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.

Clean Cookstoves and Health in Samlout, Cambodia

This paper discusses results from a study of the use of cleaner cooking solutions and general health status of people in rural areas from the Battambang province of Cambodia. Data collection included 372 demographic, health and socio-economic surveys with households living in 6 villages in the Samlout district, general health examinations, and measurements of stove use and household concentrations of PM 2.5. The data reveal that health in this population is a major concern, with a very high prevalence of reported abdominal pain, nausea, chronic cough, chest pains, and fever during examinations.  At the household level, we find that clean stove ownership is significantly correlated with the educational status of household head and socio-economic status of a household. Respondents from households with clean stoves appear less likely (though not statistically significantly so) to report household individuals having health problems such as occasional cough, high blood pressure and tuberculosis. Concentrations of PM2.5 are positively correlated with prevalence of occasional cough, high blood pressure and tuberculosis. Based on these results, we advise field testing and evaluation of targeted health interventions in these villages to address the numerous concerns of the local population, including exploring the potential role of clean stoves.

Stimulation of GABAergic neurons of the lateral septum and its effect on movement speed

The lateral septum is associated with the regulation of innate behavior, motivation, and locomotion. Its complex interconnections with cognitive and affective regions such as the hippocampus, hypothalamus, and medial septum have made it an attractive region for studying how motivation regulates behavior in context-specific settings. This GABAergic brain region’s main output is the lateral hypothalamus, which provides downstream signaling of motor commands. Even though stimulation of lateral septum projections to the hypothalamus have shown to decrease running speed in free behaving mice, characterizing movement kinematics due to LS activation has not been studied. GABAergic medium spiny neurons of the lateral septum were selectively activated through the use of optogenetic techniques in transgenic mice. Photostimulation of the lateral septum at theta frequencies caused a non-significant decrease in head and back speed. 3D motion analysis of body movement under photostimulation was quantified, revealing a slow, linear decrease of body speed as photostimulation progressed. These results support the role of lateral septum activation in movement regulation and shed light on the specific manner in which stimulation of the LS gradually decreases movement speed.