Conference Program of WCICA 2012

WCICA 2012
Book of Abstracts: Saturday Sessions
studied. The effectiveness and preciseness of the proposed model and
corresponding controllers are verified via numerical simulations, with
high energy efficiency concurrently.
◁ PSaC-40
M-Nearest Neighbor Selection for Two-Phase Test Sample Representation
in Face Recognition, pp.4661–4666
Ma, Xin Jun
Wu, Ning
Liang, TianCai
Harbin Inst. of Tech. Shenzhen Graduate School
Harbin Inst. of Tech.
South China Univ. of Tech.
The Two-Phase Test Sample Representation (TPTSR) has been proposed
as a powerful algorithm for face recognition. In TPTSR processing,
a classification task is divided into two steps. The first phase determines
M nearest neighbors to the testing sample from the training set
by a linear representation criterion, and the second phase classifies the
testing sample into the class with the representative linear combination
by the selected nearest neighbors in the first phase. However, the computational
load for this method is relatively demanding, especially for a
large training set and big number of classes. This paper studies alternative
nearest neighbor selection criterions for the first phase of TPTSR,
such as the Euclidean distance and City-block distance. Experimental
results and theoretical analysis show that computational load can be
significantly reduced by these relatively more straightforward criterions
while maintaining a comparable classification performance with the
original TPTSR method.
◁ PSaC-41
Affine Motion Segmentation from Feature Point Trajectories using Rank
Minimization, pp.4667–4670
YANG, Min
Nanjing Univ. of Posts & Telecommunications
In this paper, we examine the problem of segmenting tracked feature
point trajectories of multiple moving objects in an image sequence. Using
the affine camera model, this motion segmentation problem can be
cast as the problem of segmenting samples drawn from a union of linear
subspaces. We pose this problem as a rank minimization problem,
where the goal is to decompose the corrupted data matrix as the sum of
a low-rank dictionary plus a matrix of noise. Given a set of data vectors,
low rank representation seeks the lowest rank representation among all
the linear combination of the bases in a dictionary. For noisy data, this
non-convex problem can be solved very efficiently in the inexact Augmented
Lagrange Multiplier method. Our algorithm amounts to an SVD
of the data matrix and a shrinkage-thresholding of its singular values.
We have experimented on real image sequence, where we show good
segmentation result, comparable to the state-of-the-art in literature.
◁ PSaC-42
A Simple String Matching Method for Shape Recognition, pp.4696–
4700
Wu, Wen-Yen
I-Shou Univ.
Shape recognition is an important problem in many applications. A simple
string matching approach for shape recognition is proposed in this
paper. The shapes are coded as their dominant points. The compactness
of polygons formed by the centroid and three consecutive dominant
points is used as the feature for recognition. The experimental
results showed that the proposed method has better recognition rates
and more consistent performance than that of using the conventional
features. Further, the proposed method doesn’t need to set parameters,
so that it is robust in the shape recognition.
◁ PSaC-43
Pose Detection of Partly Covered Target in the Micro-Vision System,
pp.4721–4725
Su, Jin
Huang, Xinhan
Wang, Min
Huazhong Univ. of Sci. & Tech.
Huazhong Univ. 0f Sci. & Tech.
Huazhong Univ. of Sci. & Tech.
Abstract - In this paper, we focus on the need for location and pose
detection of partly covered target. In micro-vision system, the accuracy
of location and pose detection directly decides the effect of latter
micro-operation. Former methods can’t locate the target which
was partly covered. According to the current problem in micro-vision
system, this paper proposes a method of target location and pose detection
in micro-vision system which based on Hough transform and
template matching. For the aspect ratio and area of target is certain,
by detecting the shape feature and using template matching, we detect
the location of target which was partly covered. Firstly, use Canny algorithm
and morphology method to obtain the edge of objects. Then,
use the improved Hough transform algorithm to extract line segments
features and locate the candidate target region according to template
matching. Finally, recognize and locate the target by detecting whether
the two segments are connected to be a signal region. After recognizing
target, the slant angle of the target is detected based on Hough
transform. This method can exactly locate the target and get the angle.
The experimental results show the proposed method is better than the
former ones under complex background and partly covered targets.
◁ PSaC-44
Air-Ground Vehicle Detection using Local Feature Learning and Saliency
Region Detection, pp.4726–4731
Xu, Qinghan
JIN, Lizuo
Fei, Shumin
Jie, Feiran
Southeast Univ.
Southeast Univ.
Southeast Univ.
Sci. & Tech. on Electro-optic Control Laboratory
Moving vehicle detection is very important for urban traffic surveillance
and situational awareness on the battlefield. Algorithms with cascade
structure like Adaboost are booming in the recent decade, and successful
in real-time application. But most of them use a sliding window
protocol on multi-scale images, this involve heavy computing, so only
simple feature (e.g. Harr wavelet) is suitable.
In this paper, a biologically inspired method is proposed. We learn
patch-based features for vehicle detection by unsupervised learning,
and then employ a visual saliency step after feature extraction. Instead
of sliding window, a candidate region is sent to classifier only if its features
are “salient” on whole image. As the number of candidate regions
decreases dramatically, it allow us to utilize complex feature to increase
description ability. Experimental result indicates less computational expense
and good performance.
◁ PSaC-45
An Indoor Quadrotor Locating and Object-Following Algorithm using
Monocular Vision, pp.4747–4753
Chen, Xiaolong
Tang, Qiang
Che, Jun
Flight Automatic Control Research Inst.
Flight Automatic Control Research Inst.
Flight Automatic Control Research Inst.
Using quadrotor as an indoor robot asks for accurate locating and control
methods. To solve this problem, a composite algorithm combining
MIMU(Micro Inertial Measurement Unit) and monocular vision is used.
The algorithm uses the measurement of MIMU as the source of position
updating, while monocular vision algorithm provides the detection
result of the feature of reference lines on the ground. The location of
the reference lines detected is then compared with the result of MIMU,
and the combination of these two results brings more accurate locating
result for quadrotor. Compared to using MIMU, the error can decrease
from ±50cm to ±10cm when the height of quadrotor is 1m. An object
following platform is used for validating the composite algorithm,
and system identification method is used for modeling the AR.Drone.
Trajectory following algorithm and object following method are also developed.
The scene of an AR.Drone searching at the navigation area
and following the UGV is presented at last.
◁ PSaC-46
Facial Expression Recognition in Video Sequences, pp.4766–4770
Wan, Chuan
Tian, Yantao
Liu, Shuaishi
Jilin Univ.
Jilin Univ.
Jilin Univ.
This paper describes a method for recognition of continuous facial expression
change in video sequences. ASM automatically localizes the
facial feature points in the first frame and then tracks the feature points
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