Abstract: An investigation assist system includes a server communicably connected to a first camera, one or more second cameras and a third camera; and a client terminal communicably connected to the server. The first camera captures a wide range of an intersection, the one or more second cameras capture identification information of a license plate and a face of a person in a vehicle entering the intersection, and the third camera captures outdoors. The server acquires and performs video analysis on videos thereby holding a video analysis result. The client terminal receives input of notified information including a date and a location on and at which an incident has occurred, determines to start a first investigation process, or a second investigation process, and tracks a suspect with the first investigation process or an escaping vehicle with the second investigation process.

Abstract: A method for validation of an obstacle candidate identified within a sequence of image frames comprises the following steps: A. for a current image frame of the sequence of image frames, determining within the current image frame a region of interest representing the obstacle candidate, dividing the region of interest into sub-regions, and, for each sub-region, determining a Time-To-Contact (TTC) based on at least the current image frame and a preceding or succeeding image frame of the sequence of image frames; B. determining one or more classification features based on the TTCs of the sub-regions determined for the current image frame; and C. classifying the obstacle candidate based on the determined one or more classification features.

Abstract: A control device in a control system has a lighting control section which requests so as to cause the lighting state of an indicator lamp of a specific control unit to change in a predetermined identification pattern, and the control device or a terminal has: an indicator lamp recognition section which recognizes the indicator lamp in an image; a change pattern recognition section which recognizes a change in the lighting state of the indicator lamp in the image; an indicator lamp specification section which specifies the indicator lamp having a change pattern in lighting state which matches the identification pattern, as the indicator lamp of the control unit which the lighting control section caused the lighting state to change in the identification pattern; and a display control section which causes information related to the control unit to be displayed on the monitor of the terminal.

Abstract: A defect inspection method for a sensor package structure includes: using an image capture device to separately focus on and take pictures of at least three to-be-inspected regions of the sensor package structure along a height direction, so as to respectively obtain a defect image from one of the to-be-inspected regions, wherein the defect images are aligned with each other along the height direction and have different grayscale values; determining the defect image having a maximum grayscale value as a reference defect image, and defining any of the remaining defect images as a to-be-confirmed defect image; multiplying the maximum grayscale value by a predetermined grayscale ratio to obtain a predicted grayscale value, and confirming whether a difference between the to-be-confirmed and predicted grayscale values falls within an error range.

Abstract: A method and system for determining depth information of an image are herein provided. According to one embodiment, the method includes receiving an image input, classifying the input image into a depth range of a plurality of depth ranges, and determining a depth map of the image by applying depth estimation based on the depth range into which the input image is classified.

Abstract: Biometrics are increasingly used to provide authentication and/or verification of a user in many security and financial applications for example. However, “spoof attacks” through presentation of biometric artefacts that are “false” allow attackers to fool these biometric verification systems. Accordingly, it would be beneficial to further differentiate the acquired biometric characteristics into feature spaces relating to live and non-living biometrics to prevent non-living biometric credentials triggering biometric verification. The inventors have established a variety of “liveness” detection methodologies which can block either low complexity spoofs or more advanced spoofs. Such techniques may provide for monitoring of responses to challenges discretely or in combination with additional aspects such as the timing of user's responses, depth detection within acquired images, comparison of other images from other cameras with database data etc.

Abstract: A method and device for image recognition and a storage medium are provided. The method includes: a target image is acquired; feature extraction processing is performed on the target image through convolutional layers in a neural network model to obtain feature maps, and Instance Normalization (IN) and Batch Normalization (BN) processing is performed on the feature maps to obtain a recognition result of the target image; and the recognition result of the target image is output.

Abstract: Empirically modulated antenna systems and related methods are disclosed herein. An empirically modulated antenna system includes an antenna and a controller programmed to control the antenna. The antenna includes a plurality of discrete scattering elements arranged in a one- or two-dimensional arrangement. A method includes modulating operational states of at least a portion of a plurality of discrete scattering elements of the antenna in a plurality of different modulation patterns. The plurality of different modulation patterns includes different permutations of the discrete scattering elements operating in different operational states. The method also includes evaluating a performance parameter of the antenna responsive to the plurality of different empirical one- or two-dimensional modulation patterns. The method further includes operating the antenna in one of the plurality of different one- or two-dimensional empirical modulation patterns selected based, at least in part, on the performance parameter.

Abstract: Examples described herein include systems and methods, including wireless devices and systems with neuron calculators that may perform one or more functionalities of a wireless transceiver. The neuron calculator calculates output signals that may be implemented, for example, using accumulation units that sum the multiplicative processing results of ordered sets from ordered neurons with connection weights for each connection between an ordered neuron and outputs of the neuron calculator. The ordered sets may be a combination of some input signals, with the number of signals determined by an order of the neuron. Accordingly, a kth-order neuron may include an ordered set comprising product values of k input signals, where the input signals are selected from a set of k-combinations with repetition. As an example in a wireless transceiver, the neuron calculator may perform channel estimation as a channel estimation processing component of the receiver portion of a wireless transceiver.

