Abstract book (pdf) - ICPR 2010

13:30-16:30, Paper TuBCT8.40
Shape Guided Maximally Stable Extremal Region (MSER) Tracking
Donoser, Michael, Graz Univ. of Tech.
Riemenschneider, Hayko, Graz Univ. of Tech.
Bischof, Horst, Graz Univ. of Tech.
Maximally Stable Extremal Regions (MSERs) are one of the most prominent interest region detectors in computer vision
due to their powerful properties and low computational demands. In general MSERs are detected in single images, but given
image sequences as input, the repeatability of MSER detection can be improved by exploiting correspondences between
subsequent frames by feature based analysis. Such an approach fails during fast movements, in heavily cluttered scenes and
in images containing several similar sized regions because of the simple feature based analysis. In this paper we propose an
extension of MSER tracking by considering shape similarity as strong cue for defining the frame-to-frame correspondences.
Efficient calculation of shape similarity scores ensures that real-time capability is maintained. Experimental evaluation
demonstrates improved repeatability and an application for tracking weakly textured, planar objects.
13:30-16:30, Paper TuBCT8.41
Locating People in Images by Optimal Cue Integration
Atienza-Vanacloig, Vicente, Pol. Univ. of Valencia
Rosell Ortega, Juan, Pol. Univ. of Valencia
Andreu-Garcia, Gabriela, Pol. Univ. of Valencia
Valiente, Jose Miguel, Pol. Univ. of Valencia
This paper describes an approach to segment and locate people in crowded scenarios with application to a surveillance system
for airport dependencies. To obtain robust operation, the system analyzes a variety of visual cues color, motion and shape
and integrates them optimally. A general method for automatic inference of optimal cue integration rules is presented. This
schema, based on supervised training on video sequences, avoids the need of explicitly formulate combination rules based
on a-priori constraints. The performance of the system is at least as good as classical fusing strategies like those based on
voting, because the optimized decision engine implicitly includes these and other strategies.
13:30-16:30, Paper TuBCT8.42
Visual Tracking Algorithm using Pixel-Pair Feature
Nishida, Kenji, National Inst. of Advanced Industrial Science and Tech.
Kurita, Takio, National Inst. of Advanced Industrial Science and Tech.
Ogiuchi, Yasuo, Sumitomo Electric Industries Ltd.
Higashikubo, Masakatsu, Sumitomo Electric Industries Ltd.
A novel visual tracking algorithm is proposed in this paper. The algorithm uses pixel-pair features to discriminate between
an image patch with an object in the correct position and image patches with an object in an incorrect position. The pixelpair
feature is considered to be robust for the illumination change, and also is robust for partial occlusion when appropriate
features are selected in every video frame. The tracking precision for a deforming object (skier) is examined and also the occlusion
detection method is described.
13:30-16:30, Paper TuBCT8.43
Self-Calibration of Radially Symmetric Distortion by Model Selection
Fujiki, Jun, National Inst. of Advanced Industrial Science and Tech.
Hino, Hideitsu, Waseda Univ.
Usami, Yumi, Waseda Univ.
Akaho, Shotaro, National Inst. of Advanced Industrial Science and Tech.
Murata, Noboru, Waseda Univ.
For self-calibration of general radially symmetric distortion (RSD) of omni directional cameras such as fish-eye lenses, calibration
parameters are usually estimated so that curved lines, which are supposed to be straight in the real-world, are mapped
to straight lines in the calibrated image, which is assumed to be taken by an ideal pin-hole camera. In this paper, a method
of calibrating RSD is introduced base on the notion of principal component analysis (PCA). In the proposed method, the
distortion function, which maps a distorted image to an ideal pin-hole camera image, is assumed to be a linear combination
of a certain class of basis functions, and an algorithm for solving its coefficients by using line patterns is given. Then a
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