Browsing by Subject "Binocular vision"
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Item A three-dimensional dynamic model for the rotation of the eye in human binocular vision(Texas Tech University, 1998-12) Lockwood-Cooke, PamelaNot availableItem Active binocular vision: phase-based registration and optimal foveation(2007-05) Monaco, James Peter; Bovik, Alan C. (Alan Conrad), 1958-; Cormack, Lawrence K.Active binocular vision systems are powerful tools in machine vision. With a virtually unlimited field-of-view they have access to huge amounts of information, yet are able to confine their resources to specific regions of interest. Since they can dynamically interact with the environment, they are able to successfully address problems that are ill-posed to passive systems. A primary goal of an active binocular vision systems is to ascertain depth information. Since they employ two cameras and are able to sample a scene from two distinct vantage points, they are well suited for such a task. The depth recovery process is composed of two interrelated components: image registration and sampling. Image registration is the process of determining corresponding points between the stereo images. Once points in the images have been matched, 3D information can be recovered via triangulation. Image sampling determines how the image is discretized and represented. Image registration and sampling are highly interdependent. The choice of sampling scheme can profoundly impact the accuracy and complexity of the registrations process. In many situations, particular registration algorithms are simply incompatible with some sampling schemes. In this dissertation we meticulously address both registration and sampling in the context of stereopis for active binocular vision systems. Throughout the development of this work, contributions in each area are addressed with an eye toward their eventual integration into a cohesive registration procedure appropriate for active binocular vision systems. The actual synthesis is a daunting task that is beyond the scope of this single dissertation. The focus of this work is to assiduously analyze both registration and sampling, establishing a solid foundation for their future aggregation. One of the most successful approaches to image registration is phase-differencing. Phase-differencing algorithms provide a fast, powerful means for depth recovery. Unfortunately, phase-differencing techniques suffer from two significant impediments: phase nonlinearities and neglect of multispectral information. This dissertation uses the amenable properties of white noise images to analytically quantify the behavior of phase in these regions of phase nonlinearity. The improved understanding gained from this analysis enables us to create a new, more effective method for identifying these regions based on the second derivative of phase. We also suggest a novel approach that combines our method of nonlinear phase detection with strategies of both phase-differencing and local correlation. This hybrid approach retains the advantageous properties of phase-differencing while incorporating the multispectral aspects of local correlation. This task of registration is greatly simplified if the camera geometry is known and the search for corresponding points can be restricted to epipolar lines. Unfortunately, computation of epipolar lines for an active system requires calibration which can be both highly complex and inaccurate. While it is possible to register images without calibration information, such unconstrained algorithms are usually time consuming and prone to error. In this dissertation we propose compromise. Even without the instantaneous knowledge of the system geometry, we can restrict the region of correspondence by imposing limits on the possible range of configurations, and as a result, confine our search for matching points to what we refer to as epipolar spaces. For each point in one image, we define the corresponding epipolar space in the other image as the union of all associated epipolar lines over all possible system geometries. Epipolar spaces eliminate the need for calibration at the cost of an increased search region. Since the average size of a search space is directly related to the accuracy and efficiency of any registration algorithm, it is essential to mitigate the increase. The major contribution of this dissertation is the derivation of an optimal nonuniform sampling that minimizes the average area per epipolar space.Item Binocular mechanisms underlying the processing of three-dimensional visual motion.(2012-05) Czuba, Thaddeus Bradley; Cormack, Lawrence K; Huk, Alexander; Geisler, Wilson S; Pillow, Jonathan W; Priebe, Nicholas JIn this dissertation, I examine binocular 3D motion processing through a series of psychophysical and neuroimaging experiments aimed at uncovering the neural computations involved and their interaction with the known hierarchy of visual motion processing. Two primary binocular cues could be used to compute 3D motion: one based on changing disparities over time (CD), the other based on interocular velocity differences (IOVD). Under normal viewing conditions, both cues coexist and (potentially) provide the same 3D direction information, yet whether CD, IOVD, or both mechanisms exist has distinct implications for how 3D motion is processed along the visual stream. First, I measured 3D direction discrimination sensitivity is measured for isolated binocular cues under a range of 3D motion speeds and visual eccentricities. Comparison of isolated-cue sensitivity to corresponding combined cue sensitivity (i.e. concurrent IOVD & CD cue stimuli) provided an estimate of relative cue contributions under normal viewing conditions. Second, I conducted a series of motion adaptation experiments to differentiate the neural representation of 2D and 3D directions of motion, and examine the degree to which IOVD or CD mechanisms can account for 3D motion adaptation. Third, I examined the neural locus of 3D motion processing by measuring 3D direction- selectivity throughout a range of visual cortical areas using functional neuroimaging in an event-related paradigm that parallels psychophysical adaptation experiments. Finally, I discuss the broader implications for the neural mechanisms of binocular 3D motion processing and future experimental directions. Together, these results reveal that: (1) the IOVD cue is the dominant cue to 3D motion processing across the majority of natural speeds & eccentricities, (2) neural tuning for 3D motion is distinct from 2D motion and can be fully explained by an IOVD mechanism, and (3) the IOVD cue is computed relatively late in the visual processing stream, in areas MT & MST— cortical areas primarily associated with 2D/retinal motion and thought to be beyond the point of binocular combination. The significance of IOVD —but not CD—cues to 3D motion perception motivates a drastic modification to canonical models of motion processing to include the late-stage comparison of eye- specific motion signals.