Current models of visual spatial attention are based on the extent to which attention can be allocated in 2-dimensional displays. The distribution of attention in 3-dimensional space has received little consideration. A series of experiments were devised to explore the apparent inconsistencies in the literature pertaining to the allocation of spatial attention in the third dimension. A review of the literature attributed these inconsistencies to differences and limitations in the various methodologies employed, in addition to the use of differing attentional paradigms. An initial aim of this thesis was to develop a highly controlled novel adaptation of the conventional robust covert orienting of visual attention task (COVAT) in depth defined by either binocular (stereoscopic) or monocular cues. The results indicated that attentional selection in the COVAT is not allocated within a 3-dimensional representation of space. Consequently, an alternative measure of spatial attention in depth, the overlay interference task, was successfully validated in a different stereoscopic depth environment and then manipulated to further examine the allocation of attention in depth. Findings from the overlay interference experiments indicated that attentional selection is based on a representation that includes depth information, but only when an additional feature can aid 3D selection. Collectively, the results suggest a dissociation between two paradigms that are both purported to be measures of spatial attention. There appears to be a further dissociation between 2-dimensional and 3-dimensional attentional selection in both paradigms for different reasons. These behavioural results, combined with recent electrophysiological evidence suggest that the temporal constraints of the 3D COVAT paradigm result in early selection based predominantly on retinotopic spatial coordinates prior to the complete construction of a 3-dimensional representation. Task requirements of the 3D overlay interference paradigm, on the other hand, while not being restricted by temporal constraints, demand that attentional selection occurs later, after the construction of a 3-dimensional representation, but only with the guidance of a secondary feature. Regardless of whether attentional selection occurs early or late, however, some component of selection appears to be based on viewer-centred spatial coordinates.

<p>In this day and age when 3D cinema is ever increasing in popularity and 3D television sets on the verge of becoming a reality in our homes, the media production companies are interested in looking at how they can start producing stereoscopic format media for this exciting medium. This thesis proposes a solution to parts of the stereoscopic production pipeline by examining and proposing the extent of the requirements of a plug-in to aid in the setting up and rendering of stereo pairs and ultimately proposing such a plug-in and modifying it to suit the proposed requirements. With a high emphasis on ensuring that the plug-in is capable of producing enjoyable stereoscopic content, the plug-in is tested by rendering a demanding scene previously used in factual production work. The findings of these tests ultimately lead to the evaluation of the plug-ins usefulness as a tool for not only the production studio involved but for anyone interested in generating material for this exciting medium.</p>

Accurate earthquake prediction is a difficult, unsolved problem that is central to the ambitions of many geoscientists. Understanding why earthquakes occur requires a profound understanding of many interrelated processes; our planet functions as a massive, complex system. Scientific visualization can be applied to such problems to improve understanding and reveal relationships between data. There are several challenges inherent to visualizing seismic data: working with large, high-resolution 3D and 4D data sets in a myriad of formats, integrating and rendering multiple models in the same space, and the need for real-time interactivity and intuitive interfaces. This work describes a product of the collaboration between computer science and geophysics. TerraVis is a real-time system that incorporates advanced visualization techniques for seismic data. The software can process and efficiently render digital elevation models, earthquake catalogs, fault slip distributions, moment tensor solutions, and scalar fields in the same space. In addition, the software takes advantage of stereoscopic viewing and head tracking for immersion and improved depth perception. During reconstruction efforts after the devastating 2010 earthquake in Haiti, TerraVis was demonstrated as a tool for assessing the risk of future earthquakes.

When seen through a stereoscope, a Gabor pattern (a Gaussian enveloped sinusoid) that is masked by visual noise is more readily detectable when it appears in front of or behind the noise than when it is embedded in the noise itself. The enhanced visibility brought about by stereo cues is referred to as binocular unmasking. In this work, we investigated whether binocular unmasking may also occur with visual objects more complex than simple Gabor patterns, and with tasks more demanding than detection. Specifically, we examined the effects of binocular unmasking in the detection, categorization, and identification of noise masked images of natural objects. We observed the occurrence of binocular unmasking in all three tasks. However, the size of this effect was greater for detection performance than for categorization or identification performance; the latter two benefited to the same extent by the availability of stereoscopic cues. We argue that these results suggest that low level stereoscopic depth cues may play a helpful role, not only in simple detection tasks with psychophysical stimuli, but also in the perception of complex stimuli depicting natural objects.

When seen through a stereoscope, a Gabor pattern (a Gaussian enveloped sinusoid) that is masked by visual noise is more readily detectable when it appears in front of or behind the noise than when it is embedded in the noise itself. The enhanced visibility brought about by stereo cues is referred to as binocular unmasking. In this work, we investigated whether binocular unmasking may also occur with visual objects more complex than simple Gabor patterns, and with tasks more demanding than detection. Specifically, we examined the effects of binocular unmasking in the detection, categorization, and identification of noise masked images of natural objects. We observed the occurrence of binocular unmasking in all three tasks. However, the size of this effect was greater for detection performance than for categorization or identification performance; the latter two benefited to the same extent by the availability of stereoscopic cues. We argue that these results suggest that low level stereoscopic depth cues may play a helpful role, not only in simple detection tasks with psychophysical stimuli, but also in the perception of complex stimuli depicting natural objects.

