CityU Institutional Repository >
3_CityU Electronic Theses and Dissertations >
ETD - Dept. of Electronic Engineering >
EE - Doctor of Philosophy >
Please use this identifier to cite or link to this item:
|Title: ||Evolutionary algorithm-based affine-invariant matching of object shapes from broken boundaries|
|Other Titles: ||Ji yu jin hua suan fa zhi po sui wu jian lun kuo xian fang she bu bian xing pi pei|
|Authors: ||Situ, Wuchao ( 司徒武超)|
|Department: ||Department of Electronic Engineering|
|Degree: ||Doctor of Philosophy|
|Issue Date: ||2011|
|Publisher: ||City University of Hong Kong|
|Subjects: ||Optical pattern recognition.|
Image processing -- Digital techniques.
|Notes: ||CityU Call Number: TA1650 .S57 2011|
xii, 107 leaves : ill. 30 cm.
Thesis (Ph.D.)--City University of Hong Kong, 2011.
Includes bibliographical references (leaves -105)
|Abstract: ||Viewpoint invariant matching of object shapes is a fundamental but difficult problem
in computer vision. It pertains to the recognition or alignment of shapes that are
subject to certain geometric transformations caused by different viewing positions. In
practice, the images of objects captured by optical means are affected by the
illumination, as well as various kinds of artifacts; the objects often present fragmented,
and more severely, incomplete contours in shape. This imposes further complications
on the problem. This thesis reports the developments of several shape matching
schemes, which can work independently or corporately in identifying object images
that are captured under poor lighting conditions. Specifically, the proposed methods
can be applied in the matching of near-planar object shapes from broken boundaries,
and moreover, under noise contamination. One of the main emphases of my works, is
to maintain a high success rate in shape matching, and at the same time minimizing
the computation time.
For near-planar objects, the matching process can be posed as an optimization
problem in realising an affine transform that yields a best matching score between a
pair of contours. Along this direction, simple genetic algorithms (SGA) and particle
swarm optimization (PSO) have been proven effective. Despite the moderate
successes of these approaches, however, they present erratic performance amongst
different objects, with reduced success rates and prolonged computation times in
some cases. These shortcomings can be attributed to the lack of an initial
population/swarm community that can realise global solutions.
In this thesis, a solution to this problem is presented by integrating PSO with the
migrant principle (MP). The latter is analogous to immigrant policy in real-world
situations; it introduces a continuous influx of foreign candidates to the swarm
community to promote diversity, and therefore, exploration power in the population.
Evaluations show that the method is less sensitive to swarm size and can achieve high
success rates for all test samples based on a small swarm community.
To further enhance the performance, a novel scheme based on contour reconstruction
is also provided. This scheme enables the extraction of a closed outermost boundary
from a set of fragmented object points, and represents this boundary as a
one-dimensional sequence. The similarity between a pair of fragmented boundaries
can then be determined by searching three corresponding point pairs in the
one-dimensional sequences. This reduces the dimensions of the problem to three
(instead of six for the original set of affine parameters). Experimental results show
that the proposed method is considerably faster than previous schemes, and can realise
a high success rate in identifying matched contours. Furthermore, a method known as
labelled chamfer distance transform (LCDT) is proposed to improve computational
efficiency in contour reconstruction. LCDT enables faster generation of the distance
image and correspondence map. Compared with its parent scheme, the proposed
LCDT approach achieves up to an order of magnitude of acceleration in the entire
Apart from matching in a noise-free background, an attempt to match shapes under a
noisy setting is made, in which a scheme called successive erosion and distance
accumulation (SEDA) is proposed to alleviate the sensitivity of the distance images to
noise. Experimental evaluation demonstrates the SEDA scheme can achieve a high
success rate in identifying matched contours under moderate amount of noise
|Online Catalog Link: ||http://lib.cityu.edu.hk/record=b4086718|
|Appears in Collections:||EE - Doctor of Philosophy |
Items in CityU IR are protected by copyright, with all rights reserved, unless otherwise indicated.