Abstract: Briefly, a variety of embodiments, including the following, are described: a system embodiment and methods that allow random access to voice messages, in contrast to sequential access in existing system embodiments; a system embodiment and methods that allow for the optional use of voice recognition to enhance usability; and a system embodiment and methods that apply to the area of voicemail.

Abstract: Briefly, a variety of embodiments, including the following, are described: a system embodiment and methods that allow random access to voice messages, in contrast to sequential access in existing system embodiments; a system embodiment and methods that allow for the optional use of voice recognition to enhance usability; and a system embodiment and methods that apply to the area of voicemail.

Abstract: A system for contextual interpretation of a three-dimensional scene includes an object recognition engine that analyzes scene data collected from the three-dimensional scene to identify at least one object present in the three-dimensional scene. The system further includes a contextual inference engine trained on a context data training set to analyze context of the scene by identifying a potential contextual inference associated in memory with the at least one object identified by the object recognition engine; comparing the scene data to a subset of the context data training set identified as satisfying the potential contextual inference; and outputting scene context information conveying the potential contextual inference responsive to a determination that the scene data and the subset of the context data train set satisfy a predetermined correlation.

Abstract: Various image analysis techniques are disclosed herein that automatically assess the damage to a rooftop of a building or other object. In some aspects, the system may determine the extent of the damage, as well as the type of damage. Further aspects provide for the automatic detection of the roof type, roof geometry, shingle or tile count, or other features that can be extracted from images of the rooftop.

Abstract: To provide a technique of obtaining information for discriminating two substances included in a radiation image of a subject without performing imaging by changing radiation energy, a radiation imaging system includes a detector including a plurality of pixels which obtain pixel values corresponding to incident radiation transmitted through a subject, and an information processing unit configured to perform a process of estimating information on thicknesses and densities of substances included in the subject by performing a calculation using pixel values of an arbitrary one of the plurality of pixels and an average value of energy of radiation quanta of the arbitrary pixel calculated in accordance with the pixel values of the arbitrary pixel.

Abstract: An image processing apparatus includes a scale-down factor determination unit that generates two-dimensional images of an object viewed from a plurality of viewpoints using three-dimensional data representing a three-dimensional shape of the object and determines a scale-down factor for each viewpoint, a template generation unit that scales down the generated two-dimensional image from each viewpoint using the scale-down factor determined for the viewpoint and calculates a feature quantity from a scaled-down image to generate a template, and a template information output unit that outputs, for each viewpoint, the template and the scale-down factor used for generating the template in a manner associated with each other.

Abstract: Technology disclosed herein may involve a computing system that (i) generates (a) a first feature map based on a first visual input from a first perspective of a scene utilizing at least one first neural network and (b) a second feature map based on a second visual input from a second, different perspective of the scene utilizing at least one second neural network, where the first perspective and the second perspective share a common dimension, (ii) based on the first feature map and a portion of the second feature map corresponding to the common dimension, generates cross-referenced data for the first visual input, (iii) based on the second feature map and a portion of the first feature map corresponding to the common dimension, generates cross-referenced data for the second visual input, and (iv) based on the cross-referenced data, performs object detection on the scene.

Abstract: Systems and methods for performing depth estimation may comprise: an illuminator capable of illuminating a scene from at least a first position and a second position, an image sensor to capture (i) a first image of the scene while the illuminator illuminates the scene from the first position and (ii) a second image of the scene while the illuminator illuminates the scene from the second position, and an image processor to receive the first and second images from the image sensor and estimate a depth of at least one feature that appears in the first and second images. The depth is estimated based on the relative intensity of the first image and the second image, a distance between the first illumination position and the second illumination position, and a position of the at least one feature within at least one of the first and second images.

Abstract: A gate apparatus includes: an exit gate door; a first biometrics information acquisition unit that acquires, from a user who moves toward the exit gate door in a closed state, first target biometrics information to be compared with registered biometrics information registered in advance; a second biometrics information acquisition unit that acquires second target biometrics information to be compared with the registered biometrics information from the use who stops in front of the exit gate door when there is no matching in a comparison between the first target biometrics information and the registered biometrics information or the comparison is unable to be performed; and a door control unit that opens the closed exit gate door in accordance with a result of a comparison between the first target biometrics information or the second target biometrics information and the registered biometrics information.

Abstract: A process for operating a machine guided photo and video composition system involves generating processed image data. The process operates an object detection engine to identify objects and object locations in the processed image data. The process operates a computer vision analysis engine to identify geometric attributes of objects. The process operates an image cropping engine to select potential cropped image locations within the processed image data. The image cropping engine generates crop location scores for each of the potential cropped image locations and determine highest scored cropped image location. The image cropping engine communicates a highest crop location score to a score evaluator gate. The process generates object classifications from the object locations and the geometric attributes. The process receives device instructions at a user interface controller by way of the score evaluator gate. The method displays device positioning instructions through a display device.

