Abstract book (pdf) - ICPR 2010

native power more efficiently because the classification on Sigma sets can be directly performed in vector space. Experimental
results on the INRIA and the Daimler Chrysler pedestrian datasets show the effectiveness and efficiency of the
proposed method.
13:30-16:30, Paper WeBCT8.36
Reverse Indexing for Reading Graffiti Tags
Thurau, Christian, Fraunhofer IAIS
Bauckhage, Christian, Fraunhofer IAIS
In this paper, we consider the problem of automatically reading graffiti tags. As a preparatory step, we create a large set
of synthetic graffiti-like characters, generated from publicly available true type fonts. For each character in the database,
we extract a number of scale independent local binary descriptors. Then, using binary non negative matrix factorization,
a sufficient number of basis functions are learned. Basis function coefficients of novel images can then be directly used
for hashing characters from the database of prototypes. Finally, graffiti tags are recognized by means of a localized, spatial
voting scheme.
13:30-16:30, Paper WeBCT8.37
Generic Object Recognition by Tree Conditional Random Field based on Hierarchical Segmentation
Okumura, Takeshi, Kobe Univ.
Takiguchi, Tetsuya, Kobe Univ.
Ariki, Yasuo, Kobe Univ.
Generic object recognition by a computer is strongly required in various fields like robot vision and image retrieval in
recent years. Conventional methods use Conditional Random Field (CRF) that recognizes the class of each region using
the features extracted from the local regions and the class co-occurrence between the adjoining regions. However, there
is a problem that the discriminative ability of the features extracted from local regions is insufficient, and these methods
is not robust to the scale variance. To solve this problem, we propose a method that integrates the recognition results in
multi-scales by tree conditional random field based on hierarchical segmentation. As a result of the image dataset of 7
classes, the proposed method has improved the recognition rate by 2.2%.
13:30-16:30, Paper WeBCT8.38
A Fast Approach for Pixelwise Labeling of Facade Images
Fröhlich, Björn, Friedrich-Schiller Univ. of Jena
Rodner, Erik, Friedrich-Schiller Univ. of Jena
Denzler, Joachim, Friedrich-Schiller Univ. of Jena
Facade classification is an important subtask for automatically building large 3d city models. In the following we present
an approach for pixel wise labeling of facade images using an efficient Randomized Decision Forest classifier and robust
local color features. Experiments are performed with a popular facade dataset and a new demanding dataset of pixel wise
labeled images from the Label Me project. Our method achieves high recognition rates and is significantly faster for
training and testing than other Methods based on expensive feature transformation techniques.
13:30-16:30, Paper WeBCT8.39
Real-Time Traffic Sign Detection: An Evaluation Study
Li, Ying, IBM T. J. Watson Res. Center
Guan, Weiguang, IBM
Pankanti, Sharath
This paper presents an experimental evaluation of three different traffic sign detection approaches, which detect or localize
various types of traffic signs from real-time videos. Specifically, the first approach exploits geometric features to identify
traffic signs, while the other two are developed based on SVM (Support Vector Machine) and AdaBoost learning mechanisms.
We describe each of the three approaches, conduct a detailed comparison among them, and examine their pros and
cons. Our conclusions should lead to useful guidelines for developing a real-time traffic sign detector.
- 221 -