Abstract: Techniques are disclosed for generating a low-dimensional representation of an image. An image driver receives an image captured by a camera. The image includes features based on pixel values in the image, and each feature describes the image in one or more image regions. The image driver generates, for each of the plurality of features, a feature vector that includes values for that feature corresponding to at least one of the image regions. Each value indicates a degree that the feature is present in the image region. The image driver generates a sample vector from each of the feature vectors. The sample vector includes each of the values included in the generated feature vectors.

Abstract: An image processing apparatus performs a first detection that detects motion information of an image sensor during a period in which the image sensor captures a first image and a second image based on an output of a sensor different from the image sensor, a second detection that detects a plurality of motion vectors between the first image and the second image, a determination of first reliability of the motion information detected by the first detection and second reliability of the motion vectors detected by the second detection, and an alignment based on either one of the motion vectors or a vector indicated by the motion information, which has higher reliability, or a vector obtained from the motion vectors and the motion information according to the reliability.

Abstract: A set of healing abutments includes a first healing abutment and a second healing abutment. The first healing abutment has an upper surface with a first code thereon. The first code is associated with a size of the first healing abutment. The second healing abutment has an upper surface with a second code thereon. The second code is associated with a size of the second healing abutment. The first healing abutment and the second healing abutment have the same size. The first code is different from the second code.

Abstract: A method of recording an image using a particle microscope includes recording of plural images of an object. Each of the recorded images is associated with image data including intensity values associated with locations in a coordinate system of the recorded image. The method further includes: determining displacements between the coordinate systems of the image data of the recorded images; determining a bounding box of a resulting image based on the determined displacements; and calculating image data of the resulting image based on the intensity values of the image data of the recorded images associated with those locations which are located within the determined bounding box associated with the resulting image based on the determined displacements of the recorded images.

Abstract: Embodiments of the present disclosure provide a system and method for using facial recognition and mimicry workflows to train cognitive and emotional empathy. The system and methods comprise the use of physiological measurements (e.g., EEG, etc.) in combinations with facial recognition to detect user affect and modify one or more cognitive screening instruments to further promote a user affect. One or more specific emotions associated with one or more specific negative cognitive bias are targeted to influence affect. One or more specific psychopathology may be treated using an emotional recognition training component within a computerized cognitive-bias modification regimen. The regimen may comprise the identification, targeting, and modification of emotions and may utilize one or more facial inputs, cognitive tasks, facial recognition and facial mimicry protocols to adaptively modify one or more attributes of one or more computerized stimuli or interactions within a computerized platform or platform-product.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating descriptions of input images. One of the methods includes obtaining an input image; processing the input image using a first neural network to generate an alternative representation for the input image; and processing the alternative representation for the input image using a second neural network to generate a sequence of a plurality of words in a target natural language that describes the input image.

Abstract: A method of training a computer system for use in determining a transformation between coordinate frames of image data representing an imaged subject. The method trains a learning agent according to a machine learning algorithm, to determine a transformation between respective coordinate frames of a number of different views of an anatomical structure simulated using a 3D model. The views are images containing labels. The learning agent includes a domain classifier comprising a feature map generated by the learning agent during the training operation. The classifier is configured to generate a classification output indicating whether image data is synthesized or real images data. Training includes using unlabeled real image data to training the computer system to determine a transformation between a coordinate frame of a synthesized view of the imaged structure and a view of the structure within a real image.

Abstract: A structural analysis computing device may generate a proposed insurance claim and/or generate a proposed insurance quote for an object pictured in a three-dimensional (3D) image. The structural analysis computing device may be coupled to a drone configured to capture exterior images of the object. The structural analysis computing device may include a memory, a user interface, an object sensor configured to capture the 3D image, and a processor in communication with the memory and the object sensor. The processor may access the 3D image including the object, and analyze the 3D images to identify features of the object—such as by inputting the 3D image into a trained machine learning or pattern recognition program. The processor may generate a proposed claim form for a damaged object and/or a proposed quote for an uninsured object, and display the form to a user for their review and/or approval.

Abstract: Techniques for atmospheric compensation in satellite imagery that include converting an image including an array of radiance values to an array of surface reflectance values. The conversion is performed in an automated fashion by identifying one or more portions of the image for which the surface reflectance can be estimated and determining the Aerosol Optical Depth (AOD) by iteratively comparing the radiance value captured by the image sensor to a calculated radiance value (based on the known surface reflectance, historical values for other atmospheric parameters, and the AOD) and adjusting the AOD until the calculated radiance value is substantially the same as the captured radiance value.

Abstract: A method and apparatus for segmenting an image are provided. The method may include the steps of clustering pixels from one of a plurality of images into one or more segments, determining one or more unstable segments changing by more than a predetermined threshold from a prior of the plurality of images, determining one or more segments transitioning from an unstable to a stable segment, determining depth for one or more of the one or more segments that have changed by more than the predetermined threshold, determining depth for one or more of the one or more transitioning segments, and combining the determined depth for the one or more unstable segments and the one or more transitioning segments with a predetermined depth of all segments changing less than the predetermined threshold from the prior of the plurality of images.

Abstract: A panoramic image compression method and device is disclosed. The method comprises: obtaining a first spherical model formed by a first panoramic image to be compressed; generating a second spherical model in the first spherical model according to a main view image of an user; establishing a first mapping relationship between plane 2D rectangular coordinates in a second panoramic image and plane 2D rectangular coordinates in the first panoramic image; and sampling, from the first panoramic image, pixels corresponding to plane 2D rectangular coordinates in the second panoramic image according to the first mapping relationship to constitute the second panoramic image containing the pixels, so as to realize the compression of the first panoramic image.

