Abstract: A method for generating a representation of a polyline formed by a sequence of points determines an overcomplete set of geometric primitives that redundantly fit overlapping sections of the polyline. The overcomplete fitting is performed such that the set includes one or more types of geometric primitives, each fitted to a section of the polyline formed by a subset of the sequence of points. The method determines a local cost of each geometric primitive based on a combination of a fitting error and an encoding cost of the geometric primitive, and determines a join cost of each successive pair of adjacent primitives. Next, the method determines a combination of the geometric primitives forming a connected path along a length of the polyline such that a sum of the local costs of the geometric primitives and the join costs of adjacent primitives in the combination is optimized.

Abstract: Three-dimensional (3D) geometric boundaries are detected in images of a scene that undergoes varying lighting conditions caused by light sources in different positions, from a set of input images of the scene illuminated by at least two different lighting conditions. The images are aligned, e.g., acquired by a stationary camera, so that pixels at the same location in all of the input images correspond to the same point in the scene. For each location, a patch of corresponding pixels centered at the location is extracted from each input image. For each location, a confidence value that there is a 3D geometric boundary at the location is determined.

Abstract: A set of nonnegative lighting basis images representing a scene illuminated by a set of stationary light sources is recovered from a set of input images of the scene that were acquired by a stationary camera. Each image is illuminated by a combination of the light sources, and at least two images in the set are illuminated by different combinations. The set of input images is factorized into the nonnegative lighting basis images and a set of indicator coefficients, wherein each lighting basis image corresponds to an appearance of the scene illuminated by one of the light sources, and wherein each indicator coefficient indicates a contribution of one of the light sources to one of the input images.

Abstract: In a sequence of images of a scene acquired by a stationary camera, objects are detected and tracked by determining a first set of candidate foreground regions according to a background model. A second set of candidate foreground regions is determined according to a set of foreground models. Then, candidate foreground regions in the first set and the second set are validated to produce a final set of foreground regions in the image that include the objects.

Abstract: A method for generating a representation of a polyline formed by a sequence of points determines an overcomplete set of geometric primitives that redundantly fit overlapping sections of the polyline. The overcomplete fitting is performed such that the set includes one or more types of geometric primitives, each fitted to a section of the polyline formed by a subset of the sequence of points. The method determines a local cost of each geometric primitive based on a combination of a fitting error and an encoding cost of the geometric primitive, and determines a join cost of each successive pair of adjacent primitives. Next, the method determines a combination of the geometric primitives forming a connected path along a length of the polyline such that a sum of the local costs of the geometric primitives and the join costs of adjacent primitives in the combination is optimized.

Abstract: A set of images is acquired of a scene by a camera. The scene includes a moving object, and a relative difference of a motion of the camera and a motion of the object is substantially zero. Statistical properties of pixels in the images are determined, and a statistical method is applied to the statistical properties to identify pixels corresponding to the object.

Abstract: A climate control unit is controlled by constructing background and foreground models of an environment from images acquired of the environment by a camera. The background model represents the environment when unoccupied, and there is one foreground model for each person in the environment. A 2D location of each person in the environment is determined using the background and foreground models. A 3D location of each person is determined using the 2D locations and inferences made from the images. The controlling of the climate control unit is according to the 3D locations.

Abstract: A set of nonnegative lighting basis images representing a scene illuminated by a set of stationary light sources is recovered from a set of input images of the scene that were acquired by a stationary camera. Each image is illuminated by a combination of the light sources, and at least two images in the set are illuminated by different combinations. The set of input images is decomposed into the nonnegative lighting basis images and a set of indicator coefficients, wherein each lighting basis image corresponds to an appearance of the scene illuminated by one of the light sources, and wherein each indicator coefficient indicates a contribution of one of the light sources to one of the input images.

Abstract: Three-dimensional (3D) geometric boundaries are detected in images of a scene that undergoes varying lighting conditions caused by light sources in different positions, from a set of input images of the scene illuminated by at least two different lighting conditions. The images are aligned, e.g., acquired by a stationary camera, so that pixels at the same location in all of the input images correspond to the same point in the scene. For each location, a patch of corresponding pixels centered at the location is extracted from each input image. For each location, a confidence value that there is a 3D geometric boundary at the location is determined.

Abstract: In a sequence of images of a scene acquired by a stationary camera, objects are detected and tracked by determining a first set of candidate foreground regions according to a background model. A second set of candidate foreground regions is determined. according to a set of foreground models. Then, candidate foreground regions in the first set and the second set are validated to produce a final set of foreground regions in the image that include the objects.

Abstract: A climate control unit is controlled by constructing background and foreground models of an environment from images acquired of the environment by a camera. The background model represents the environment when unoccupied, and there is one foreground model for each person in the environment. A 2D location of each person in the environment is determined using the background and foreground models. A 3D location of each person is determined using the 2D locations and inferences made from the images. The controlling of the climate control unit is according to the 3D locations.

Abstract: A probe is registered with an object by probing the object with the probe at multiple poses, wherein each pose of the probe includes a location and an orientation. A probability distribution of a current location of the probe is represented by a set of particles, and a probability distribution of a current orientation of the probe is represented by a Gaussian distribution for each particle conditioned on the current location. A set of candidate motions is chosen, and for each candidate motion, an expected uncertainty based on the set of particles is determined. The candidate motion with a least expected uncertainty is selected as a next motion of the probe, the probe is moved according to the next motion, and the set of particles is updated using the next pose of the probe.

Abstract: A Gabor filter is approximated as a block-Gabor filter. The Gabor filter is represented by a matrix of numbers in which each number is a sample derived from a continuous Gabor function. The block-Gabor filter is partitioned into a set of blocks. Identical filter values are assigned to all the pixels in any particular block based on the Gabor filter. Then, a feature can be extracted from an image by filtering the image with a set of the block-Gabor filters to obtain a corresponding set of filtered images. Each filtered image is partitioned into regions of pixels. For each pixel, an N-bit signature is determined. Histograms of the N-bit signatures of the pixels in each region are combined to form the feature. The features of multiple images can be used for face recognition.

Abstract: A probe is registered with an object by probing the object with the probe at multiple poses, wherein each pose of the probe includes a location and an orientation. A probability distribution of a current location of the probe is represented by a set of particles, and a probability distribution of a current orientation of the probe is represented by a Gaussian distribution for each particle conditioned on the current location. A set of candidate motions is chosen, and for each candidate motion, an expected uncertainty based on the set of particles is determined. The candidate motion with a least expected uncertainty is selected as a next motion of the probe, the probe is moved according to the next motion, and the set of particles is updated using the next pose of the probe.

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.

Abstract: A set of images is acquired of a scene by a camera. The scene includes a moving object, and a relative difference of a motion of the camera and a motion of the object is substantially zero. Statistical properties of pixels in the images are determined, and a statistical method is applied to the statistical properties to identify pixels corresponding to the object.