Abstract book (pdf) - ICPR 2010

matching 3D CAD models, shape matching and medical imaging, to name but a few. In this paper, we present a new integer
linear formulation for the problem and employ a combinatorial optimization technique, called column generation, in order
to solve instances of the problem. We also present computational experiments with generated instances.
09:00-11:10, Paper TuAT8.50
Pattern Recognition using Functions of Multiple Instances
Zare, Alina, Univ. of Florida
Gader, Paul, Univ. of Florida
The Functions of Multiple Instances (FUMI) method for learning a target prototype from data points that are functions of
target and non-target prototypes is introduced. In this paper, a specific case is considered where, given data points which are
convex combinations of a target prototype and several non-target prototypes, the Convex-FUMI (C-FUMI) method learns
the target and non-target patterns, the number of nontarget patterns, and determines the weights (or proportions) of all the
prototypes for each data point. For this method, training data need only binary labels indicating whether the data contains or
does not contain some proportion of the target prototype; the specific target weights for the training data are not needed.
After learning the target prototype using the binary labeled training data, target detection is performed on test data. Results
showing detection of the skin in hyper spectral imagery and sub-pixel target detection in simulated data are presented.
09:00-11:10, Paper TuAT8.51
Linear Decomposition of Planar Shapes
Faure, Alexandre, LAIC Univ. d’Auvergne
Feschet, Fabien, Univ. d’Auvergne Clermont-Ferrand 1
The issue of decomposing digital shapes into sets of digital primitives has been widely studied over the years. Practically all
existing approaches require perfect or cleaned shapes. Those are obtained using various pre-processing techniques such as
thinning or skeletonization. The aim of this paper is to bypass the use of such pre-processings, in order to obtain decompositions
of shapes directly from connected components. This method has the advantage of taking into account the intrinsic
thickness of digital shapes, and provides a decomposition which is also robust to
09:00-11:10, Paper TuAT8.52
Sketched Symbol Recognition with a Latent-Dynamic Conditional Model
Deufemia, Vincenzo, Univ. di Salerno
Risi, Michele, Univ. of Salerno
Tortora, Genoveffa, Univ. di Salerno
In this paper we present a recognizer of sketched symbols based on Latent-Dynamic Conditional Random Fields (LDCRF),
a discriminative model for sequence classification. The LDCRF model classifies unsegmented sequences of strokes into domain
symbols by taking into account contextual and temporal information. In particular, LDCRFs learn the extrinsic dynamics
among strokes by modeling a continuous stream of symbol labels, and learn internal stroke sub-structure by using intermediate
hidden states. The performance of our work is evaluated in the electric circuit domain.
09:00-11:10, Paper TuAT8.53
Canonical Patterns of Oriented Topologies
Mankowski, Walter, Drexel Univ.
Shokoufandeh, Ali, Drexel Univ.
Salvucci, Dario, Drexel Univ.
A common problem in many areas of behavioral research is the analysis of the large volume of data recorded during the execution
of the tasks being studied. Recent work has proposed the use of an automated method based on canonical sets to
identify the most representative patterns in a large data set, and described an initial experiment in identifying canonical webbrowsing
patterns. However, there is a significant limitation to the method: it requires the similarity matrix to be symmetric,
and thus can only be used for problems that can be modeled as unoriented topologies. In this paper we propose a novel enhancement
to the method to support oriented topologies by allowing the similarity matrix to be nonsymmetric. We demonstrate
the power of this new technique by applying the new method to find canonical lane changes in a driving simulator experiment.
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