Abstract: A method for face alignment operates on a face image and a set of initial landmark locations by first aligning globally the initial locations to a set of landmark locations of a face with a prototype shape to obtain global alignment parameters, and then warping the initial locations and the image from a coordinate frame of the image to a coordinate frame of the prototype shape according to the global alignment parameters to obtain warped landmark locations and a warped face image. Features are extracted from the warped face image at the warped landmark locations, and a regression function is applied to the features to obtain updated landmark locations in the coordinate frame of the prototype shape. Finally, the updated landmark locations in the coordinate frame of the prototype shape are warped to the coordinate frame of the image, to obtain updated landmark locations.

Abstract: A sensor acquires an input image X of a scene. The image includes noise with a variance ?2. A deep Gaussian conditional random field (GCRF) network is applied to the input image to produce an output image Y, where the output image is denoised, and wherein the deep GCRF includes a prior generation (PgNet) network followed by an inference network (InfNet), wherein the PgNet produces patch covariance priors ?ij for patches centered on every pixel (i,j) in the input image, and wherein the InfNet is applied to the patch covariance priors and the input image to solve the GCRF.

Abstract: A resolution of a low resolution depth image is increased by applying joint geodesic upsampling to a high resolution image to obtain a geodesic distance map. Depths in the low resolution depth image are interpolated using the geodesic distance map to obtain a high resolution depth image. The high resolution image can be a gray scale or color image, or a binary boundary map. The low resolution depth image can be acquired by any type of depth sensor.

Abstract: A method semantically labels an image acquired of a scene by first obtaining a local semantic feature for each local region in the image. The local semantic features are combined recursively to form intermediate segments until a semantic feature for the entire image is obtained. Then, the semantic feature for the entire image is decombined recursively into intermediate segments until an enhanced semantic feature for each local region is obtained. Then, each local region is labeled according to the enhanced semantic feature.

Abstract: A vehicle includes a set of devises for determining a state of the vehicle at an instant of time and a processor operatively connected to a memory for processing parameters of the state of the vehicle collected over a plurality of instances of time for a period of time from an initial instance of time till a current instant of time to determine a set of driving conditions leading to a current state of the vehicle at the current instant of time. Each driving condition indicates a condition of an operation of the vehicle for the period of time. The vehicle also includes a communication module for outputting the current state of the vehicle determined by the set of devices at the current instant of time and for outputting the set of driving conditions leading to the current state of the vehicle.

Abstract: A method for training a classifier that is customized to detect and classify objects in a set of images acquired in a target environment, first generates a 3D target environment model from the set of images, and then acquires 3D object models. Training data is synthesized from the target environment model and the 3D object models, and then the classifier is trained using the training data.

Abstract: A method labels an image of a street view by first extracting, for each pixel, an appearance feature for inferring a semantic label, a depth feature for inferring a depth label. Then, a column-wise labeling procedure is applied to the features to jointly determine the semantic label and the depth label for each pixel using the appearance feature and the depth feature, wherein the column-wise labeling procedure is according to a model of the street view, and wherein each column of pixels in the images includes at most four layers.

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: A method for determining a pose of an object in a scene by determining a set of scene features from data acquired of the scene and matching the scene features to model features to generate weighted candidate poses when the scene feature matches one of the model features, wherein the weight of the candidate pose is proportional to the model weight. Then, the pose of the object is determined from the candidate poses based on the weights.

Abstract: A method denoises a noisy image by, for each pixel in the noisy image, first constructing a key from a patch, wherein the patch includes locally neighboring pixels around the pixel. A function is selected from a function library using the key. Then, the function is applied to the patch to generate a corresponding noise free pixel for the pixel.

Abstract: A method semantically labels an image acquired of a scene by first obtaining a local semantic feature for each local region in the image. The local semantic features are combined recursively to form intermediate segments until a semantic feature for the entire image is obtained. Then, the semantic feature for the entire image is decombined recursively into intermediate segments until an enhanced semantic feature for each local region is obtained. Then, each local region is labeled according to the enhanced semantic feature.

Abstract: A method denoises a noisy image by, for each pixel in the noisy image, first constructing a key from a patch, wherein the patch includes locally neighboring pixels around the pixel. A function is selected from a function library using the key. Then, the function is applied to the patch to generate a corresponding noise free pixel for the pixel.

Abstract: A method detects an object in a pair of stereo images acquired of a scene, by first generating a cost volume from the pair of stereo images, wherein the cost volume includes matching costs for a range of disparity values, for each pixel the stereo images, between the stereo images in the pair. Feature vectors are determined from sub-images in the cost volume using a feature function of the disparity values with a minimal accumulated cost within regions inside the sub-images. Then, a classifier is appled to the feature vectors to detect whether the sub-image includes the object.

Abstract: A method detects an object in a pair of stereo images acquired of a scene, by first generating a cost volume from the pair of stereo images, wherein the cost volume includes matching costs for a range of disparity values, for each pixel the sterao images, between the stereo images in the pair. Feature vectors are determined from sub-images in the cost volume using a feature function of the disparity values with a minimal accumulated cost within regions inside the sub-images. Then, a classifier is appled to the feature vectors to detect whether the sub-image includes the object.

Abstract: A resolution of a low resolution depth image is increased by applying joint geodesic upsampling to a high resolution image to obtain a geodesic distance map. Depths in the low resolution depth image are interpolated using the geodesic distance map to obtain a high resolution depth image. The high resolution image can be a gray scale or color image, or a binary boundary map. The low resolution depth image can be acquired by any type of depth sensor.

Abstract: A resolution of a low resolution depth image is increased by applying joint geodesic upsampling to a high resolution image to obtain a geodesic distance map. Depths in the low resolution depth image are interpolated using the geodesic distance map to obtain a high resolution depth image. The high resolution image can be a gray scale or color image, or a binary boundary map. The low resolution depth image can be acquired by any type of depth sensor.

Abstract: A method for determining a pose of an object in a scene by determining a set of scene features from data acquired of the scene and matching the scene features to model features to generate weighted candiate poses when the scene feature matches one of the model features, wherein the weight of the candidate pose is proportional to the model weight. Then, the pose of the object is determined from the candidate poses based on the weights.

Abstract: A method and system extract features from an image acquired of an object with a specular surface by first acquiring an image while illuminating the object with a hue circle generated by a set of lights flashed simultaneously. The lights have different colors and are arranged circularly around a lens of a camera. Then, the features correspond to locations of pixels in the image within a neighborhood of pixels that includes a subset of the colors of the lights.

Abstract: A pose of an object is estimated by first defining a set of pair features as pairs of geometric primitives, wherein the geometric primitives include oriented surface points, oriented boundary points, and boundary line segments. Model pair features are determined based on the set of pair features for a model of the object. Scene pair features are determined based on the set of pair features from data acquired by a 3D sensor, and then the model pair features are matched with the scene pair features to estimate the pose of the object.