Abstract: An approach for re-identifying an object in a test image is presented. Similarity measures between the test image and training images captured by a first camera are determined. The similarity measures are based on Bhattacharyya distances between feature representations of an estimated background region of the test image and feature representations of background regions of the training images. A transformed test image based on the Bhattacharyya distances has a brightness that is different from the test image's brightness and matches a brightness of training images captured by a second camera. An appearance of the transformed test image resembles an appearance of a capture of the test image by the second camera. Another image included in test images captured by the second camera is identified as being closest in appearance to the transformed test image and another object in the identified other image is a re-identification of the object.

Abstract: A system and method to enhance observation resolution using continuous learning include obtaining a first image of a surface area from a first satellite, and obtaining a second image of the surface area from a second satellite. The first image has a lower spatial resolution than the second image, and temporal resolution of the first images obtained by the first satellite is higher than temporal resolution of the second images obtained by the second satellite. The method also includes determining a convolution matrix A or training a neural network, obtaining additional one or more of the first images prior to obtaining an additional one of the second images, and generating a new image from each of the one or more of the first images using the convolution matrix A or the neural network. The new image has a higher spatial resolution than the one or more of the first images.

Abstract: A face clustering system for video face clustering in a video sequence, the system including an inherent supervision summarization device configured to collect group-level supervision and instance level supervision within a same chunklet based on a user input of face images for a person, a discriminative projection learning device configured to embed group constraints of the group-level supervision into a transformed space, and configured to generate an embedding space from the original image feature space, and a clustering device, in the embedding space, configured to execute pair-wise based clustering to cluster the video images into different clusters with the instance level supervision collected by the inherent supervision summarization device.

Abstract: A method and system of stitching a plurality of image views of a scene, including grouping matched points of interest in a plurality of groups, and determining a similarity transformation with smallest rotation angle for each grouping of the matched points. The method further includes generating virtual matching points on non-overlapping area of the plurality of image views and generating virtual matching points on overlapping area for each of the plurality of image views.

Abstract: A face clustering system for video face clustering in a video sequence, the system including an inherent supervision summarization device configured to collect group-level supervision and instance level supervision within a same chunklet based on a user input of face images for a person, a discriminative projection learning device configured to embed group constraints of the group-level supervision into a transformed space, and configured to generate an embedding space from the original image feature space, and a clustering device, in the embedding space, configured to execute pair-wise based clustering to cluster the video images into different clusters with the instance level supervision collected by the inherent supervision summarization device.

Abstract: A computer-implemented method includes receiving one or more training images depicting one or more training geographical regions. One or more environmental characteristic (EC) values are determined for the training images. The EC values include at least one EC value for each of the training images. One or more models are generated for mapping an EC value of an image to a determination of whether an artifact is present in a geographical region depicted by the image, based on the EC values and based on knowledge of which of the training images depict training geographical regions having artifacts present. A new image is received depicting a new geographical region. The models are applied to the new image. A probability that a new artifact is present in the new geographical region depicted in the new image is determined, based on the applying the models to the new image.

Abstract: A computer-implemented method for finding objects in a collection of images or video, the method comprising: assessing a similarity between two subgraphs; detecting the objects in the collection; assessing a relationship between the objects; finding attributes of the objects; constructing a collection graph for the collection where each of the objects is the vertices and nodes of the collection graph and their relationships with other objects and attributes are edges of the collection graphs; recursively identifying coherent subgraphs and turning the coherent subgraphs into new meta-nodes of the collection graph; identification of the stable graph reducing the collection graph into zero or more distinct stories; assigning a similarity score for each pair of the stories based on a similarity between the corresponding stories; linking the stories into inlier stories based on the similarity score being greater than a first pre-determined threshold level.

Abstract: A compression system for compressing a video received from an imaging device, the compression system including a transformation estimation device configured to estimate, based on pixel transformations between a first frame and a second frame of the video, a transformation matrix, an encoding device configured to encode the second frame as an increment with the first frame based on the transformation matrix generated by the transformation device, a compression device configured to compress the increments into compressed data, and a reconstruction device configured to reconstruct the first frame and the second frame using the transformation matrix generated by the transformation estimation device.

Abstract: A similarity detection system receiving a plurality of input entities, the system including a cohesive subgraph identification device configured to calculate, based on attributes of the plurality of input entities, a first parameter and a second parameter based on the first parameter, and further configured to identify a plurality of subgraphs from the second parameter and a subgraph correlation tracking and clustering device configured to determine a relationship between different subgraphs based on a similarity factor between the second parameter and the plurality of subgraphs.

Abstract: A system and method to enhance observation resolution using continuous learning include obtaining a first image of a surface area from a first satellite, and obtaining a second image of the surface area from a second satellite. The first image has a lower spatial resolution than the second image, and temporal resolution of the first images obtained by the first satellite is higher than temporal resolution of the second images obtained by the second satellite. The method also includes determining a convolution matrix A or training a neural network, obtaining additional one or more of the first images prior to obtaining an additional one of the second images, and generating a new image from each of the one or more of the first images using the convolution matrix A or the neural network. The new image has a higher spatial resolution than the one or more of the first images.

