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The writer defines graphics-oriented data types and operators as the basis for a graphics-oriented programming language. Most of these data types are not available in today's languages. They are usually defined as structures or records composed of some other basic types. These include points, rectangles, transformations, etc. Some operators, such as vector addition, are defined to work with them.The design and implementation of an interpreter to test some of these graphics data types and operators are discussed.The writer also examines some of the tools needed in a graphics programming environment.

In this thesis visual search experiments are devised to explore the feasibility of an eye gaze driven search mechanism. The thesis first explores gaze behaviour on images possessing different levels of saliency. Eye behaviour was predominantly attracted by salient locations, but appears to also require frequent reference to non-salient background regions which indicated that information from scan paths might prove useful for image search. The thesis then specifically investigates the benefits of eye tracking as an image retrieval interface in terms of speed relative to selection by mouse, and in terms of the efficiency of eye tracking mechanisms in the task of retrieving target images. Results are analysed using ANOVA and significant findings are discussed. Results show that eye selection was faster than a computer mouse and experience gained during visual tasks carried out using a mouse would benefit users if they were subsequently transferred to an eye tracking system. Results on the image retrieval experiments show that users are able to navigate to a target image within a database confirming the feasibility of an eye gaze driven search mechanism. Additional histogram analysis of the fixations, saccades and pupil diameters in the human eye movement data revealed a new method of extracting intentions from gaze behaviour for image search, of which the user was not aware and promises even quicker search performances. The research has two implications for Content Based Image Retrieval: (i) improvements in query formulation for visual search and (ii) new methods for visual search using attentional weighting. Futhermore it was demonstrated that users are able to find target images at sufficient speeds indicating that pre-attentive activity is playing a role in visual search. A current review of eye tracking technology, current applications, visual perception research, and models of visual attention is discussed. A review of the potential of the technology for commercial exploitation is also presented.

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