Abstract: Systems and methods disclosed herein include a method implemented by a computing system, the method comprising: (i) creating a first and second user profile associated with a recreational activity; (ii) receiving a first and second user input, wherein the first and second user input comprises selecting the first and second user profile associated with the recreational activity; (iii) in response to receiving the first or second user input, recording a first and second live video feed of a first and second portion of the recreational activity; and (iv) executing an automated video processing procedure.

Abstract: Systems, methods, and devices are described that may mitigate display pixel and touch crosstalk noise. A touch processing system may compensate touch scan data to reduce the noise based on a luminance value. An image processing system may determine the luminance value based on image data and a display brightness value of an electronic display. Using the compensated touch scan data, the touch processing system may determine a proximity of a capacitive object to at least one touch sense region of the electronic display.

Abstract: Systems and methods for tracking an object in video surveillance are provided. A method may include obtaining a video including a plurality of consecutive frames; obtaining a current frame from the plurality of consecutive frames, wherein an object of interest is identified in at least two previous frames of the current frame; obtaining at least two template frames from the at least two previous frames; and identifying a position related to the object of interest in the current frame based on the at least two template frames using a correlation filter.

Abstract: A method for at least one of detecting and classifying a wanted signal in an electromagnetic signal is described. The method includes the following steps: the electromagnetic signal is received; a spectrogram of the electromagnetic signal is determined; at least one correlation parameter, for example a correlation multi-dimensional algebraic object including several correlation parameters, is determined based on the determined spectrogram; the at least one correlation parameter is used as an input for a machine learning module; the wanted signal in the electromagnetic signal is detected and/or classified via the machine learning module based at least partially on the at least one correlation parameter. Further, a signal detection and/or classification system and a computer program are described.

Abstract: Technologies for summarizing tax documents that include an unstructured portion, such as K1 filings. The system extracts data from both the structured information, such as a K1 facepage, and unstructured information, such as whitepaper statement(s). The system includes machine learning model(s) to determine the information to be extracted from the unstructured information. The machine learning model(s) generate a confidence level associated with the extracted unstructured information that represents a prediction on how likely the extracted unstructured information was accurately extracted. The system generates a document in an electronic interchange format that represents both the structured and unstructured information in the analyzed tax document.

Abstract: In vision-based authentication platforms for secure resources such as computer systems, false positives and/or false negatives in the detection of walk-away events are reduced or eliminated by incorporating depth information into tracking authenticated system operators.

Abstract: A robot and a method of capturing an image applied to the robot, an electronic device for implementing the method of capturing the image, and a computer-readable storage medium are provided. The robot includes: a robot body; a workbench; a telescopic structure having one end pivotally connected to the robot body and the other end connected to the workbench; a driving mechanism arranged on the robot body and configured to drive the telescopic structure to extend, retract and/or move relative to the robot body; and an image capture device arranged on the workbench. The telescopic structure is configured to allow the image capture device to capture an image of a target object from different angles with the extension, retraction and/or movement of the telescopic structure.

Abstract: A sentiment analysis system is described that uses a machine learning model to predict a mood of an individual based on the motions used by the individual when performing sign language. The present disclosure uses image processing to monitor how a non-verbal individual communicates to, for example, predict overall health and mental well-being.

Abstract: Techniques for evaluating height data are disclosed herein. Height values for a tracked object are obtained over a specified period of time. Clusters including at least one of the height values are generated. The cluster having the largest amount of height values is identified. A height of the tracked object is estimated from the identified cluster.

Abstract: The present invention improves an accuracy to discern a fake finger created by attaching a transparent thin film to the surface of a finger. The present invention has: a mounting surface that has a mounting area to mount an authentication target that is an object of fingerprint authentication; a transparent plate that is provided on mounting surface and defines a range of an image used for determining the authenticity of the authentication target; a light source that allows light to directly enter the authentication target mounted on the mounting area, not by way of the transparent plate; and an imaging device that images the authentication target mounted on the mounting area, by way of a the transparent plate, wherein a light emitting surface for emitting light of the light source to outside is disposed on the same plane as the mounting surface.