Abstract: Embodiments include method, systems and computer program products for reducing surface reflectance, for example in a photograph. Aspects include receiving an image set containing a plurality of images of an object. Aspects also include determining geometric transformations for the images and constructing a panorama of the object from the images using the geometric transformations. Aspects also include replacing a portion in the panorama with a corresponding replacement portion from the image set.

Abstract: An approach for re-identifying an object in a test image is presented. Similarity measures between the test image and training images captured by a first camera are determined. The similarity measures are based on Bhattacharyya distances between feature representations of an estimated background region of the test image and feature representations of background regions of the training images. A transformed test image based on the Bhattacharyya distances has a brightness that is different from the test image's brightness and matches a brightness of training images captured by a second camera. An appearance of the transformed test image resembles an appearance of a capture of the test image by the second camera. Another image included in test images captured by the second camera is identified as being closest in appearance to the transformed test image and another object in the identified other image is a re-identification of the object.

Abstract: Embodiments of the present invention provide a system, method, and program product to determine whether a product has been successfully purchased by identifying in a video record when a movement of a product adjacent to a scanner occurs, and whether the scanner did not record a purchase transaction at that time; measuring a difference in time between the time of the movement of the product and a time of another movement of a product, and determining by a trained support vector machine a likelihood that the product was successfully purchased. Alternately, the difference in time can be measured between the time of the movement of the product and a time of a transaction record, or between the time of the movement of the product and a boundary time. The support vector machine can use a radial basis function kernel and can generate a decision value and a confidence score.

Abstract: A method includes obtaining two-dimensional measurements of a given scene from sensors, the given scene comprising a plurality of scene points, distributing the given scene into bundles each associated with at least one sensor and at least one scene point, establishing constraints associated with the sensors and the plurality of scene points that are associated with two different bundles, estimating, individually for each of the bundles, a set of parameters for the at least one sensor and the at least one scene point associated with that bundle utilizing at least one of the two-dimensional measurements, generating a consensus parameter set for the one or more sensors and the plurality of scene points based on the estimated parameter sets, the established constraints being utilized to reconcile differences in parameter values in the estimated parameter sets, and producing a three-dimensional representation of the given scene utilizing the consensus parameter set.

Abstract: A computer-implemented method includes receiving one or more training images depicting one or more training geographical regions. One or more environmental characteristic (EC) values are determined for the training images. The EC values include at least one EC value for each of the training images. One or more models are generated for mapping an EC value of an image to a determination of whether an artifact is present in a geographical region depicted by the image, based on the EC values and based on knowledge of which of the training images depict training geographical regions having artifacts present. A new image is received depicting a new geographical region. The models are applied to the new image. A probability that a new artifact is present in the new geographical region depicted in the new image is determined, based on the applying the models to the new image.

Abstract: A discrepancy detection system for detecting a discrepancy between a plurality of video images of a same scene captured by an imaging device at different times, the system including a correspondence point detection device configured to detect corresponding points matching each other between a first video image sequence of the plurality of video images and a second video image sequence of the plurality of video images and a comparison device configured to compare image content of the first video image sequence and the second video image sequence for the discrepancy in the image content between the first video image sequence and the second video image sequence.

Abstract: An aerial drone is coupled to a warning sign for warning other vehicles of a presence of a faulty vehicle. The aerial drone is positioned at a location of the faulty vehicle, in response to detecting the faulty vehicle. The aerial drone assesses environmental conditions at the location of the faulty vehicle. An optimal position for positioning the warning sign is determined, based on the environmental conditions at the location of the faulty vehicle, and the aerial drone is positioned at the optimal position.

Abstract: An approach for re-identifying an object in a test image is presented. Similarity measures between the test image and training images captured by a first camera are determined. The similarity measures are based on Bhattacharyya distances between feature representations of an estimated background region of the test image and feature representations of background regions of the training images. A transformed test image based on the Bhattacharyya distances has a brightness that is different from the test image's brightness, and matches a brightness of training images captured by a second camera. An appearance of the transformed test image resembles an appearance of a capture of the test image by the second camera. Another image included in test images captured by the second camera is identified as being closest in appearance to the transformed test image and another object in the identified other image is a re-identification of the object.

Abstract: Machine logic that pre-processes and post-processes images for visual object detection by performing the following steps: receiving a set of image(s); filtering the set of image(s) using a set of multimodal integral filter(s), thereby removing at least a portion of the set of image(s) and resulting in a filtered set of image(s); performing object detection on the filtered set of image(s) to generate a set of object-detected image(s); assembling a first plurality of object-detected image(s) from the set of object-detected image(s); and upon assembling the first plurality of object-detected image(s), performing non-maximum suppression on the assembled first plurality of object-detected image(s).

Abstract: A method, system and computer program product for shielding an operator of a vehicle from light, including determining a position of a light source based on the location of the vehicle and the current date and time. A line of sight between the light source and the eyes of the operator is determined, where the line of sight goes through a windshield of the vehicle that includes a plurality of pixels. One or more pixels in the line of sight are identified; and, one or more physical properties of the pixel(s) in the line of sight are modified.