Abstract book (pdf) - ICPR 2010

We propose a scene classification method, which combines two popular methods in the literature: Spatial Pyramid Matching
(SPM) and probabilistic Latent Semantic Analysis (pLSA) modeling. The proposed scheme called Cascaded pLSA performs
pLSA in a hierarchical sense after the soft-weighted BoW representation based on dense local features is extracted.
We associate spatial layout information by dividing each image into overlapping regions iteratively at different resolution
levels and implementing a pLSA model for each region individually. Finally, an image is represented by concatenated
topic distributions of each region. In performance evaluation, we compare the proposed method with the most successful
methods in the literature, using the popular 15-class-dataset. In the experiments, it is seen that our method slightly outperforms
the others in that particular dataset.
09:00-11:10, Paper ThAT8.46
Optimization of Target Objects for Natural Feature Tracking
Gruber, Lukas, Graz Univ. of Tech.
Zollmann, Stefanie, Graz Univ. of Tech.
Wagner, Daniel, Graz Univ. of Tech.
Schmalstieg, Dieter, Graz Univ. of Tech.
Hollerer, Tobias, UCSB
This paper investigates possible physical alterations of tracking targets to obtain improved 6DoF pose detection for a
camera observing the known targets. We explore the influence of several texture characteristics on the pose detection, by
simulating a large number of different target objects and camera poses. Based on statistical observations, we rank the importance
of characteristics such as texturedness and feature distribution for a specific implementation of a 6DoF tracking
technique. These findings allow informed modification strategies for improving the tracking target objects themselves, in
the common case of man-made targets, as for example used in advertising. This fundamentally differs from and complements
the traditional approach of leaving the targets unchanged while trying to optimize the tracking algorithms and parameters.
09:00-11:10, Paper ThAT8.47
View-Invariant Action Recognition using Rank Constraint
Ashraf, Nazim, Univ. of Central Florida
Shen, Yuping, Univ. of Central Florida
Foroosh, Hassan, Univ. of Central Florida
We propose a new method for view-invariant action recognition based on the rank constraint on the family of planar homographies
associated with triplets of body points. We represent action as a sequence of poses and we use the fact that the
family of homographies associated with two identical poses would have rank 4 to gauge similarity of the pose between
two subjects, observed by different perspective cameras and from different viewpoints. Extensive experimental results
show that our method can accurately identify action from video sequences when they are observed from totally different
viewpoints with different camera parameters.
09:00-11:10, Paper ThAT8.48
Coarse-To-Fine Particle Filter by Implicit Motion Estimation for 3D Head Tracking on Mobile Devices
Sung, Hacheon, Yonsei Univ.
Choi, Kwontaeg, Yonsei Univ.
Byun, Hyeran, Yonsei Univ.
Due to the widely spread mobile devices over the years, a low cost implementation of an efficient head tracking system
is becoming more useful for a wide range of applications. In this paper, we make an attempt to solving real-time 3D head
tracking problem on mobile devices by enhancing the fitness of the dynamics. In our method, the particles are generated
by implicit motion estimation between two particles rather than the explicit motion estimation using corresponding point
matching between consecutive two frames. This generation is applied iteratively using coarse-to fine strategy in order to
handle a large motion using a small number of particle. This reduces the computational cost while preserving the performance.
We evaluate the efficiency and effectiveness of the proposed algorithm by empirical experiments. Finally, we demonstrate
our method on a recent mobile phone.
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