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In this work, three novel approaches to detecting visual attention in images are presented. The idea behind detecting areas within images or video that naturally attract a viewer’s attention is based on the concept of generating pre-attentive saliency maps. Saliency, in and of itself, relates to some measure of “conspicuity” in the visual field and is believed to be an important precursor for many tasks in computer vision. One of the proposed methods in this thesis detects salient regions, while the other two detect salient edges. The classical approach to saliency detection proposed by Itti is extended by introducing wavelets as a lossless resizing tool while maintaining the aspect of biological inspiration. In addition to this, the spectral residual method and the frequency tuned method are modified using wavelets to allow for salient edge detection. Tests show that the proposed methods yield results that are not only comparable to leading,cutting-edge methods, but also exceed them in terms of correct and complete object detection as well as noise reduction.

This paper presents a framework for the rapid isolation and representation of structures from spatially registered image stacks. The introduced framework enables dynamic visualization with next generation volumetric displays, in which image volumes are rendered in real 3D space. Two recent semi-supervised approaches to the problem of segmenting relevant structures are investigated and integrated to yield a generic interactive visualization pipeline. Information obtained from the segmentation process is used to guide layered object reconstruction via the SpatialGL SDK. The reconstructed original objects in 3D may then be selectively visualized on the Perspecta: a spatially-rendered display. Practical considerations, future improvements and applications are discussed._____________________________ 978-1-4244-3930-03930-009/$25.00 ©2009 IEEE