Abstract: An abnormality detection apparatus is provided. The abnormality detection apparatus includes a first generation part configured to generate pseudo-abnormal image data by synthesizing a substantially circular image at a random position of an image of normal image data obtained by photographing equipment that includes a liquid supply and supplies a liquid from the liquid supply without an abnormality, a second generation part configured to generate a determination model for determining whether the equipment is normal or abnormal by performing learning of the normal image data and the pseudo-abnormal image data, an acquisition part configured to acquire image data obtained by photographing the equipment, and a detection part configured detect an abnormality in the equipment from the image data acquired by the acquisition part using the determination model.

Abstract: Disclosed are a system and a method for still image-based media content monitoring and, more specifically, a system and a method for still image-based media content monitoring, in which a copied content obtained by illegally copying an original content, which is a video content such as a movie content or a broadcast content, is monitored on the basis of a still image, thereby preventing illegal distribution of the copied content.

Abstract: A facility providing systems and methods for discovering novel features to use in machine learning techniques. The facility receives, for a number of subjects, one or more sets of data representative of some output or condition of the subject over a period of time or capturing some physical aspect of the subject. The facility then extracts or computes values from the data and applies one or more feature generators to the extracted values. Based on the outputs of the feature generators, the facility identifies novel feature generators for use in at least one machine learning process and further mutates the novel feature generators, which can then be applied to the received data to identify additional novel feature generators.

Abstract: Image fingerprints (embeddings) are generated by an image fingerprinting model and indexed with an approximate nearest neighbors (ANN) model trained to identify the most similar fingerprint based on a subject embedding. For image matching, a score is provided that indicates a similarity between the input embedding and the most similar identified embedding, which allows for matching even when an image has been distorted, rotated, cropped, or otherwise modified. For image classification, the embeddings in the index are clustered and the clusters are labeled. Users can provide just a few images to add to the index as a labeled cluster. The ANN model returns a score and label of the most similar identified embedding for labeling the subject image if the score exceeds a threshold. As improvements are made to the image fingerprinting model, a converter model is trained to convert the original embeddings to be compatible with the new embeddings.

Abstract: Image processing techniques can be used to determine whether a person has installed an unauthorized device on or in a kiosk. For example, an image processing server can determine whether a person's hand(s) are engaging in abnormal behavior by obtaining recorded images of the person's hand(s) that extend towards the kiosk to perform an action, and by comparing the information in the recorded images with a set of previously known information about other persons' hands that are similarly extended towards the kiosk.

Abstract: A facial expression recognition method includes extracting a first feature from color information of pixels in a first image, and extracting a second feature of facial key points from the first image. The method further includes combining the first feature and the second feature, to obtain a fused feature, and determining, by processing circuitry of an electronic device, a first expression.

Abstract: Improving configuration of routing areas within an area of interest can include obtaining input data including a list of common language location identifiers within a defined area of interest and identification of a number of routing areas to be created within the area of interest; creating a bounding rectangle around the area of interest represented by the list of common language location identifiers; generating, within the bounding rectangle, a uniformly distributed sequence point; determining, from within the list of common language location identifiers, if the uniformly distributed sequence point is located in a particular common language location identifier of the list of common language location identifiers; if so, adding, to a list of kernel common language location identifiers the uniformly distributed sequence point; and if not, discarding the uniformly distributed sequence point.

Abstract: In some implementations, a method includes: obtaining uncorrected hand tracking data; obtaining a depth map associated with a physical environment; identifying a position of a portion of the finger within the physical environment based on the depth map and the uncorrected hand tracking data; performing spatial depth smoothing on a region of the depth map adjacent to the position of the portion of the finger; and generating corrected hand tracking data by performing point of view (POV) correction on the uncorrected hand tracking data based on the spatially depth smoothed region of the depth map adjacent to the portion of the finger.

Abstract: Assessment of pulmonary function in coronavirus patients includes use of a computer aided diagnostic (CAD) system to assess pulmonary function and risk of mortality in patients with coronavirus disease 2019 (COVID-19). The CAD system processes chest X-ray data from a patient, extracts imaging markers, and grades disease severity based at least in part on the extracted imaging markers, thereby distinguishing between higher risk and lower risk patients.

