Learning Non-linear Models of
Shape and Motion
Deformable models have been an active
area of research in computer vision for a number of years. Their ability to model
non-ridgid objects through the combination of geometry and physics has proven a valuable
tool in image processing. More recently a class of deformable objects known as Point
Distribution Models or Eigen Models have been introduced. These statistical models of
deformation overcome some of the shortfalls of earlier deformable models by learning what
is 'allowable' deformation, for an object class, from a training set of examples. This
semi-automated learning procedure provides a more generic approach to object recognition,
tracking and classification. Their strength lies in their simplicity and speed of
operation, allowing the robust ability to model complex deformations in cluttered
environments. However, the automated construction of such models leads to a breakdown of
the fundamental assumptions upon which they are based. Primarily, that the underlying
mathematical model is linear in nature. Furthermore, as more complex objects are
considered, these assumptions fail completely and what is produced is an unreliable model.
This work addresses these problems and
presents novel techniques for the automated construction and application of non-linear
deformable models, which retain the speed, and simplicity of the linear Point Distribution
Model. It is further shown how these non-linear models can be augmented with probabilistic
temporal constraints, which are essential in object tracking and classification.
This work presents, in essence, three
developments to the field. Firstly, a piecewise linear approach to modelling non-linearity
is proposed and results demonstrated that show its accuracy in modelling both low and high
dimensional datasets with heavy non-linearity. The technique is then extended to the
automated construction of models. Secondly, it is shown how the piecewise approach can be
augmented with temporal constraints and used in both model prediction, animation and for
the support of multiple hypotheses during tracking. It is further shown how these temporal
models can be extended to incorporate information from other sources, providing more
reliable tracking in the absence of complete training data. Thirdly, it is shown how
elements can be combined statistically and used to infer information about an object from
its shape alone. Using human motion capture as an example, it is demonstrated that models
can be assembled which allow 3D structural information about body pose and motion to be
inferred from a monoscopic image sequence using only natural features of the body as
Welcome to my thesis download page.
The thesis is available in pdf as a
complete document or as individual chapters.
It uses colour extensively throughout so if you
are planning on printing in black and white some of the figures may not be too clear.
non-linear Models of Shape and Motion
Table of Contents
Table of Figures
Point Distribution Models
Tracking Using Colour
Based non-linear Point Distribution Models
Constraints on Shape Space
the Point Distribution Model
and Fuzzy k-means Clustering
This document and its parts are covered by copyright and no
repoduction of the document or content may be made without the permission of the author.
Any reference to this work must contain an appropriate reference to the author.
© Richard Bowden 2000