Abstract book (pdf) - ICPR 2010

complexity of the co-association matrix. The sparseness of the matrix is related to the construction of the clustering ensemble.
Using a split and merge strategy combined with a sparse matrix representation, we empirically show that a linear
space complexity is achievable in this framework, leading to the scalability of EAC method to clustering large data-sets.
10:00-10:20, Paper TuAT2.4
A Hierarchical Clustering Method for Color Quantization
Zhang, Jun, Waseda Univ.
Hu, Jinglu, Waseda Univ.
In this paper, we propose a hierarchical frequency sensitive competitive learning (HFSCL) method to achieve color quantization
(CQ). In HFSCL, the appropriate number of quantized colors and the palette can be obtained by an adaptive procedure
following a binary tree structure with nodes and layers. Starting from the root node that contains all colors in an
image until all nodes are examined by split conditions, a binary tree will be generated. In each node of the tree, a frequency
sensitive competitive learning (FSCL) network is used to achieve two-way division. To avoid over-split, merging condition
is defined to merge the clusters that are close enough to each other at each layer. Experimental results show that HFSCL
has the desired ability for CQ.
10:20-10:40, Paper TuAT2.5
Combining Real and Virtual Graphs to Enhance Data Clustering
Wang, Liang, The Univ. of Melbourne
Leckie, Christopher, The Univ. of Melbourne
Kotagiri, Rao, Univ. of Melbourne
Fusion of multiple information sources can yield significant benefits to accomplishing certain learning tasks. This paper
exploits the sparse representation of signals for the problem of data clustering. The method is built within the framework
of spectral clustering algorithms, which convexly combines a real graph constructed from the given physical features with
a virtual graph constructed from sparse reconstructive coefficients. The experimental results on several real-world data
sets have shown that fusion of both real and virtual graphs can obtain better (or at least comparable) results than using
either graph alone.
TuAT3 Topkapı Hall A
3D Shape Recovery Regular Session
Session chair: Sato, Jun (Nagoya Institute of Technology)
09:00-09:20, Paper TuAT3.1
Calibration Method for Line Structured Light Vision Sensor based on Vanish Points and Lines
Wei, Zhenzhong, Beihang Univ.
Xie, Meng, Beihang Univ. Ministry of Education
Zhang, Guangjun, Beihang Univ.
Line structured light vision sensor (LSLVS) calibration is to establish the location relationship between the camera and
the light plane projector. This paper proposes a geometrical calibration method of LSLVS based on the property of vanish
points and lines, by randomly moving the planar target. This method contains two steps, (1) the vanish point of the light
stripe projected by the light plane is found in each target image, and all the obtained vanish points form the vanish line of
the light plane, which is helpful to determine the normal of the light plane. (2) one 3D feature point on the light plane is
acquired (one is enough, surely can be more than one) to determine d parameter of the light plane. Then the equation of
the light plane under the camera coordinate system can be solved out. Computer simulations and real experiments have
been carried out to validate our method, and the result of the real calibration reaches the accuracy of 0.141mm within the
view field of about 300mm×200mm.
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