Abstract: A method for vehicle damage identification, includes: obtaining a vehicle damage picture to be identified; inputting the vehicle damage picture into a plurality of pre-trained target detection models respectively, and obtaining corresponding detection results from the plurality of target detection models as a detection result set, wherein the detection result set comprises candidate bounding boxes detected by the plurality of target detection models and category prediction results of the candidate bounding boxes; determining an integrated feature vector of a first candidate bounding box of the candidate bounding boxes; and separately inputting integrated feature vectors corresponding to the candidate bounding boxes into a pre-trained classification model, and optimizing the detection result set according to output results of the classification model.

Abstract: Systems, apparatuses and methods may provide for detecting a facial image including generating a spatial convolutional neural network score for one or more detected facial images from a facial image detector, generating a temporal convolutional network score for detected facial video frames from the facial image detector and generating a combined spatial-temporal score to determine whether a detected facial image gains user access to a protected resource.

Abstract: Ultrasound image data on a handheld imaging probe can be compressed on the handheld imaging probe prior to transmission from the probe in order to decrease the amount of data transmitted from the probe. The compressed data may comprise compressed pixels to maintain spatial image resolution. The compression circuitry may comprise an amount of memory related to a dynamic range of the compressed data that is independent of the dynamic range of the input data, which can decrease memory, power consumption, and latencies. The compression circuitry can be configured to pipeline data such as image data in accordance with clock cycles, such that throughput of outputting data words is increased.

Abstract: A system for video surveillance includes an image sensor configured capture an image including a reference zone and a memory device that stores instructions. The system also includes one or more processors that are configured to execute the instructions to determine 3D coordinates of a target comprised in the image and determine 3D coordinates of the reference zone. The one or more processors are further configured to identify an event according to the 3D coordinates of the target and the 3D coordinates of the reference zone.

Abstract: The invention relates to a method of Computed Tomography imaging comprising: a. Performing a single acquisition of image data from at least two contrast agents into a blood vessel network, a first contrast agent among said at least two contrast agents having been in said blood vessel network for a longer time than a second contrast agent among said at least two contrast agents, b. Processing said image data using K-Edge detection and/or iodine delineation to separate data associated with each contrast agents in order to obtain a concentration map of each contrast agent, c. Determining from said image data a first part of the blood vessel network comprising both the first contrast agent and the second contrast agent, and a second part of the blood vessel network comprising only the first contrast agent, d. Calculating a partial blood volume map of the first part of the blood vessel network based on the total amount of second contrast agent and on the concentration map of the second contrast agent, e.

Abstract: Systems and methods are provided for evaluating the complexity of a stenosis or a section of a vessel. At least one image of the stenosis or the section of the vessel is provided. A geometrical feature value of the stenosis and/or or the section of the vessel is identified from the at least one image. At least one intensity feature value is determined based on a grey level intensity of the stenosis or the section of the vessel from the at least one image. A complexity value relating to the geometrical complexity of the stenosis or the section of the vessel is calculated as a function of the at least one geometrical feature value and the at least one intensity feature value of the stenosis or the section of the vessel.

Abstract: Aspects of the subject disclosure may include, for example, analyzing media content to recognize an object therein, wherein the media content is provided as a video stream displayed to a user of a communication device, the media content comprising a plurality of digital frames transported to the communication device over a private network; associating product information of a product with the object; receiving a user input selecting the object; determining providers of goods or services of the product, wherein the providers of goods or services are selected during the determining based on a proximity of the providers to the user of the communication device; and providing the product information to the communication device. Other embodiments are disclosed.

Abstract: In one example implementation according to embodiments of the invention, a computer-implemented method includes identifying a first discrete element of the composite object from a first image. The method further includes identifying the composite object based at least in part on the first discrete element of the composite object. The method further includes determining whether a category associated with the composite object is a category of interest. The method further includes, based at least in part on determining that the category associated with the composite object is a category of interest, increasing a discrete element count. The method further includes determining whether the discrete element count meets a threshold. The method further includes, based at least in part on determining that the discrete element count exceeds the threshold, performing a mitigation action.

Abstract: Disclosed are Computer Aided Diagnosis (CAD) apparatus and method to combine information on sequential image frames and to provide a superior classification result for the ROI in the image frame. The CAD apparatus may include a Region of Interest (ROI) detector configured to detect an ROI from image frames, a categorizer configured to create groups of image frames having successive ROI sections from among the image frames based on a result of the detection, a classifier configured to classify an ROI detected from each of the image frames belonging to the groups, and a result combiner configured to combine classification results for the image frames belonging to each group from the groups and to calculate a group result for the each group.

Abstract: Disclosed systems and methods relate to remote sensing, deep learning, and object detection. Some embodiments relate to machine learning for object detection, which includes, for example, identifying a class of pixel in a target image and generating a label image based on a parameter set. Other embodiments relate to machine learning for geometry extraction, which includes, for example, determining heights of one or more regions in a target image and determining a geometric object property in a target image. Yet other embodiments relate to machine learning for alignment, which includes, for example, aligning images via direct or indirect estimation of transformation parameters.