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.
Date of Award | 1 Mar 2009 |
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Original language | English |
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Awarding Institution | |
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Supervisor | Peter Hall (Supervisor) |
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- Computer vision
- computer graphics
Hierarchical Image Descriptions for Classification and Painting
Song, Y. (Author). 1 Mar 2009
Student thesis: Doctoral Thesis › PhD