Abstract book (pdf) - ICPR 2010

some applications such as stereo matching and optical flow. In continuous formulation, however, it is much more difficult
to optimize the target functions. In this paper, we propose a new method called fusion move driven Markov Chain Monte
Carlo method (MCMC-F) that combines the Markov Chain Monte Carlo method and the fusion move to solve continuous
MRF problems effectively. This algorithm exploits powerful fusion move while it fully explore the whole solution space.
We evaluate it using the stereo matching problem. We empirically demonstrate that the proposed algorithm is more stable
and always finds lower energy states than the state-of-the art optimization techniques.
14:10-14:30, Paper TuBT1.3
Approximate Belief Propagation by Hierarchical Averaging of Outgoing Messages
Ogawara, Koichi, Kyushu Univ.
This paper presents an approximate belief propagation algorithm that replaces outgoing messages from a node with the
averaged outgoing message and propagates messages from a low resolution graph to the original graph hierarchically. The
proposed method reduces the computational time by half or two-thirds and reduces the required amount of memory by
60% compared with the standard belief propagation algorithm when applied to an image. The proposed method was implemented
on CPU and GPU, and was evaluated against Middlebury stereo benchmark dataset in comparison with the
standard belief propagation algorithm. It is shown that the proposed method outperforms the other in terms of both the
computational time and the required amount of memory with minor loss of accuracy.
14:30-14:50, Paper TuBT1.4
Cascaded Background Subtraction using Block-Based and Pixel-Based Codebooks
Guo, Jing-Ming, National Taiwan Univ. of Science and Tech.
Chih-Sheng Hsu, Sheng, National Taiwan Univ. of Science and Tech.
This paper presents a cascaded scheme with block-based and pixel-based codebooks for background subtraction. The
codebook is mainly used to compress information to achieve high efficient processing speed. In the block-based stage, 12
intensity values are employed to represent a block. The algorithm extends the concept of the Block Truncation Coding
(BTC), and thus it can further improve the processing efficiency by enjoying its low complexity advantage. In detail, the
block-based stage can remove the most noise without reducing the True Positive (TP) rate, yet it has low precision. To
overcome this problem, the pixel-based stage is adopted to enhance the precision, which also can reduce the False Positive
(FP) rate. Moreover, this study also presents a color model and a match function which can classify an input pixel as
shadow, highlight, background, or foreground. As documented in the experimental results, the proposed algorithm can
provide superior performance to that of the former approaches.
14:50-15:10, Paper TuBT1.5
Moving Cast Shadow Removal based on Local Descriptors
Qin, Rui, Chinese Acad. of Sciences
Liao, Shengcai, Chinese Acad. of Sciences
Lei, Zhen, Chinese Acad. of Sciences
Li, Stan Z., Chinese Acad. of Sciences
Moving cast shadow removal is an important yet difficult problem in video analysis and applications. This paper presents
a novel algorithm for detection of moving cast shadows, that based on a local texture descriptor called Scale Invariant
Local Ternary Pattern (SILTP). An assumption is made that the texture properties of cast shadows bears similar patterns
to those of the background beneath them. The likelihood of cast shadows is derived using information in both color and
texture. An online learning scheme is employed to update the shadow model adaptively. Finally, the posterior probability
of cast shadow region is formulated by further incorporating prior contextual constrains using a Markov Random Field
(MRF) model. The optimal solution is found using graph cuts. Experimental results tested on various scenes demonstrate
the robustness of the algorithm.
TuBT2 Topkapı Hall A
Feature Extraction – I Regular Session
Session chair: Franke, Katrin (Gjøvik Univ. College)
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