Abstract book (pdf) - ICPR 2010

09:00-11:10, Paper WeAT8.18
Active Contours with Thresholding Value for Image Segmentation
Chen, Gang, Chinese Acad. of Sciences
Zhang, Haiying, Chinese Acad. of Sciences
Chen, Iron, Chinese Acad. of Sciences
Yang, Wen, Wuhan Univ.
In this paper, we propose an active contour with threshold value to detect objects and at the same time get rid of unimportant
parts rather than extract all information. The basic ideal of our model is to introduce a weight matrix into region-based
active contours, which can enhance the weight for the main parts while filter the weak intensity, such as shadows, illumination
and so on. Moreover, we can choose threshold value to set weight matrix manually for accurate image segmentation.
Thus, the proposed method can extract objects of interest in practice. Coupled partial differential equations are used to
implement this method with level set algorithms. Experimental results show the advantages of our method in terms of accuracy
for image segmentation.
09:00-11:10, Paper WeAT8.19
An Iterative Method for Superresolution of Optical Flow Derived by Energy Minimisation
Mochizuki, Yoshihiko, Chiba Univ.
Kameda, Yusuke, Chiba Univ.
Imiya, Atsushi, IMIT, Chiba Univ.
Sakai, Tomoya, Chiba Univ.
Super resolution is a technique to recover a high resolution image from a low resolution image. We develop a variational
super resolution method for the subpixel accurate optical flow computation using variational optimisation. We combine
variational super resolution and the variational optical flow computation for the super resolution optical flow computation.
09:00-11:10, Paper WeAT8.20
Non-Rigid Image Registration for Historical Manuscript Restoration
Wang, Jie, National Univ. of Singapore
Tan, Chew-Lim, National Univ. of Singapore
This paper presents a non-rigid registration method for the restoration of double-sided historical manuscripts. Firstly, the
gradient direction maps of the two images of a manuscript are examined to identify candidate control points. Then the
correspondences of these points are established by minimizing a disimilarity measure consisting of intensity, gradient and
displacement. To fully capture the spatial relationship between the two images, a mapping function is defined as the combination
of a global affine and local b-splines transformation. The cost function for optimization consists of two parts:
normalized mutual information for the goal of similarity and space integral of the square of the second order derivatives
for smoothness. To evaluate the proposed method, a wavelet based restoration procedure is applied to registered images.
Real documents from the National Archives of Singapore are used for testing and the experimental results are impressive.
09:00-11:10, Paper WeAT8.21
An Effective Decentralized Nonparametric Quickest Detection Approach
Yang, Dayu, Univ. of Tennessee
Qi, Hairong, Univ. of Tennessee
This paper studies decentralized quickest detection schemes that can be deployed in a sensing environment where data
streams are simultaneously collected from multiple channels located distributively to jointly support the detection. Existing
decentralized detection approaches are largely parametric that require the knowledge of pre-change and post-change distributions.
In this paper, we first present an effective nonparametric detection procedure based on Q-Q distance measure.
We then describe two implementations schemes, binary quickest detection and local decision fusion by majority voting,
that realize decentralized nonparametric detection. Experimental results show that the proposed method has a comparable
performance to the parametric CUSUM test in binary detection. Its decision fusion-based implementation also outperforms
the other three popular fusion rules under the parametric framework.
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