Abstract book (pdf) - ICPR 2010

tracking. The proposed learning method uses adaptive boosting and classification trees on a wide collection (shape, pose,
color, texture, etc.) of image features that constitute a model for tracked objects. The temporal dimension is taken into account
by using k-mean clusters of sequence samples. Most of the utilized object descriptors have a temporal quality also.
We argue that with a proper boosting approach and decent number of reasonably descriptive image features it is feasible
to do view-independent sequence matching in sparse camera networks. The experiments on real-life surveillance data support
this statement.
15:00-17:10, Paper MoBT8.32
Saliency Detection and Object Localization in Indoor Environments
Rudinac, Maja, Delft Univ. of Tech.
Jonker, Pieter, Delft Univ. of Tech.
In this paper we present a scene exploration method for the identification of interest regions in unknown indoor environments
and the position estimation of the objects located in those regions. Our method consists of two stages: First, we
generate a saliency map of the scene based on the spectral residual of three color channels and interest points are detected
in this map. Second, we propose and evaluate a method for the clustering of neighboring interest regions, the rejection of
outliers and the estimation of the positions of potential objects. Once the location of objects in the scene is known, recognition
of objects/object classes can be performed or the locations can be used for grasping the object. The main contribution
of this paper lies in a computationally inexpensive method for the localization of multiple salient objects in a scene. The
performance obtained on a dataset of indoor scenes shows that our method performs good, is very fast and hence highly
suitable for real-world applications, such as mobile robots and surveillance.
15:00-17:10, Paper MoBT8.33
Bubble Tag Identification using an Invariant–Under–Perspective Signature
Patraucean, Viorica, Univ. of Toulouse
Gurdjos, Pierre, Univ. of Toulouse
Conter, Jean, Univ. of Toulouse
We have at our disposal a large database containing images of various configurations of coplanar circles, randomly laidout,
called Bubble Tags. The images are taken from different viewpoints. Given a new image (query image), the goal is to
find in the database the image containing the same bubble tag as the query image. We propose representing the images
through projective invariant signatures which allow identifying the bubble tag without passing through an Euclidean reconstruction
step. This is justified by the size of the database, which imposes the use of queries in 1D/vectorial form, i.e.
not in 2D/matrix form. The experiments carried out confirm the efficiency of our approach, in terms of precision and complexity.
15:00-17:10, Paper MoBT8.35
The Role of Polarity in Haar-Like Features for Face Detection
Landesa-Vázquez, Iago, Univ. de Vigo
Alba Castro, Jose Luis, Univ. of Vigo
Human vision is primarily based on local contrast perception and its polarity. Viola and Jones proposed, in their wellknown
face detector framework, a boosted cascade of weak classifiers based on Haar-like features which encode local
contrast and polarity information. Nevertheless contrast polarity invariance, which is not directly modeled in their framework,
has been shown to be perceptually relevant for the human capability of detecting faces. In this paper we study, from
both algorithmical and perceptual points of view, the effect of enhancing Haar-like features with polarity invariance and
how it may improve cascaded classifiers.
15:00-17:10, Paper MoBT8.36
A Human Detection Framework for Heavy Machinery
Heimonen, Teuvo Antero, Univ. of Oulu
Heikkilä, Janne, Univ. of Oulu
A stereo camera based human detection framework for heavy machinery is proposed. The framework allows easy integration
of different human detection and image segmentation methods. This integration is essential for diverge and challenging
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