Abstract: One of the described methods includes receiving a plurality of images from a camera, the plurality of images comprising a sequence; identifying one or more two-dimensional features in each of a plurality of images in the received sequence of images; associating a three-dimensional point with each of the identified one or more two-dimensional features; tracking each of the one or more two-dimensional features through successive images in the plurality of images; and iteratively minimizing a two-dimensional image error between the tracked each of the one or more two-dimensional features and an image reprojection with respect to the three-dimensional point corresponding to the one or more two-dimensional features and a three-dimensional position of the camera corresponding to one or more of the plurality of images.

Abstract: Systems and methods for aligning panoramic imagery of a geographic area captured from a perspective at or near ground level and aerial imagery captured from an oblique perspective are provided. More particularly, a facade plane can be detected in a panoramic image based at least in part on geometric data associated with the image. The panoramic image can also have an associated image pose. An aerial image depicting the same facade can then be identified. The aerial image can be associated with an image pose and geometric data of the depicted imagery. The panoramic image can be warped into an image having a perspective associated with the aerial image. One or more feature matches between the warped image and the aerial image can be identified using a feature matching technique. The matched features can be used to align the panoramic image with the aerial image.

Abstract: Systems and methods for aligning panoramic imagery of a geographic area captured from a perspective at or near ground level and aerial imagery captured from an oblique perspective are provided. More particularly, a facade plane can be detected in a panoramic image based at least in part on geometric data associated with the image. The panoramic image can also have an associated image pose. An aerial image depicting the same facade can then be identified. The aerial image can be associated with an image pose and geometric data of the depicted imagery. The panoramic image can be warped into an image having a perspective associated with the aerial image. One or more feature matches between the warped image and the aerial image can be identified using a feature matching technique. The matched features can be used to align the panoramic image with the aerial image.

Abstract: A method for determining a salient region of an image is disclosed. For a plurality of different saliency cue functions, a single saliency value is calculated for each pixel in a plurality of adjacent pixels in an image using the saliency cue function, wherein one of the saliency cue functions is based on whether the pixel is in a region of the image whose colors contrast with the region's background and another of the saliency cue functions is based on a foreground and background color models of the image. A classifier is used to calculate a combined single saliency value for each pixel based on the single saliency values for the pixel. The salient region of the pixels is determined with a subwindow search based on the combined single saliency values.

Abstract: A method for determining a salient region of an image is disclosed. For a plurality of different saliency cue functions, a single saliency value is calculated for each pixel in a plurality of adjacent pixels in an image using the saliency cue function, wherein one of the saliency cue functions is based on whether the pixel is in a region of the image whose colors contrast with the region's background and another of the saliency cue functions is based on a foreground and background color models of the image. A classifier is used to calculate a combined single saliency value for each pixel based on the single saliency values for the pixel. The salient region of the pixels is determined with a subwindow search based on the combined single saliency values.

Abstract: Wiberg minimization operates on a system with two sets of variables described by a linear function and in which some data or observations are missing. The disclosure generalizes Wiberg minimization, solving for a function that is nonlinear in both sets of variables, U and V, iteratively. In one embodiment, defining a first function ƒ(U, V) that may be defined that may be nonlinear in both a first set of variables U and a second set of variables V. A first function ƒ(U, V) may be transformed into ƒ(U, V(U)). First assumed values of the first set of variables U may be assigned. The second set of variables V may be iteratively estimated based upon the transformed first function ƒ(U, V(U)) and the assumed values of the first set of variables U such that ƒ(U, V(U)) may be minimized with respect to V. New estimates of the first set of variables U may be iteratively computed.

Abstract: A method and system of identity masking to obscure identities corresponding to face regions in an image is disclosed. A face detector is applied to detect a set of possible face regions in the image. Then an identity masker is used to process the detected face regions by identity masking techniques in order to obscure identities corresponding to the regions. For example, a detected face region can be blurred as if it is in motion by a motion blur algorithm, such that the blurred region can not be recognized as the original identity. Or the detected face region can be replaced by a substitute facial image by a face replacement algorithm to obscure the corresponding identity.

Abstract: A method and system of identity masking to obscure identities corresponding to face regions in an image is disclosed. A face detector is applied to detect a set of possible face regions in the image. Then an identity masker is used to process the detected face regions by identity masking techniques in order to obscure identities corresponding to the regions. For example, a detected face region can be blurred as if it is in motion by a motion blur algorithm, such that the blurred region can not be recognized as the original identity. Or the detected face region can be replaced by a substitute facial image by a face replacement algorithm to obscure the corresponding identity.

Abstract: A method and system of identity masking to obscure identities corresponding to face regions in an image is disclosed. A face detector is applied to detect a set of possible face regions in the image. Then an identity masker is used to process the detected face regions by identity masking techniques in order to obscure identities corresponding to the regions. For example, a detected face region can be blurred as if it is in motion by a motion blur algorithm, such that the blurred region can not be recognized as the original identity. Or the detected face region can be replaced by a substitute facial image by a face replacement algorithm to obscure the corresponding identity.

Abstract: A method and system of identity masking to obscure identities corresponding to face regions in an image is disclosed. A face detector is applied to detect a set of possible face regions in the image. Then an identity masker is used to process the detected face regions by identity masking techniques in order to obscure identities corresponding to the regions. For example, a detected face region can be blurred as if it is in motion by a motion blur algorithm, such that the blurred region can not be recognized as the original identity. Or the detected face region can be replaced by a substitute facial image by a face replacement algorithm to obscure the corresponding identity.

Abstract: The dataset describing an entity in a first modality and of a first, high resolution is used to enhance the resolution of a dataset describing the same entity in a second modality of a lower resolution. The two data sets of different modalities are spatially registered to each other. From this information, a joint histogram of the values in the two datasets is computed to provide a raw analysis of how the intensities in the first dataset correspond to intensities in the second dataset. This is converted into a joint probability of possible intensities for the missing pixels in the low resolution dataset as a function of the intensities of the corresponding pixels in the high-resolution dataset to provide a very rough estimate of the intensities of the missing pixels in the low resolution dataset.