, Univ. of York
This paper describes a method for constructing a generative model for sets of graphs. The method is posed in terms of
learning a supergraph from which the samples can be obtained by edit operations. We construct a probability distribution
for the occurrence of nodes and edges over the supergraph. We use the EM algorithm to learn both the structure of the supergraph
and the correspondences between the nodes of the sample graphs and those of the supergraph, which are treated
as missing data. In the experimental evaluation of the method, we a) prove that our supergraph learning method can lead
to an optimal or suboptimal supergraph, and b) show that our proposed generative model gives good graph classification
results.
16:20-16:40, Paper TuCT4.3
Levelings and Flatzone Morphology
Meyer, Fernand, Mines-ParisTech
Successive levelings are applied on document images. The residues of successive levelings are made of flat zones for
which morphological transforms are described.
16:40-17:00, Paper TuCT4.4
Combining Force Histogram and Discrete Lines to Extract Dashed Lines
Debled-Rennesson, Isabelle, LORIA – Nancy Univ.
Wendling, Laurent, Univ. Paris Descartes
- 124 -