| dc.description |
The aim of the present work was to build a novel 3D object recognition system capable of classifying
man-made and natural objects based on single 2D views. The approach to this problem
has been one motivated by recent theories on biological vision and multiresolution analysis. The
project's objectives were the implementation of a system that is able to deal with simple 3D
scenes and constitutes an engineering solution to the problem of 3D object recognition, allowing
the proposed recognition system to operate in a practically acceptable time frame.
The developed system takes further the work on automatic classification of marine phytoplank-
(ons, carried out at the Centre for Intelligent Systems, University of Plymouth. The thesis discusses
the main theoretical issues that prompted the fundamental system design options. The
principles and the implementation of the coarse data channels used in the system are described.
A new multiresolution representation of 2D views is presented, which provides the classifier
module of the system with coarse-coded descriptions of the scale-space distribution of potentially
interesting features. A multiresolution analysis-based mechanism is proposed, which directs
the system's attention towards potentially salient features. Unsupervised similarity-based
feature grouping is introduced, which is used in coarse data channels to yield feature signatures
that are not spatially coherent and provide the classifier module with salient descriptions of object
views. A simple texture descriptor is described, which is based on properties of a special wavelet
transform.
The system has been tested on computer-generated and natural image data sets, in conditions
where the inter-object similarity was monitored and quantitatively assessed by human subjects,
or the analysed objects were very similar and their discrimination constituted a difficult task even
for human experts. The validity of the above described approaches has been proven. The studies
conducted with various statistical and artificial neural network-based classifiers have shown that
the system is able to perform well in all of the above mentioned situations. These investigations
also made possible to take further and generalise a number of important conclusions drawn during
previous work carried out in the field of 2D shape (plankton) recognition, regarding the behaviour
of multiple coarse data channels-based pattern recognition systems and various classifier
architectures.
The system possesses the ability of dealing with difficult field-collected images of objects and
the techniques employed by its component modules make possible its extension to the domain
of complex multiple-object 3D scene recognition. The system is expected to find immediate applicability
in the field of marine biota classification. |
|