Hierarchical Image Descriptions for Classification and Painting

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.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Mimicking Hand-Drawn Pencil Lines

For many applications such as architecture, early design sketches containing accurate line drawings can often mislead the target audience [SSRL96]. Approximate human-drawn sketches are typically accepted as a better way of demonstrating the fundamental design concepts. To this end we have designed an algorithm that creates lines that perceptually resemble human-drawn lines. Our algorithm works directly with input point data and a mathematical model of using a physically based model of human arm movement. Further, the algorithm does not rely on a database of human lines, nor does it require any input other than the end points of the lines to generate a line of arbitrary length. The algorithm will generate any number of aesthetically pleasing and natural looking lines, where each one is unique. The algorithm was designed by conducting various user studies on human line sketches, and analyzing the lines to produce basic heuristics. We found that an observational analysis of human lines made a bigger impact on the algorithm than a statistical analysis. A further study has shown that the algorithm produces lines that are perceptually indistinguishable from that of a hand-drawn straight pencil line