Abstract: A computer implemented method for detecting an object in a crowded scene utilizing an image capturing device. The method includes receiving an image of a predetermined area. From the image, the existence of selected portions as representing an entity of a selected class is determined. Each selected portion is assigned an initial confidence value that the selected portion is an entity representative of a selected class. Each selected portion is evaluated with each other to determine a context confidence value. The context confidence value and initial confidence value are utilized to determine which of the one or more selected portions are entities of a selected class.

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 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 for detecting an object in a crowded scene utilizing an image capturing device. The method includes receiving an image of a predetermined area. From the image, the existence of selected portions as representing an entity of a selected class is determined. Each selected portion is assigned an initial confidence value that the selected portion is an entity representative of a selected class. Each selected portion is evaluated with each other to determine a context confidence value. The context confidence value and initial confidence value are utilized to determine which of the one or more selected portions are entities of a selected class.

Abstract: A camera at a fixed vertical height positioned above a reference plane, with an axis of a camera lens at an acute angle with respect to a perpendicular of the reference plane. One or more processors receive camera images of a multiplicity of people of unknown height and vertical axis of the images are transformed into pixel counts. The known heights of people from a known statistical distribution of heights of people are received by one or more processors and transformed to a normalized measurement of pixel counts, based in part on a focal length of the camera lens, the angle of the camera, and an objective function summing differences between pixel counts of the known heights of people and the unknown heights of people. The fixed vertical height of the camera is determined by adjusting the estimated camera height to minimize the objective function.

Abstract: A method of operating an image detection device includes receiving an image, dividing the image into a plurality of patches, grouping ones of the plurality of patches, generating a set of saccadic paths through the plurality of patches of the image, generating a cluster-direction sequence for each saccadic path, generating a policy function for identifying an object in a new image using a combination of the cluster-direction sequences, and operating the image detection device using the policy function to identify an object in the new image.

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 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: 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: 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: Technical solutions are described for training an object-recognition neural network that identifies an object in a computer-readable image. An example method includes assigning a first neural network for determining a visual alignment model of the images for determining a normalized alignment of the object. The method further includes assigning a second neural network for determining a visual representation model of the images for recognizing the object. The method further includes determining the visual alignment model by training the first neural network and determining the visual representation model by training the second neural network independent of the first. The method further includes determining a combined object recognition model by training a combination of the first neural network and the second neural network. The method further includes recognizing the object in the image based on the combined object recognition model by passing the image through each of the neural networks.

Abstract: A computer implemented method for detecting an object in a crowded scene utilizing an image capturing device. The method includes receiving an image of a predetermined area. From the image, the existence of selected portions as representing an entity of a selected class is determined. Each selected portion is assigned an initial confidence value that the selected portion is an entity representative of a selected class. Each selected portion is evaluated with each other to determine a context confidence value. The context confidence value and initial confidence value are utilized to determine which of the one or more selected portions are entities of a selected class.

Abstract: Software for static object detection that performs the following operations: (i) detecting an object that is present in at least one image of a set of images, wherein the set of images correspond to a time period; (ii) identifying a set of corner points for the detected object; (iii) tracking the object's presence in the set of images over the time period, wherein the object's presence is determined by matching the set of images to a template generated based on the identified corner points; and (iv) identifying the object as a static object when an amount of time corresponding to the object's presence in the set of images is greater than a predefined threshold.

Abstract: Field of view overlap among multiple cameras are automatically determined as a function of the temporal overlap of object tracks determined within their fields-of-view. Object tracks with the highest similarity value are assigned into pairs, and portions of the assigned object track pairs having a temporally overlapping period of time are determined. Scene entry points are determined from object locations on the tracks at a beginning of the temporally overlapping period of time, and scene exit points from object locations at an ending of the temporally overlapping period of time. Boundary lines for the overlapping fields-of-view portions within the corresponding camera fields-of-view are defined as a function of the determined entry and exit points in their respective fields-of-view.

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 method of operating an image detection device includes receiving an image, dividing the image into a plurality of patches, grouping ones of the plurality of patches, generating a set of saccadic paths through the plurality of patches of the image, generating a cluster-direction sequence for each saccadic path, generating a policy function for identifying an object in a new image using a combination of the cluster-direction sequences, and operating the image detection device using the policy function to identify an object in the new image.

Abstract: Images are retrieved and ranked according to relevance to attributes of a multi-attribute query through training image attribute detectors for different attributes annotated in a training dataset. Pair-wise correlations are learned between pairs of the annotated attributes from the training dataset of images. Image datasets may are searched via the trained attribute detectors for images comprising attributes in a multi-attribute query. The retrieved images are ranked as a function of comprising attributes that are not within the query subset plurality of attributes but are paired to one of the query subset plurality of attributes by the pair-wise correlations, wherein the ranking is an order of likelihood that the different ones of the attributes will appear in an image with the paired one of the query subset plurality of attributes.

Abstract: An approach that detects locations of hazardous conditions within an infrastructure is provided. This approach uses satellite imagery, GIS data, automatic image processing, and predictive modeling to determine the location of the hazards automatically, thus optimizing infrastructure management. Specifically, a hazard detection tool provides this capability. The hazard detection tool comprises a detection component configured to: receive visual media containing asset location data about a set of physical assets, and hazard location data about potential hazards within a vicinity of each of the set of physical assets. The detection component further receives graphical information system (GIS) data containing asset location data about each of the set of physical assets.

Abstract: An approach for re-identifying an object in a first test image is presented. Brightness transfer functions (BTFs) between respective pairs of training images are determined. Respective similarity measures are determined between the first test image and each of the training images captured by the first camera (first training images). A weighted brightness transfer function (WBTF) is determined by combining the BTFs weighted by weights of the first training images. The weights are based on the similarity measures. The first test image is transformed by the WBTF to better match one of the training images captured by the second camera. Another test image, captured by the second camera, is identified because it is closer in appearance to the transformed test image than other test images captured by the second camera. An object in the identified test image is a re-identification of the object in the first test image.