Abstract book (pdf) - ICPR 2010

15:00-17:10, Paper MoBT8.15
Contextual Features for Head Pose Estimation in Football Games
Launila, Andreas, Royal Inst. of Tech. (KTH)
Sullivan, Josephine, Royal Inst. of Tech. (KTH)
We explore the benefits of using contextual features for head pose estimation in football games. Contextual features are
derived from knowledge of the position of all players and combined with image based features derived from low-resolution
footage. Using feature selection and combination techniques, we show that contextual features can aid head pose estimation
in football games and potentially be an important complement to the image based features traditionally used.
15:00-17:10, Paper MoBT8.16
Coarse-To-Fine Multiclass Nested Cascades for Object Detection
Verschae, Rodrigo, Univ. de Chile
Ruiz-Del-Solar, Javier, Univ. de Chile
Building robust and fast object detection systems is an important goal of computer vision. A problem arises when several
object types are to be detected, because the computational burden of running several specific classifiers in parallel becomes
a problem. In addition the accuracy and the training time can be greatly affected. Seeking to provide a solution to these
problems, we extend cascade classifiers to the multiclass case by proposing the use of multiclass coarse-to-fine (CTF)
nested cascades. The presented results show that the proposed system scales well with the number of classes, both at
training and running time.
15:00-17:10, Paper MoBT8.17
Visual SLAM with an Omnidirectional Camera
Rituerto, Alejandro, Univ. de Zaragoza
Puig, Luis, Univ. de Zaragoza
Guerrero, Jose J., Univ. de Zaragoza
In this work we integrate the Spherical Camera Model for catadioptric systems in a Visual-SLAM application. The Spherical
Camera Model is a projection model that unifies central catadioptric and conventional cameras. To integrate this model
into the Extended Kalman Filter-based SLAM we require to linearize the direct and the inverse projection. We have performed
an initial experimentation with omni directional and conventional real sequences including challenging trajectories.
The results confirm that the omni directional camera gives much better orientation accuracy improving the estimated camera
trajectory.
15:00-17:10, Paper MoBT8.18
Shape Index SIFT: Range Image Recognition using Local Features
Bayramoglu, Neslihan, Middle East Tech. Univ.
Alatan, A. Aydin, Middle East Tech. Univ.
Range image recognition gains importance in the recent years due to the developments in acquiring, displaying, and storing
such data. In this paper, we present a novel method for matching range surfaces. Our method utilizes local surface properties
and represents the geometry of local regions efficiently. Integrating the Scale Invariant Feature Transform (SIFT) with the
shape index (SI) representation of the range images allows matching of surfaces with different scales and orientations. We
apply the method for scaled, rotated, and occluded range images and demonstrate the effectiveness it by comparing the
previous studies.
15:00-17:10, Paper MoBT8.19
Windows Detection using K-Means in CIE-Lab Color Space
Recky, Michal, ICG TU Graz
Leberl, Franz, ICG TU Graz
In this paper, we present a method for window detection, robust enough to process complex facades of historical buildings.
This method is able to provide results even for facades under severe perspective distortion. Our algorithm is able to detect
many different window types and does not require a learning step. We achieve these features thanks to an extended gradient
projection method and introduction of a I color descriptor based on a k-means clustering in a CIE-Lab color space into the
- 42 -