Abstract book (pdf) - ICPR 2010

and iterations. To label object candidates, we build a tree-structure database of object classes, which captures latent patterns
in shape of 3D objects in a hierarchical manner. We demonstrate our system on the aerial LIDAR dataset acquired from a
few square kilometers of Ottawa.
13:30-16:30, Paper WeBCT8.44
Data-Driven Foreground Object Detection from a Non-Stationary Camera
Sun, Shih-Wei, Acad. Sinica, Taiwan
Huang, Fay, National Ilan Univ. Taiwan
Liao, Mark, Acad. Sinica, Taiwan
In this paper, we propose a data-driven foreground object detection technique which can detect foreground objects from
a moving camera. We propose to build a data-driven consensus foreground object template (CFOT) and then detect the
foreground object region in each frame. The proposed foreground object detection technique is equipped with the following
functions: (1) the ability to detect the foreground object captured by a fast moving camera ; (2) the ability to detect a low
contrast (spatially/temporally) foreground object; and (3) the ability to detect a foreground object from a dynamic background.
There are three contributions of our method: (1) a newly proposed data-driven foreground region decision process
for generating the CFOT has been shown robust and efficient; (2) a foreground object probability is proposed for properly
dealing with the imperfect initial foreground region estimations; and (3) a CFOT is generated for precise foreground object
detection.
13:30-16:30, Paper WeBCT8.45
Efficient Shape Retrieval under Partial Matching
Demirci, Fatih, TOBB Univ. of Ec. and Tech.
Indexing into large database systems is essential for a number of applications. This paper presents a new indexing structure,
which overcomes an important restriction of a previous indexing technique using a recently developed theorem from the
domain of matrix analysis. Specifically, given a set of distance values computed by distance function, which do not necessarily
satisfy the triangle inequality, this paper shows that computing its nearest distance values that obey the properties
of a metric enables us to overcome the limitations of the previous indexing algorithm. We demonstrate the proposed framework
in the context of a recognition task.
13:30-16:30, Paper WeBCT8.46
Component Identification in the 3D Model of a Building
Xu, Mai, Imperial Coll.
Petrou, Maria, Imperial Coll.
Jahangiri, Mohammad, Imperial Coll.
This paper addresses the problem of identifying the components (such as balconies and windows) of the 3D model of a
building. A novel method, based on a voting scheme, is presented for solving such a problem. It is intuitive that interference
(such as shadows and occlusions) rarely happen at the same place or at different times when looking at a scene from different
directions. In the spirit of this intuition, the voting-based method combines the information from various images to
identify and segment the components of a building.
13:30-16:30, Paper WeBCT8.48
Multi-Scale Color Local Binary Patterns for Visual Object Classes Recognition
Zhu, Chao, Ec. Centrale de Lyon
Bichot, Charles-Edmond, Ec. Centrale de Lyon
Chen, Liming, Ec. Centrale de Lyon
The Local Binary Pattern (LBP) operator is a computationally efficient yet powerful feature for analyzing local texture
structures. While the LBP operator has been successfully applied to tasks as diverse as texture classification, texture segmentation,
face recognition and facial expression recognition, etc., it has been rarely used in the domain of Visual Object
Classes (VOC) recognition mainly due to its deficiency of power for dealing with various changes in lighting and viewing
conditions in real-world scenes. In this paper, we propose six novel multi-scale color LBP operators in order to increase
photometric invariance property and discriminative power of the original LBP operator. The experimental results on the
- 223 -