Abstract: In one aspect, the present disclosure relates to a method for detecting fraud in image recognition systems, the method including receiving one or more input images from a device associated with a user, the one or more input images comprising a face associated with the user; identifying the face in the one or more input images with a computer vision technique; analyzing the one or more input images using a first neural network trained to recognize the user's face; analyzing the one or more input images using a second neural network trained to detect fraudulent images; determining whether the image is fraudulent or non-fraudulent; granting access to the user if the first neural network recognizes the user's face and the image is determined to be non-fraudulent; and preventing access to the user if the first neural network cannot recognize the user's face or the image is determined to be fraudulent.

Abstract: Methods, systems, and devices, including computer programs encoded on computer storage media, for selecting a target face are provided. One of the methods includes: obtaining at least one facial area including one or more faces in an image taken by a camera; determining, based on the image, a spatial distance between each of the one or more faces and the camera; and selecting, based on the spatial distance, the target face from the one or more faces.

Abstract: A gesture recognition system using siamese neural network executes a gesture recognition method. The gesture recognition method includes steps of: receiving a first training signal to calculate a first feature; receiving a second training signal to calculate a second feature; determining a distance between the first feature and the second feature in a feature space; adjusting the distance between the first feature and the second feature in feature space according to a predetermined parameter. Two neural networks are used to generate the first feature and the second feature, and determine the distance between the first feature and the second feature in the feature space for training the neural networks. Therefore, the gesture recognition system does not need a big amount of data to train one neural network for classifying a sensing signal. A user may easily define a new personalized gesture.

Abstract: A surface inspection apparatus includes: an inspection pattern forming unit that forms inspection patterns; a projection unit that projects the inspection patterns onto an inspection target object; a captured image acquiring unit that acquires captured images of the inspection target object; an edge extraction image creating unit that extracts edges from captured images, and creates edge extraction images; a correction coefficient setting unit that sets a correction coefficient for correcting intensities of edges in the edge extraction image; an intensity correcting unit that corrects the intensities of the edges; a corrected edge extraction image creating unit that creates corrected edge extraction images; an integrated image creating unit that creates a single integrated image by integrating the brightness values at the same position of the inspection target object; and a determination unit that determines the presence or absence of unevenness on a surface of the inspection target object.

Abstract: Various embodiments provide for visual similarity based search techniques that certain desirable visual attributes of one or more items to search for items having similar visual attributes. In order to create an electronic catalog of items that is searchable by parts-based visual attributes, the visual attributes are identified and corresponding feature vectors are extracted from the image data of each item. Thus, feature values of parts-based visual attributes of items in the electronic catalog can be determined and used to select or rank the items in response to a search query based on desirable visual attributes. To conduct a search, a user may define desirable visual attributes of one or more items. The feature vectors of the desirable visual attributes are determined and used to query the electronic catalog of items, in which items having visual attributes of similar feature vectors are selected and returned as search results.

Abstract: Methods, systems, and apparatus for segmenting and dimensioning objects are disclosed. An example method disclosed herein includes determining a first sensor of a plurality of sensors toward which a vehicle is moving based on image data generating by the plurality of sensors; designating the first sensor as a reference sensor; combining the image data from the plurality of sensors to generate combined image data representative of the vehicle and an object carried by the vehicle, the combining based on reference sensor; generating a plurality of clusters based on the combined image data; and identifying a first one of the clusters nearest the reference sensor as the object.

Abstract: An HDR imaging apparatus include: a deinterlacing circuit configured to generate a high-exposure Bayer frame and low-exposure Bayer frame by deinterlacing an interlace Bayer raw frame in which a high-exposure region and low-exposure region are interlaced; a demosaicing circuit configured to convert the high-exposure Bayer frame and the low-exposure Bayer frame into a high-exposure RGB frame and low-exposure RGB frame, respectively; a reconstructing circuit configured to remove interlace artifacts and noise from the high-exposure RGB frame and the low-exposure RGB frame, using joint dictionaries generated through joint dictionary learning on training data sets each containing a plurality of bases, but the corresponding bases have different values; and an HDR generation circuit configured to generate an HDR image frame by combining the high-exposure RGB frame and the low-exposure RGB frame from which interlace artifacts and noise were removed by the reconstructing circuit.

Abstract: A method and a system for optimizing an image capturing boundary in a proposed image for enhancing user experience while capturing an image are provided. The method includes locating an image capturing boundary in a proposed image and computing a composition measure for the image capturing boundary. Further, the method includes identifying at least one missing portion in the image capturing boundary based on the composition measure. Further, the method includes providing an indication, associated with an image capturing device, to optimize the image capturing boundary based on the identified at least one missing portion. Furthermore, the method includes computing an optimal zoom level automatically in response to actions performed by the user and captures the image by including the at least one missing portion.

Abstract: Computer processor hardware receives image data specifying element settings for each image of multiple original images in a sequence. The computer processor hardware analyzes the element settings across the multiple original images. The computer processor hardware then utilizes the element settings of the multiple original images in the sequence to produce first encoded image data specifying a set of common image element settings, the set of common image element settings being a baseline to substantially reproduce each of the original images in the sequence.