Abstract: A learning processing device that is based on a neural network model and image data obtained by capturing an image of the object to be inspected, and constructs the neural network model used for inspecting the object to be inspected. The learning processing device is provided with a learning unit which performs a learning process under a prescribed learning condition on the basis of a list of the image data including a plurality of learning images and constructs the neutral network model. The learning unit embeds unique model identification data in the neural network model, whenever the neural network model is constructed.

Abstract: A front-end image acquisition component acquires photographs and/or videos of the exterior of a vehicle traveling on a path between a first predefined location and a second predefined location. In one aspect, an image acquisition system comprises a first camera support structure, a second camera support structure, an image processor, and a memory. Cameras are affixed to each of the camera support structures for acquiring image data of a subject vehicle. Image data is controlled by the image processor and the memory. In another aspect, an image acquisition system comprises a first camera tower, a second camera tower, a camera boom. Cameras are affixed to each of the camera towers.

Abstract: This disclosure addresses the single-image compressive sensing (CS) and reconstruction problem. A scalable Laplacian pyramid reconstructive adversarial network (LAPRAN) facilitates high-fidelity, flexible and fast CS image reconstruction. LAPRAN progressively reconstructs an image following the concept of the Laplacian pyramid through multiple stages of reconstructive adversarial networks (RANs). At each pyramid level, CS measurements are fused with a contextual latent vector to generate a high-frequency image residual. Consequently, LAPRAN can produce hierarchies of reconstructed images and each with an incremental resolution and improved quality. The scalable pyramid structure of LAPRAN enables high-fidelity CS reconstruction with a flexible resolution that is adaptive to a wide range of compression ratios (CRs), which is infeasible with existing methods.

Abstract: Setting or verification of a monitoring rule in response to a monitoring target environment is supported. An information processing system includes a situation information receiving unit that receives an input of situation information indicating a situation in a monitoring target environment. The information processing system further includes a normal situation storage unit. The normal situation storage unit stores environment information indicating the monitoring target environment in association with a set of situation information indicating a situation that is not abnormal in the monitoring target environment. The information processing system further includes a retrieval unit. The retrieval unit refers to the normal situation storage unit upon receiving the input of the situation information indicating the information in the monitoring target environment.

Abstract: A system and method for tracking an object comprising: an image input gateway arranged to receive a series of image frames from a stream of images, wherein the stream of images includes representations of the object being spatially displaced over the series of image frames; an object tracker arranged to process the series of image frames to track the object with a first object location process arranged to locate the object in the series of image frames; and, when upon the object is unable to be located by the first object location process in any of the image frames within the series of image frames, use a second object location process to locate the object in the image frames or subsequent image frames for tracking the object through the remaining series of image frames.

Abstract: In a system for efficiently organizing, storing, and/or accessing project data, and for visualizing project progress, for a specified project, a fractal-based or bioinspired reference structure is selected. A file organization and storage structure is generated based on the reference structure. The reference structure includes tags that can indicate the appropriate storage locations for different types of project-related data based on the nature of the data. To storage a file, the name and/or contents of the file are analyzed and matched with one or more tags, so that the file can be stored at the location identified by the best matching tag.

Abstract: The present invention provides an image processing apparatus including: an image acquisition unit acquiring a candidate of a query image; a skeletal structure detection unit detecting a two-dimensional skeletal structure of a person included in a candidate of the query image; a query evaluation unit computing an evaluation value of a candidate of the query image, based on a detection result of the two-dimensional skeletal structure; a selection unit selecting a query image out of candidates of the query image, based on the evaluation value; a feature value computation unit computing a feature value of the two-dimensional skeletal structure detected from the query image; and a retrieval unit retrieving an analysis target image including a person in a pose similar to a pose of a person included in the query image out of analysis target images, based on a degree of similarity of the computed feature value.

