Learning and Exploiting Camera Geometry for Computer Vision

This work explores the use of statistical methods in describing and estimating camera poses, as well as the information feedback loop between camera pose and object detection. Surging development in robotics and computer vision has pushed the need for algorithms that infer, understand, and utilize information about the position and orientation of the sensor platforms when observing and/or interacting with their environment.

The first contribution of this thesis is the development of a set of statistical tools for representing and estimating the uncertainty in object poses. A distribution for representing the joint uncertainty over multiple object positions and orientations is described, called the mirrored normal-Bingham distribution. This distribution generalizes both the normal distribution in Euclidean space, and the Bingham distribution on the unit hypersphere. It is shown to inherit many of the convenient properties of these special cases: it is the maximum-entropy distribution with fixed second moment, and there is a generalized Laplace approximation whose result is the mirrored normal-Bingham distribution. This distribution and approximation method are demonstrated by deriving the analytical approximation to the wrapped-normal distribution. Further, it is shown how these tools can be used to represent the uncertainty in the result of a bundle adjustment problem.

Another application of these methods is illustrated as part of a novel camera pose estimation algorithm based on object detections. The autocalibration task is formulated as a bundle adjustment problem using prior distributions over the 3D points to enforce the objects' structure and their relationship with the scene geometry. This framework is very flexible and enables the use of off-the-shelf computational tools to solve specialized autocalibration problems. Its performance is evaluated using a pedestrian detector to provide head and foot location observations, and it proves much faster and potentially more accurate than existing methods.

Finally, the information feedback loop between object detection and camera pose estimation is closed by utilizing camera pose information to improve object detection in scenarios with significant perspective warping. Methods are presented that allow the inverse perspective mapping traditionally applied to images to be applied instead to features computed from those images. For the special case of HOG-like features, which are used by many modern object detection systems, these methods are shown to provide substantial performance benefits over unadapted detectors while achieving real-time frame rates, orders of magnitude faster than comparable image warping methods.

The statistical tools and algorithms presented here are especially promising for mobile cameras, providing the ability to autocalibrate and adapt to the camera pose in real time. In addition, these methods have wide-ranging potential applications in diverse areas of computer vision, robotics, and imaging.

One sound or two? Object-related negativity indexes echo perception.

The ability to isolate a single sound source among concurrent sources and reverberant energy is necessary for understanding the auditory world. The precedence effect describes a related experimental finding, that when presented with identical sounds from two locations with a short onset asynchrony (on the order of milliseconds), listeners report a single source with a location dominated by the lead sound. Single-cell recordings in multiple animal models have indicated that there are low-level mechanisms that may contribute to the precedence effect, yet psychophysical studies in humans have provided evidence that top-down cognitive processes have a great deal of influence on the perception of simulated echoes. In the present study, event-related potentials evoked by click pairs at and around listeners' echo thresholds indicate that perception of the lead and lag sound as individual sources elicits a negativity between 100 and 250 msec, previously termed the object-related negativity (ORN). Even for physically identical stimuli, the ORN is evident when listeners report hearing, as compared with not hearing, a second sound source. These results define a neural mechanism related to the conscious perception of multiple auditory objects.