This thesis investigates the problem of estimating the three-dimensional structure of a scene from a sequence of images. Structure information is recovered from images continuously using shading, motion or other visual mechanisms. A Kalman filter represents structure in a dense depth map. With each new image, the filter first updates the current depth map by a minimum variance estimate that best fits the new image data and the previous estimate. Then the structure estimate is predicted for the next time step by a transformation that accounts for relative camera motion. Experimental evaluation shows the significant improvement in quality and computation time that can be achieved using this technique.

Yes / When a target is flanked by distractors, it becomes more difficult to identify. In the periphery, this crowding effect extends over a wide range of target-flanker separations, called the spatial extent of interaction (EoI). A recent study showed that the EoI dramatically increases in size for short presentation durations (Chung & Mansfield, 2009). Here we investigate this duration-EoI relation in greater detail and show that (a) it holds even when visibility of the unflanked target is equated for different durations, (b) the function saturates for durations shorter than 30 to 80 ms, and (c) the largest EoIs represent a critical spacing greater than 50% of eccentricity. We also investigated the effect of same or different polarity for targets and flankers across different presentation durations. We found that EoIs for target and flankers having opposite polarity (one white, the other black) show the same temporal pattern as for same polarity stimuli, but are smaller at all durations by 29% to 44%. The observed saturation of the EoI for shortduration stimuli suggests that crowding follows the locus of temporal integration. Overall, the results constrain theories that map crowding zones to fixed spatial extents or to lateral connections of fixed length in the cortex. / This study was supported by the ERC POSITION 324070 (PC) and a visiting professorship to Anglia Ruskin University from the Leverhulme Trust (HEB).