Abstract: User interaction concepts, principles and algorithms for gestures involving facial expressions, motion or orientation of body parts, eye gaze, tightening muscles, mental activity, and other user actions are disclosed. User interaction concepts, principles and algorithms for enabling hands-free and voice-free interaction with electronic devices are disclosed. Apparatuses, systems, computer implementable methods, and non-transient computer storage media storing instructions, implementing the disclosed concepts, principles and algorithms are disclosed. Gestures for systems using eye gaze and head tracking that can be used with augmented, mixed or virtual reality, mobile or desktop computing are disclosed. Use of periods of limited activity and consecutive user actions in orthogonal axes is disclosed. Generation of command signals based on start and end triggers is disclosed. Methods for coarse as well as fine modification of objects are disclosed.

Abstract: The present disclosure provides an efficient system for cancelable biometric template protection which is not available in the conventional systems. Initially, the system receives a biometric template from a user. The biometric template is one of, a face image, a fingerprint, an iris image and a palmprint. Further, a vector embedding is computed based on the biometric template using a pretrained neural network. After computing the vector embedding, a key based permutation is computed based on the vector embedding and a permutation key. The permutation key is generated based on an Identification number (ID) associated with the user using a random number based key generation technique. Finally, a protected biometric template is generated by computing a permuted embedding corresponding to the biometric template based on the key based permutation using a plurality of fully connected layers pretrained using a weight based center triplet loss function.

Abstract: The present disclosure relates to an image processing apparatus and an image processing method capable of reducing an increase in a load imposed by processing of multispectral image data. Multispectral image data including components in four or more wavelength bands is simple-coded at a fixed compression rate. Alternatively, coded data generated by simple coding at a fixed compression rate for multispectral image data including components in four or more wavelength bands is simple-decoded. For example, the present disclosure is applicable to an image processing apparatus, an image coding apparatus, an image decoding apparatus, an imaging element, an imaging apparatus, or the like.

Abstract: A decoder includes a memory and a processor coupled to the memory. The processor is configured to obtain first information from a bitstream, the first information indicating a number of first points in a current picture. The processor is also configured to determine second information provided for each of the first points based on the first information, the second information indicating a difference between a coordinate value of a first point and a coordinate value of a corresponding second point, the corresponding second point being included in a reference picture. The processor is further configured to generate a prediction image of a current block by performing an affine prediction according to the determined second information.

Abstract: An eye state detecting method, applied to an electronic apparatus with an image sensor, which comprises: (a) acquiring a detecting image via the image sensor; (b) defining a face range on the detecting image; (c) defining a determining range on the face range; and (d) determining if the determining range comprises an open eye image or a close eye image.

Abstract: An object recognition ingestion system is presented. The object ingestion system captures image data of objects, possibly in an uncontrolled setting. The image data is analyzed to determine if one or more a priori know canonical shape objects match the object represented in the image data. The canonical shape object also includes one or more reference PoVs indicating perspectives from which to analyze objects having the corresponding shape. An object ingestion engine combines the canonical shape object along with the image data to create a model of the object. The engine generates a desirable set of model PoVs from the reference PoVs, and then generates recognition descriptors from each of the model PoVs. The descriptors, image data, model PoVs, or other contextually relevant information are combined into key frame bundles having sufficient information to allow other computing devices to recognize the object at a later time.

Abstract: Example systems, devices, media, and methods are described for controlling the presentation of one or more virtual or graphical elements on a display in response to bimanual hand gestures detected by an eyewear device that is capturing frames of video data with its camera system. An image processing system detects a first hand and defines an input plane relative to a surface of the detected first hand. The image processing system also detects a series of bimanual hand shapes, including the detected first hand and at least one fingertip of a second hand. In response, the system presents a first movable element on the display at a location that is correlated with the current fingertip location. In addition, the image processing system determines whether the detected series of bimanual hand shapes matches a predefined hand gesture. In response to a matching gesture, the system executes a selecting action of an element nearest the current fingertip location.

Abstract: Disclosed are a face image and iris image acquisition method and device, a computer-readable readable storage medium and an apparatus. The method includes rotating the first tripod head to force the face lens and the iris lens to be in acquisition positions; capturing a first face image and a first iris image simultaneously by the face lens and the iris lens; and locating the iris in the first iris image, and if no iris is located, determining whether a condition of light-avoiding rotation is satisfied, and if the condition is satisfied, rotating the second tripod head to adjust an angle or a position of the supplementary light source to enable a light spot region to avoid an iris region.