In this thesis, we proposed a low cost stereoscopic image generation hardware using Depth Image Based Rendering (DIBR) method. Due to the unfavorable artifacts produced by the DIBR algorithm, researchers have developed various algorithms to handle the problem. The most common one is to smooth the depth map before rendering. However, pre-processing of the depth map usually generates other artifacts and even degrades the perception of 3D images. In order to avoid these defects, we present a method by modifying the disparity of edges to make the edges of foreground objects on the synthesized virtual images look more natural. In contrast to the high computational complexity and power consumption in previous designs, we propose a method that fills the holes with the mirrored background pixel values next to the holes. Furthermore, unlike previous DIBR methods that usually consist of two phases, image warping and hole filling, in this thesis we present a new DIBR algorithm that combines the operations of image warping and hole filling in one phase so that the total computation time and power consumption are greatly reduced. Experimental results show that the proposed design can generate more natural virtual images for different view angles with shorter computation latency.

The graphics hardware have evolved from simple feed forward triangle rasterization devices to flexible, programmable, and powerful parallel processors. This evolution allows the researchers to use graphics processing units (GPU) for both general purpose computations and advanced graphics rendering. Sophisticated GPUs hold great opportunities for the acceleration of computationally expensive photorealistic rendering methods. Rendering of photorealistic images in real-time is a challenge. In this work, we investigate efficient ways to utilize GPUs for real-time photorealistic rendering. Specifically, we studied uniform grid based ray tracing acceleration methods and GPU friendly traversal algorithms. We show that our method is faster than or competitive to other GPU based ray tracing acceleration techniques. The proposed approach is also applicable to the fast rendering of volumetric data. Additionally, we devised GPU based solutions for real-time stereoscopic image generation which can be used in companion with GPU based ray tracers.

Natural scene statistics (NSS) have played an increasingly important role in both our understanding of the function and evolution of the human vision system, and in the development of modern image processing applications. Because depth/range, i.e., egocentric distance, is arguably the most important thing a visual system must compute (from an evolutionary perspective), the joint statistics between natural image and depth/range information are of particular interest. However, while there exist regular and reliable statistical models of two-dimensional (2D) natural images, there has been little work done on statistical modeling of natural luminance/chrominance and depth/disparity, and of their mutual relationships. One major reason is the dearth of high-quality three-dimensional (3D) image and depth/range database. To facilitate research progress on 3D natural scene statistics, this dissertation first presents a high-quality database of color images and accurately co-registered depth/range maps using an advanced laser range scanner mounted with a high-end digital single-lens reflex camera. By utilizing this high-resolution, high-quality database, this dissertation performs reliable and robust statistical modeling of natural image and depth/disparity information, including new bivariate and spatial oriented correlation models. In particular, these new statistical models capture higher-order dependencies embedded in spatially adjacent bandpass responses projected from natural environments, which have not yet been well understood or explored in literature. To demonstrate the efficacy and effectiveness of the advanced NSS models, this dissertation addresses two challenging, yet very important problems, depth estimation from monocular images and no-reference stereoscopic/3D (S3D) image quality assessment. A Bayesian depth estimation framework is proposed to consider the canonical depth/range patterns in natural scenes, and it forms priors and likelihoods using both univariate and bivariate NSS features. The no-reference S3D image quality index proposed in this dissertation exploits new bivariate and correlation NSS features to quantify different types of stereoscopic distortions. Experimental results show that the proposed framework and index achieve superior performance to state-of-the-art algorithms in both disciplines. / text

With increasing information density and complexity, computer displays may become visually cluttered, adversely affecting overall usability. Text labels can significantly add to visual clutter in graphical user interfaces, but are generally kept legible through specific label placement algorithms that seek visual separation of labels and other objects in the 2D view plane. This work studies an alternative approach: can overlapping labels be visually segregated by distributing them in stereoscopic depth? The fact that we have two forward-looking eyes yields stereoscopic disparity: each eye has a slightly different perspective on objects in the visual field. Disparity is used for depth perception by the human visual system, and is therefore also provided by stereoscopic 3D displays to produce a sense of depth. This work has shown that a stereoscopic label placement algorithm yields user performance comparable with existing algorithms that separate labels in the view plane. At the same time, such stereoscopic label placement is subjectively rated significantly less disturbing than traditional methods. Furthermore, it does not allow for potentially ambiguous spatial relationships between labels and background objects inherent to labels separated in the view plane. These findings are important for display systems where disturbance, distraction and ambiguity of the overlay can negatively impact safety and efficiency of the system, including the reference application of this work: an augmented vision system for Air Traffic Control towers.

Conventional stereoscopic displays present a pair of stereoscopic images on a single and fixed image plane decoupled with the vergence and accommodation responses of the viewer. In consequence, these displays lack the capability of correctly rendering focus cues (i.e. accommodation and retinal blur) and may induce the discrepancy between accommodation and convergence. A number of visual artifacts associated with incorrect focus cues in stereoscopic displays have been reported, limiting the applicability of these displays for demanding applications and daily usage.In this dissertation, methods and apparatus for generating addressable focus cues in conventional stereoscopic displays are proposed. Focus cues can be addressed throughout a volumetric space, either through dynamically varying the focal distance of a display enabled by an active optical element or by multiplexing a stack of 2-D image planes. Optimal depth-weighted fusing functions are developed to fuse a number of discrete image planes into a seamless volumetric space with continuous and near-correct focus cues similar to the real world counterparts.The optical design, driving methodology, and prototype implementation of the addressable focus displays are presented and discussed. Experimental results demonstrate continuously addressable focus cues from infinity to as close as the near eye distance. Experiments to further evaluate the depth perception in the display prototype are conducted. Preliminary results suggest that the perceived distance and accommodative response of the viewer match with the addressable accommodation cues rendered by the display, approximating the real-world viewing condition.