Abstract: Systems and methods for generating a centerline for an object in an image are provided. An exemplary method includes receiving an image containing the object. The method also includes generating a distance cost image using a trained first learning network based on the image. The method further includes detecting end points of the object using a trained second learning network based on the image. Moreover, the method includes extracting the centerline of the object based on the distance cost image and the end points of the object.

Abstract: Various deficiencies in the prior art are addressed by systems, methods, architectures, mechanisms and/or apparatus configured to fuse data received from a plurality of sensor sources on a network. The fusing data includes forming an empirical distribution for each of the sensor sources, reformatting the data from each of the sensor sources into pre-rotational alpha-trimmed depth regions, applying an affine transformation rotation to each of the reformatted data to form post-rotational pre-rotational alpha-trimmed depth regions, and reformatting each affine transformation into a new data fusion operator.

Abstract: To support a wide range of code rates in probabilistic shaping based modulation schemes solutions, a constellation which is at least based on a trained model is used in the modulation. Depending on an implementation the trained model may be a constellation comprising a plurality of sub-constellations with trained parameters as constellation points, or the trained model may be for generating a constellation and corresponding constellation points.

Abstract: Aspects of the present disclosure may include methods, apparatuses, and computer readable media for receiving a plurality of images having a plurality of interactions associated with a plurality of objects, identifying a plurality of spatial features from the plurality of images, identifying a plurality of spatial-temporal features from the plurality of spatial features, obtaining a past trajectory data of the target, generating a relational state of the target and the plurality of interactions based at least on the past trajectory data, the plurality of spatial-temporal features, or the plurality of descriptive relations, and generating a plurality of heatmaps indicating a projected trajectory of the target.

Abstract: Generally discussed herein are systems, devices, and methods for improved automatic toll generation. A system can include one or more cameras positioned to capture images of vehicles passing through a toll station, vehicle detection and classification circuitry to receive an image of a vehicle from the one or more cameras and execute a first machine learning (ML) model to classify the vehicle, axle count circuitry to, in response to receiving data from the vehicle detection and classification circuitry that a vehicle was detected, execute a second ML model to classify a number of axles of the vehicle for determining a toll charge, and toll circuitry to issue the toll charge based on the classified number of axles.

Abstract: A feature fusion and dense connection-based method for infrared plane object detection includes: constructing an infrared image dataset containing an object to be recognized, calibrating a position and class of the object to be recognized in the infrared image dataset, and obtaining an original known label image; dividing the infrared image dataset into a training set and a validation set; performing image enhancement preprocessing on images in the training set, performing feature extraction and feature fusion, and obtaining classification results and bounding boxes through a regression network; calculating a loss function according to the classification results and the bounding boxes in combination with the original known label image, and updating parameter values of a convolutional neural network; repeating the steps to iteratively update the parameters of the convolutional neural network; and processing images in the validation set through the parameters to obtain a final object detection result map.

Abstract: A similar picture identification method is described. According to the method, processing circuitry of a device obtains n first local regions of a first picture, the n first local regions being a first set of regions that is affine invariant, and obtains m second local regions of a second picture, the m second local regions being a second set of regions that is affine invariant. The processing circuitry obtains n first characteristic values respectively corresponding to the n first local regions, and obtains m second characteristic values respectively corresponding to the m second local regions. The processing circuitry further determines, according to a comparison result of the n first characteristic values and the m second characteristic values, whether the first picture is similar to the second picture, where n and m are positive integers.

Abstract: Disclosed embodiments provide systems and methods for transforming real-world images for augmentation of image data for improvement of training data sets utilized by machine learning algorithms to provide autonomous and/or assistive driving functionality.

Abstract: A computer enabled method for locating a misplaced object that includes receiving at an object locating system a request defining an object to be located, the input data request being received audibly by a digital assistant. The method may include analyzing the request with the object locating system by classifying the object to be located with an image search of a corpus of images correlated to comparison objects; and receiving images from Internet of Thing devices (IoT) devices of search objects. The method further includes comparing the images correlated to comparison objects from the image search with images of the search objects to identify retrieval objects that match the object classified by the object locating system that analyzed the request. The location of the retrieval object is then identified.

Abstract: A method performed by an electronic device is described. The method includes receiving an image. The image depicts a face. The method also includes detecting at least one facial landmark of the face in the image. The method further includes receiving a depth image of the face and determining at least one landmark depth by mapping the at least one facial landmark to the depth image. The method also includes determining a plurality of scales of depth image pixels based on the at least one landmark depth and determining a scale smoothness measure for each of the plurality of scales of depth image pixels. The method additionally includes determining facial liveness based on at least two of the scale smoothness measures. Determining the facial liveness may be based on a depth-adaptive smoothness threshold and/or may be based on a natural face size criterion.

Abstract: A method for determining an object comprises: determining a reference representation based on positions of a first reference point and a second reference point of a first object in a reference image, which indicates a distance and a relative direction between the first and second reference points in the reference image; determining a detection representation based on positions of the first and second reference points in a detection frame including the first object in a video, which indicates a distance and a relative direction between the first and second reference points in the detection frame; determining rotation parameters associated with a first predetermined point and a second predetermined point in a second object associated with the first object based on the reference representation and the detection representation; and adjusting a target representation between the first and second predetermined points in the second object based on the rotation parameter.