Abstract: An information processing device according to the present disclosure includes an inference unit that infers, on the basis of a result of checking a first image against a plurality of images taken in the past, a third image that is an image taken in the past at a position corresponding to a second image to be taken at a next timing of the first image, and a generation unit that generates a fourth image that is an image obtained by correcting the second image on the basis of the third image in a case where the second image is acquired.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer-storage media, for lighting adaptive navigation. In some implementations, a method includes receiving map data associated with a property; obtaining sensor data; based on the map data and the sensor data, determining a lighting scenario; and based on the lighting scenario, configuring the robotic device.

Abstract: The technology of this application related to an image processor comprising a plurality of modules, the plurality of modules comprising a first module and a second module, wherein the image processor is configured to receive an input image and output a plurality of mathematical descriptors for characteristic regions of the input image. The first module is configured to implement a first trained artificial intelligence model to detect a set of characteristic regions in the input image; and the second module is configured to implement a second trained artificial intelligence model to determine a mathematical descriptor for each of said set of characteristic regions. The first and second trained artificial intelligence models are collectively trained end to end.

Abstract: A method of determining an optimal target gemstone to be obtained from a rough gemstone comprises obtaining a first series of 2D images of the rough gemstone; providing a 3D model of a target gemstone to be obtained from the rough gemstone; and generating a second series of 2D images of the target gemstone from the 3D model thereof. The method then comprises comparing the first and second series of 2D images to determine an optimal transformation to be applied to the 3D model of the target gemstone.

Abstract: Embodiments are directed to calibrating multi-view triangulation systems that perceive surfaces and objects based on reflections of one or more scanned laser beams that are continuously sensed by two or more sensors. In addition to sampling and triangulating points from a spline formed by an unbroken line trajectory of a laser beam, the calibration system samples and triangulates a corresponding velocity vector. Iterative reduction is performed on velocity vectors instead of points or splines. The velocity vector includes directions and magnitudes along a trajectory of a scanning laser beam which are used to determine the actual velocities. Translation and rotation vectors are based on the velocity vectors for matching trajectories determined for two or more sensors having offset physical positions, which are used to calibrate sensor offset errors associated with the matching trajectories provided to a modeling engine.

Abstract: This disclosure describes a system for automatically updating item image information stored in an item images data store and used for processing captured images to identify items represented in those images. In one implementation, once an identity of an item has been verified, captured images of that item are associated with the item and stored in the item images data store. As a result, the item images data store is updated each time an image of the item is captured and the identity of the item is verified.

Abstract: A camera apparatus includes control circuitry, where in a training phase, the control circuitry is configured to generate a set of binary images of foliage masks, where each binary image comprises one or more foliage regions demarcated from a non-foliage region. A modified training dataset of color images is generated and a custom neural network model for foliage detection is trained. The control circuitry causes the custom neural network model to not only learn a plurality of features related to foliage but also learn a color variation range of predefined color associated with the plurality of features and utilize combination of the plurality of features related to foliage and the color variation range to obtain trained custom neural network model. The control circuitry is configured to operate the trained custom neural network model to detect one or more foliage regions in a new color image in real time.

Abstract: The present disclosure provides a training method and device for an image identifying model, and an image identifying method. The training method comprises: obtaining image samples of a plurality of categories; inputting image samples of each category into a feature extraction layer of the image identifying model to extract a feature vector of each image sample; calculating a statistical characteristic information of an actual distribution function corresponding to each category according to the feature vector of each image sample of the each category; establishing an augmented distribution function corresponding to the each category according to the statistical characteristic information; obtaining augmented sample features of the each category based on the augmented distribution function; and inputting feature vectors of the image samples and the augmented sample features into a classification layer of the image identifying model for supervised learning.

