Conference Program of WCICA 2012

Conference Program WCICA 2012
through the video frames. After that the step is the selection of the 20
optimal key facial points, those which change the most with changes in
expression. Since the distance of geometric features, a set of displacement
vectors, is of a high dimensions, it is mapped into a low dimensional
space, called feature space, by applying PCA expansion. Then
estimation of input image is achieved by projecting it on to the feature
space. After build the feature space, we trained SVM classification and
tested it for result.
◁ PSaC-47
Sequence Detection of Planetary Surface Craters From DEM Data,
pp.4775–4779
Yu, Zhengshi
Zhu, Shengying
CUI, Pingyuan
Beijing Inst. of Tech.
Beijing Inst. of Tech.
Beijing Inst. of Tech.
The research on identification and recognition of impact craters on planetary
surface is focused on how to detect them from background. A novel
sequence algorithm is proposed to crater detection that utilizes DEM
data instead of images. By investigating the features of ideal craters,
several constraints can be developed to extract candidate crater edges
from other topographies. Based on the fact that the shape of most
craters is approximate to an ellipse, the Least Median Square Ellipse
Fitting Method can be used to exclude pseudo-edges, and to reserve
the real edges which contain the feature of the crater. The location,
orientation and other physical parameters of the crater can be determined
by fitting real edges to an ellipse based on Robust Least Square
Method. Mathematical simulations are performed with the moon DEM
data. The results show that the topography-based crater detection algorithm
offers an effective method for identification and characterization
of ellipse-like impact craters, and the accuracy is high enough.
◁ PSaC-48
A video tracking method based on Niche Particle Swarm Algorithm-
Particle Filter, pp.4780–4783
Li, Xin
Chen, Wenjie
Shang, Zengguang
Beijing Inst. of Tech.
Beijing Inst. of Tech.
The Chinese people’s liberation army
In order to improve the stability and robustness in video tracking based
on particle filter. We proposed Niche Particle Swarm Algorithm-Particle
Filter (NPSA-PF) which applies Niche Particle Swarm Algorithm to the
re-sampling stage in particle filter. The ability of Niche Particle Swarm
Algorithm which improves the particles’local search ability and weakens
the information sharing between particles, effectively reduces the
tracking particles number and improves tracking stability and robustness.
We use it in video tracking and the performance is validated to
be effective.
◁ PSaC-49
An Efficient Approach of 3D Ear Recognition, pp.4784–4790
Wang, Kai
Mu, Zhichun
He, Zhijun
Univ. of Sci. & Tech. Beijing
School of Automation & Electrical Engineering,
Univ. of Sci. & Tech. Beijing
China Nuclear Power Engineering Co.,Ltd
The Iterative Closest Point(ICP) algorithm is usually used for 3D ear
recognition in the literatures. However, the high computational cost
of ICP limits the application of 3D ear biometrics. In this paper, we
present an efficient approach based on local feature and ICP for 3D
ear recognition. The local features are detected and represented with
LSP(Local Surface Patch), and used to compute the initial transformation
for matching with ICP. An elite preservation strategy is introduced
to refine the candidate gallery ears that a modified ICP algorithm with
kd-tree index, distance and uniqueness constraints is applied to. The
proposed approach achieved a rank-1 recognition rate 98.55% on Collection
J2 of UND biometrics datasets. Matching an ear with a gallery
requires only 1.73 sec on average.
◁ PSaC-50
A new method on Solving Correlation Dimension of Chaotic Timeseries,
pp.4820–4824
Qiao, Meiying
Ma, Xiaoping
China Univ. Mining & Tech.
China Univ. of Mining & Tech.
Traditional G-P algorithm exist two drawbacks in solving the correlation
dimension of chaotic time series. The one is the subjective existence to
determine scaleless range, the other is calculation error is large when
the amount of data is small. For two shortcomings, the fuzzy C-means
clustering is introduced to the G-P algorithm to determine the no-scales
range. Least-squares fitting method is used to find the saturation correlation
dimension value in determining the scalelesss range. Using different
amount of Loren and Rossler data, such as 500,1000,2000,5000
and 10000, verify the improved algorithm in this paper,. Simulation results
show that the error relatively small if the delay time is small when
the amount of 500, 1000 and 2000. With the length of data increases,
the cluster centre value of the slope relatively flat closer to their ideal
value. The conclusions are applicable to Lorenz and Rossler data.
◁ PSaC-51
3D Ear Modeling Based on SFS, pp.4837–4841
Liu, Cong
Mu, Zhichun
Wang, Kai
Zeng, Hui
Univ. of Sci. & Tech. Beijing
School of Automation & Electrical Engineering,
Univ. of Sci. & Tech. Beijing
Univ. of Sci. & Tech. Beijing
Univ. of Sci. & Tech. beijing
Ear recognition, by using ear for identification recognition, is a kind of
new biometric identification technology. Currently there are a lot of ear
recognition methods based on 2D images, while 3D data can provide
more information. 3D reconstruction methods based on multiple 2D images
have a common difficult which is to extract corresponding feature
points of different images. In this paper, SFS(Shape From Shading)
was used for 3D modeling by only one grayscale image. Light direction
was estimated by analyzing singular points, which refers to the greatest
grayscale point, in 2D grayscale image. In addition, in order to achieve
more accurate 3D modes, the abnormal high brightness of the cavity
of auricular concha is processed. The matching accuracy of the model
can reach to 84%. Therefore the experiments shown that the method
proposed in this paper is simple and effective to modeling 3D ear.
◁ PSaC-52
A Target Detection Method in Dynamic Scene Based on Harris Algorithm
with Sub-block Threshold, pp.4842–4847
Lu, Jinghua
LEI, Yinghui
Chen, Jie
Zhang, Juan
Beijing Inst. of Tech.
Beijing Inst. of Tech.
Beijing Inst. of Tech.
Beijing Inst. of Tech.
In this paper, an improved Harris algorithm for the target detection in dynamic
scene due to the camera motion is presented. First, a sub-block
thresholding method is proposed to solve the problem of uneven distribution
of corners detected by Harris algorithm. Then, the improved
Harris algorithm is used to extract feature points, which are used to
estimate the parameters of global motion with the random-max consistency
algorithm and the least-square method. Compensated by the
result parameters, the reference image together with the current image
are used to detect the target with the frame difference method. Experiment
results show that the algorithm can detect the moving target more
accurately in dynamic scene.
◁ PSaC-53
Robust Visual Tracking with Classifier-like Appearance Model and Entropy
Particle Filter, pp.4853–4858
Song, Yu
Li, Qingling
Yan, Deli
Kang, Yifei
Beijing Jiaotong Univ.
China Univ. of Mining & Tech., Beijing
Beijing Jiaotong Univ.
beijing jiaotong Univ.
The detection based visual tracker treats tracking as the object and its
surround background online classification problem. There are two
main difficult issues in this method: one is to specify exact labels for
the online samples, the other is to avoid template drift that caused by
wrong update of the classifier-like appearance model. To overcome
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