Abstract:
Embodiments of the invention disclose a system and a method for determining a pose of a probe relative to an object by probing the object with the probe, comprising steps of: determining a probability of the pose using Rao-Blackwellized particle filtering, wherein a probability of a location of the pose is represented by a location of each particle, and a probability of an orientation of the pose is represented by a Gaussian distribution over orientation of each particle conditioned on the location of the particle, wherein the determining is performed for each subsequent probing until the probability of the pose concentrates around a particular pose; and estimating the pose of the probe relative to the object based on the particular pose.
Abstract:
Embodiments of the invention disclose a system and a method for determining a pose of a probe relative to an object by probing the object with the probe, comprising steps of: determining a probability of the pose using Rao-Blackwellized particle filtering, wherein a probability of a location of the pose is represented by a location of each particle, and a probability of an orientation of the pose is represented by a Gaussian distribution over orientation of each particle conditioned on the location of the particle, wherein the determining is performed for each subsequent probing until the probability of the pose concentrates around a particular pose; and estimating the pose of the probe relative to the object based on the particular pose.
Abstract:
A system and method for providing programming to a client in a Multi-Dwelling Unit or Multi-Tenant unit network. The system and method includes various embodiments for converting pay per view content to video on demand content, collecting and displaying popular programs to a client, and billing a client for the utilization of personal video recording functions.
Abstract:
An image of an object from a known object class is synthesized by first obtaining reflectance fields for various training objects from the object class. A reflectance field model is defined for the object class using a combination of the reflectance fields of the training objects. The parameters of the reflectance field model are optimized to estimate a particular reflectance field of a particular object from the object class given one or more input images of the particular object. The particular reflectance field is fitted to the particular object, and then the new image of the particular object is synthesized by changing the illumination parameters of the particular fitted reflectance field model after the fitting.