Abstract: Disclosed are a bone age estimation method and a bone age estimation apparatus. The bone age estimation method may comprise the steps of: extracting a region of interest including a cervical spine region from a lateral cephalometric radiographic image obtained by imaging a subject's cervical spine, by using a first deep learning model; extracting landmarks from the extracted region of interest by using a second deep learning model; calculating a landmark numerical value on the basis of the extracted landmarks; and providing maturity information of a maturation stage of the cervical spine on the basis of the calculated landmark numerical value.

Abstract: A method and system for improving prediction of an incident affecting public safety is described. At a machine learning analytics system, the method includes obtaining a real-time video for a location of a public safety emergency call and predicting a public safety incident therefrom. At a learning processor, the method includes: receiving information related to the public safety emergency call; identifying a public safety incident type based thereon; receiving the prediction of the public safety incident; using a first public safety incident machine learning model based on the identified public safety incident type and comparing the public safety incident prediction with the information related to the public safety emergency call and identifying an incident prediction mismatch therefrom. The learning processor identifies an artifact in the real-time video that is a contributing factor for the incident prediction mismatch; and implements a model improvement action associated with the artifact.

Abstract: Unsupervised, deep learning of eye-landmarks in a user-specific eyes' image data by capturing an unlabeled image comprising an eye region of a user, using an initial geometrically regularized loss function, training a plurality of convolutional autoencoders on the unlabeled image comprising the eye region of the user to recover a plurality of user-specific eye landmarks, training a convolutional neural network for autoencoded landmarks-based recovery from the unlabeled image, and where the initial geometrically regularized loss function is represented by the formula LAE=?reconLrecon+?concLconc+?sepLsep+?eqvLeqv where LAE is total AutoEncoder Loss, ?reconLrecon is ?-weighted reconstruction loss, ?concLconce is ?-weighted concentration loss, ?sepLsep is ?-weighted separation loss, and ?eqvLeqv is ?-weighted equivalence loss.

Abstract: Feature descriptor matching is reformulated into a graph-matching problem. Keypoints from a query image and a reference image are initially matched and filtered based on the match. For a given keypoint, a feature graph is constructed based on neighboring keypoints surrounding the given keypoint. The feature graph is compared to a corresponding feature graph of a reference image for the matched keypoint. Relocalization data is obtained based on the comparison.

Abstract: An electronic device is provided. The electronic device includes at least one processor, and a memory functionally connected to the at least one processor. The memory may store instructions that, when executed, enable the electronic device to obtain a plurality of images, generate a first basic extended image based on first images among the plurality of images, identify at least one first masking area included in the first basic extended image, and generate a first inference image by modifying the at least one first masking area using at least one first inference result, based on the first images and the first basic extended image. An angle of view of the first inference image may be larger than an angle of view of each of the first images.

Abstract: A meter readout on a meter has digits including a first digit, a second digit, etc. A sequence of images of the meter is obtained. The images include images of the digits in the meter readout. Automated recognition of the digits in the images result in likelihood arrays indicating the likelihoods for the digit values for the digits imaged in the meter images. Short chains of digit values are identified and spliced together to form a series of single digit, two-digit, three-digits, etc. paths that are built up based on the likelihood arrays. Various criteria are used to discard most of the chains and thereby avoid the combinatorial explosion of possible paths and thereby produce reliable meter readings without consuming considerable computational resources.

Abstract: Example posture analysis systems and methods are described. In one implementation, a computing system identifies a deviation of a user's posture from a predetermined posture based on visual data associated with the user. The user is informed of the deviation via a graphical image displayed on a display device. The user is then provided with instructions for correcting the deviation via the graphical image displayed on the display device.

Abstract: Processes and systems for displaying information received in a query response. One system includes a communication device including a user interface and an electronic processor. The electronic processor transmits a query, and receives a response to the query including stored information in each of a plurality of fields of information about an object of interest. The electronic processor receives an image of the object from a camera, identifies visually descriptive text information describing the object, and categorizes at least a portion of the visually descriptive text information into the plurality of fields of information. The electronic processor determines a confidence level for each field of information based on a comparison of the stored information and the visually descriptive text information in each field, and displays the stored information in one or more fields for which the confidence level of the comparison is below a predetermined threshold.