The overall argument this thesis makes is that topological object structures captured within hierarchical image descriptions are invariant to depictive styles and offer a level of abstraction found in many modern abstract artworks. To show how object structures can be extracted from images, two hierarchical image descriptions are proposed. The first of these is inspired by perceptual organisation; whereas, the second is based on agglomerative clustering of image primitives. This thesis argues the benefits and drawbacks of each image description and empirically show why the second is more suitable in capturing object strucutures. The value of graph theory is demonstrated in extracting object structures, especially from the second type of image description. User interaction during the structure extraction process is also made possible via an image hierarchy editor. Two applications of object structures are studied in depth. On the computer vision side, the problem of object classification is investigated. In particular, this thesis shows that it is possible to classify objects regardless of their depictive styles. This classification problem is approached using a graph theoretic paradigm; by encoding object structures as feature vectors of fixed lengths, object classification can then be treated as a clustering problem in structural feature space and that actual clustering can be done using conventional machine learning techniques. The benefits of object structures in computer graphics are demonstrated from a Non-Photorealistic Rendering (NPR) point of view. In particular, it is shown that topological object structures deliver an appropriate degree of abstraction that often appears in well-known abstract artworks. Moreover, the value of shape simplification is demonstrated in the process of making abstract art. By integrating object structures and simple geometric shapes, it is shown that artworks produced in child-like paintings and from artists such as Wassily Kandinsky, Joan Miro and Henri Matisse can be synthesised and by doing so, the current gamut of NPR styles is extended. The whole process of making abstract art is built into a single piece of software with intuitive GUI.

Thesis (Ph.D.)--Columbia University, 2008. / Adviser: Shree K. Nayar. Includes bibliographical references.

Thesis (M.S.)--Brigham Young University. Dept. of Computer Science, 2005. / Includes bibliographical references (p. 79-80).

An image is a two-dimensional representation of the three-dimensional world. Recovering the information which is lost in the process of image formation is one of the fundamental problems in Computer Vision. One approach to this problem involves generating and evaluating a succession of surface hypotheses, with the best hypothesis selected as the final estimate. The fitness of each hypothesis can be evaluated by comparing the reference images against synthetic images of the hypothesised surface rendered with the reference cameras. An infinite number of surfaces can recreate any set of reference images, so many approaches to the reconstruction problem recover the largest from this set of surfaces. In contrast, the approach we present here accommodates prior structural information about the scene, thereby reducing ambiguity and finding a reconstruction which reflects the requirements of the user. The user describes structural information by defining a set of primitives and relating them by parameterised transformations. The reconstruction problem then becomes one of estimating the parameter values that transform the primitives such that the hypothesised surface best recreates the reference images. Two appearance-based likelihoods which measure the hypothesised surface against the reference images are described. The first likelihood compares each reference image against an image synthesised from the same viewpoint by rendering a projection of a second image onto the surface. The second likelihood finds the ‘optimal’ surface texture given the hypothesised scene configuration. Not only does this process maximise photo-consistency with respect to all reference images, but it prohibits incorrect reconstructions by allowing the use of prior information about occlusion. The second likelihood is able to reconstruct scenes in cases where the first is biased. / http://proxy.library.adelaide.edu.au/login?url= http://library.adelaide.edu.au/cgi-bin/Pwebrecon.cgi?BBID=1330993 / Thesis (Ph.D.) -- University of Adelaide, School of Computer Science, 2008

Thesis (Ph.D.) -- University of Adelaide, School of Computer Science, 2008. / "December, 2007" Bibliography: p. 181-185. Also available in print form.

Thesis (Ph. D.)--University of Washington, 1997. / Vita. Includes bibliographical references (p. [117]-124).

Thesis (Ph. D.)--Carleton University, 2001. / Also available in electronic format on the Internet.

Thesis (MScIng)--University of Stellenbosch, 2005. / Appendix B on CD-ROM in pocket in book. Bibliography. Also available via the Internet.

Stop-motion animation is a traditional form of animation that has been practised for over 100 years. While the unique look and feel of stop-motion animation has been retained in modern productions, the production process has been modernised to take advantage of technological advancements. Modern stop-frame animation production integrates digital imaging technology and computational methods with traditional hand-crafted skills. This portfolio documents three projects undertaken at Aardman Animations, each investigated with the aim of improving efficiency in the stop-motion production process: - Rig removal is the removal of equipment, or ‘rigging’, used on set during stop-motion animation to hold characters or objects in unstable positions. All rigging captured in frames must be removed in post-production and currently manual methods are used which can be very time-consuming. The key task is to separate the character from the rig. In Chapter 2, I present a novel spatio-temporal segmentation algorithm for segmenting characters from stop-motion footage. The algorithm has been designed to work with stop-motion animated content, in contrast to other state of the art algorithms which struggled when tested on stop-motion footage. - Set shift is a problem which occurs when background items on set move subtly over the time taken to shoot a scene. For example, temperature and humidity changes can cause wood to warp during a weekend, changing the position of a background object the following week. These small ‘shifts’ are recorded in the footage and must be corrected in post-production. Chapter 3 describes the problem in detail, investigates potential solutions and explains why solving set shift automatically is a significant challenge. - Plasticine shading is required when a plasticine model has to be generated computationally. One motivation for producing footage computationally is that problems such as rig removal and set shift do not arise. In order to simulate plasticine accurately, the distinct reflectance model of this material must be known and reproduced. By collecting experimental data from plasticine samples and fitting parametric models, I have developed a bespoke surface shading model for plasticine (Chapter 4). This new model provides the best fit to the measured data when compared to existing state of the art surface shaders. It has been implemented into commercially used production systems, for use with existing rendering software. Advancing state of the art research is only one of the challenges when working in a production studio such as Aardman Animations. Additionally, findings must be integrated into the production pipeline. Chapter 5 discusses the challenges and constraints faced when conducting research in this environment. In order for stop-motion animation to remain competitive it is vital that production companies stay up-to-date with technological advancements in research areas that can contribute to their production processes. I conclude by discussing whether technological advancements can help Aardman Animations in improving the efficiency of their stop-motion production pipeline.

We investigate some problems commonly found when dealing with stereo images. Working within the context of visual effects for films, we explore software solutions to issues arising with stereo images captured on-set. These images originate from a wide variety of hardware which may or may not provide additional data support for post-production needs. Generic software solutions are thus greatly to be preferred. This dissertation documents contributions in the following three areas. Each project was undertaken at Double Negative and investigated with the aim of improving the post-production pipeline for 3D films. Colour matching is the process whereby the colours of one view from a stereo pair are matched with those of the other view. This process is necessary due to the fact that slight differences in hardware and viewing angle can result in some surprisingly large colour discrepancies. Chapter 3 presents a novel approach to colour matching between stereo pairs of images, with a new tool for visual effects artists given in section 6.2.Vertical alignment of stereo images is key to providing a comfortable experience for the viewer, yet we are rarely presented with perfectly aligned footage from the outset. In chapter 4 we discuss the importance of correcting misalignments for both the final audience and the artists working on these images. We provide a tool for correcting misalignments in section 6.3.Disparity maps are used in many areas of post-production, and so in chapter 5 we investigate ways in which disparity map generation can be improved for the benefit of many existing tools at Double Negative. In addition, we povide an extensive exploration of the requirements of 3D films in order to make them presentable in the cinema. Through these projects, we have provided improvements to the stereo workflow and shown that academic research is a necessary component of developing tools for the visual effects pipeline. We have provided new algorithms to improve the 3D experience for moviegoers, as well as artists, and conclude by discussing the future work that will provide further gains in the field.