Conference Program of WCICA 2012

Conference Program WCICA 2012
then locating eyes correctly according to geometry and pixel features
of human eyes. Experimental results show that this algorithm can be
applicable in images with different backgrounds and Non-uniform illumination
environment. It has proved that it is real-time and accurate.
◁ PSaC-76
Adaptive Fuzzy Apporach to Background Modeling using PSO and
KLMS, pp.4601–4607
Li, Zilong
South China Univ. of Tech.
This paper presents a new adaptive fuzzy approach for background estimation
in video sequences of complex scene from the function estimation
point of view. A Takagi-Sugeno-Kang (TSK) type fuzzy system is
used as the function approximator in the study. The proposed approach
uses a hybrid learning method combines both the particle swarm optimization
(PSO) and the Kernel Least Mean Square (KLMS) to train the
fuzzy approximator. In order to estimate background, we first interpret
foreground samples as outliers relative to the background ones and so
propose an Outlier Separator (OS). Then, the obtained results of OS
algorithm are employed in the fuzzy approximator in order to train and
estimate background in each pixel. Experimental results show the high
accuracy and effectiveness of the proposed method in background estimation
and foreground detection for various scenes.
◁ PSaC-77
Optimal Motion Control for IBVS of Robot, pp.4608–4611
Gao, Cheng
Univ.
In conventional image-based visual servoing (IBVS), the robot endeffector
(camera) motion is controlled directly according to image error,
there isn’t direct control over the Cartesian velocities of the robot endeffector.
As a result, the robot trajectories can be seemingly roundabout
in Cartesian space. This paper presents a new control scheme, that is
IBVS of rotation separated. As there is not interferences of translation
and rotation of image, the performance of this approach is more advantage
in the setting times and the motion trajectories of Cartesian space
than classical IBVS, and can executes visual servoing task that conventional
IBVS can’t accomplished. We illustrate new control scheme
with two representative simulation result.
◁ PSaC-78
Machine-Vision Based Preceding Vehicle Detection Algorithm: A Review,
pp.4617–4622
Zhou, Jun-jing
Duan, JianMin
Yu, Hongxiao
Beijing Univ. of Tech.
Beijing Univ. of Tech.
Beijing Univ. of Tech.
Onboard vehicle detection system is of great importance to reduce vehicle
collision accident and increase the driving safety on road. It aims
at detecting vehicles appearing around the ego vehicle using vehiclemounted
camera, so as to alert the driver about driving environments
and possible collision with other vehicles. In this paper, we analyze the
detail difficulties lying in the problem and review most of the literatures.
A typical vehicle detection algorithm includes two steps: hypothesis
generation and hypothesis verification. After a vehicle is detected, it’s
tracked. This paper introduces the principle of typical methods of detection
and tracking and analyzes their respective pros and cons. Finally,
we propose some research directions in the future.
◁ PSaC-79
Traffic Sign recognition Using Dual Tree-Complex Wavelet Transform
and 2D Independent Component Analysis, pp.4623–4627
Gu, Mingqin
Cai, Zi-xing
Central South Univ.
Central South Univ.
A novel traffic sign recognition algorithm is presented in the paper. This
algorithm integrates the Dual-Tree Complex Wavelet Transform(DT-
CWT) representation of traffic sign images and 2D Independent Component
Analysis(2DICA) method. First traffic sign color-image is preprocessed
with gray scaling, and normalizing to 6464 size.
Then four levels DT-CWT images are used to represent gray image of
traffic sign, so the image features could be obtained. Second, 2DICA
and nearest neighbor classifier are used to recognize the traffic signs.
The whole recognition algorithm is implemented for classification of 50
categories of traffic signs and accuracy reach 97%. It also compares
the presented algorithm with well-established image representation like
template, Gabor, and feature selection techniques such as PCA, LPP,
2DPCA at same time. Experimental results indicate that the proposed
algorithm was robust, effective, and accurate to recognize traffic signs.
◁ PSaC-80
Feature Detection and Matching for Traffic Sign Images, pp.4628–4632
Li, Lei-Min
LI, Li
Tong, Ru-qiang
Li, Pei-xi
Southwest Univ. of Sci. & Tech.
Changchun Univ. of Sci. & Tech.
School of Information Engineering , Southwest
Univ. of Sci. & Tech.
Changchun Univ. of Sci. & Tech.
It is important to detect and recognize the traffic sign for mobile robot
localization and navigation. In this paper, an algorithm frame of feature
detection and matching has been developed which includes shape
detection, Harris corner detection, SIFT feature matching and robust
estimation method. Firstly, the color threshold segmentation algorithm
in RGB color space is adopted to get the candidate region of traffic signs
and the region growing method is applied to remove the noise in this
image. Secondly, the shape features on the edge image are detected
using template matching. Thirdly, Harris corner features are calculated
and sorted, then the SIFT feature descriptors are computed on the
extraction corner points. Finally, according to the minimum Euclidean
distance the matching characteristic vectors are obtained between t-
wo images􀋈then random sampling algorithm with robust estimation
is used to reduce mismatch. Experiment result shows that this
algorithm is efficient.
◁ PSaC-81
Machine Vision Based Localization of Intelligent Vehicle, pp.4638–4643
Wang, Fei
Duan, Jianmin
ZHENG, Banggui
Beijing Univ. of Tech.
Beijing Univ. of Tech.
Beijing Univ. of Tech.
Aiming at implementing the lane recognition and determining the host
vehicle’s parameters of position and direction accurately according to
lane line parameters, realize the lane departure warning, one kind of
practical algorithm for lane recognition and the host vehicle localization
has been proposed. In the algorithm, lane parameters are obtained using
Hough Transformation. Combined the inner and outer parameters
of CCD and lane parameters, the parameters such as the host vehicle’s
location parameter, the direction variable and the lane width are
obtained using the coordinate transformation. The experiments indicated
that this algorithm has the good adaptive ability and anti-jamming
ability when roadway structure and illumination condition changes. It
can satisfy many kinds of initiative safety system’s requests in a certain
extent.
◁ PSaC-82
Divide and Conquer Strategy for Spectral Clustering, pp.4644–4648
Jia, Zhixian
Xinjiang Univ. of Finance & Economics
The spectral clustering algorithm’s space complexity is O(n-squared),
while time complexity is O(n-cubed). When dealing with large amounts
of data, the memory will overflow and run-time is too long. For the general
problem of spectral clustering, if the clustering data of sub-problem
between the original problem has the same probability distribution, it
can be applied to divide and conquer strategy for the problem of spectral
clustering, by the spectral clustering results of sub-problems to get
the spectral clustering results of original problem. To spectral clustering
image segmentation as a research object, we will discuss the divide
and conquer strategy for spectral clustering in this paper. Experiments
show that the application of divide and conquer method for spectral
clustering image segmentation, we can get a perfect performance in
image segmentation.
◁ PSaC-83
Improved Algorithm for the k-means Clustering, pp.4717–4720
ZHANG, SHENG
Shandong Jiaotong Univerisity
202