Abstract: A computer-implemented method of augmented reality includes capturing the video flux with a video camera, extracting, from the video flux, one or more 2D images each representing the real object, and obtaining a 3D model representing the real object. The method also includes determining a pose of the 3D model relative to the video flux, among candidate poses. The determining rewards a mutual information, for at least one 2D image and for each given candidate pose, which represents a mutual dependence between a virtual 2D rendering and the at least one 2D image. The method also includes augmenting the video flux based on the pose. This forms an improved solution of augmented reality for augmenting a video flux of a real scene including a real object.

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 device, system and method for generating an alert and an automatic search for a candidate subject is provided. The device monitors one or more data sources to identify a candidate subject. The device determines, from rules for rating candidate subjects stored in a memory, a rating of the candidate subject. When the rating meets a first threshold: the device transmits, to one or more communication devices, an alert identifying the candidate subject. When the rating meets a second threshold, lower than the first threshold: the devices provides, a classifier defining the candidate subject to an object recognition engine to cause the object recognition engine to automatically search for the candidate subject, using the classifier, in multimedia data received at one or more edge devices. The multimedia data may include images.

Abstract: This disclosure is directed to item-identifying carts that may generate and store image data of items being placed into, or removed from, the carts in the event that a cart is unable to recognize an item. For example, the cart may analyze frames of generated image data, representing item(s) being placed into or removed from the cart, to determine which one or more frames to store. If the cart is later unable to identify such an item, the cart may retrieve one or more of the stored frames, generate a user interface (UI) that includes these frame(s) and a request that a user operating the cart to help identify the item, and present the UI on a display of the cart.

Abstract: A method includes screening, by a video-clip screening module in a video description model, a plurality of video proposal clips acquired from a video to be analyzed, to acquire a plurality of video clips suitable for description. The plural video proposal clips acquired from the video to be analyzed may be screened by the video-clip screening module to acquire the plural video clips suitable for description; and then, each video clip is described by a video-clip describing module, thus avoiding description of all the video proposal clips, only describing the screened video clips which have strong correlation with the video and are suitable for description, removing the interference of the description of the video clips which are not suitable for description in the description of the video, guaranteeing the accuracy of the final descriptions of the video clips, and improving the quality of the descriptions of the video clips.

Abstract: Methods and systems for face recognition and response include extracting a face image from a video stream. A pre-processed index is searched for a watchlist image that matches the face image, based on a similarity distance that is computed from a normalized similarity score to satisfy metric properties. The index of the watchlist includes similarity distances between face images stored in the watchlist. An action is performed responsive to a determination that the extracted face image matches the watchlist image.

Abstract: System and method for crowdsourcing a video summary for creating an enhanced video summary are disclosed. The method includes receiving videos, analysing the videos, creating the video summary of the videos using a building block model, storing the video summary in a video library database, crowdsourcing the video summary to at least one of the plurality of users, enabling the at least one of the plurality of users to review the video summary and identify at least one new characteristic, enabling the at least one of the plurality of users to share the at least one new characteristic on the platform, comparing at least one existing characteristic of the building block model with the corresponding new characteristic, reconciling the video summary along with at least one inserted new characteristic, creating a new building block model, editing the video summary for creating the enhanced video summary.

Abstract: Disclosed is a method and system for assessing the authenticity of a communication. The method comprises receiving data of the communication by the processor between one or more participants. Further, extracting one or more features by the processor from the data by using data extraction techniques. Further, comparing the one or more features by the processor with predefined threshold features stored in a feature repository. Further, generating, one or more authenticity attributes by using one or more trained Artificial Intelligence (AI) models applied over the one or more features, along with results of the comparing. Each of the one or more authenticity attributes generates a recommendation output, providing the authenticity of the communication.

Abstract: The present application provides a method of corner detection and an image processing system for detecting corners in an image. The preferred implementation is in software using enabling and reusable hardware features in the underlying vector processor architecture. The advantage of this combined software and programmable processor datapath hardware is that the same hardware used for the FAST algorithm can also be readily applied to a variety of other computational tasks, not limited to image processing.

Abstract: Systems and methods for monitoring product placement. The method comprises accessing a first image depicting a plurality of items arranged in accordance with a first layout, accessing a second image, the second image depicting at least some of the plurality of items arranged in accordance with a second layout. The method then proceeds to inputting, to a machine learning algorithm (MLA), a first density map and a second density map, the first density map having been generated from the first image and the second density map having been generated from the second image. An anomaly map is then outputted by the ML, the anomaly map comprising a first indication of an item class associated with an anomaly and a second indication of a position associated with the anomaly.

Abstract: The systems and methods disclosed herein provide determination of an orientation of a feature towards a reference target. As a non-limiting example, a system consistent with the present disclosure may include a processor, a memory, and a single camera affixed to the ceiling of a room occupied by a person. The system may analyze images from the camera to identify any objects in the room and their locations. Once the system has identified an object and its location, the system may prompt the person to look directly at the object. The camera may then record an image of the user looking at the object. The processor may analyze the image to determine the location of the user's head and, combined with the known location of the object and the known location of the camera, determine the direction that the user is facing. This direction may be treated as a reference value, or “ground truth.” The captured image may be associated with the direction, and the combination may be used as training input into an application.

Abstract: Examples disclosed herein involve data stream analytics. In examples herein, a data stream may be analyzed by computing a set of hashes of a real-valued vector, the real-valued vector corresponding to a sample data object of a data stream; generating a list of data objects from a database corresponding to the sample data object based on the set of hashes, the list of data objects ordered based on similarity of the data objects to the sample data object of the data stream; and updating a data structure representative of activity of the sample data object in the data stream based on the list of data objects, the data structure to provide incremental analysis corresponding to the sample data object.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for finding lost objects. In some implementations, a video frame is displayed. An input drawing a shape around an area of the video frame is received. A second video frame is displayed. An indication of the shape in the second video frame is displayed. An input to adjust the shape such that the shape is drawn around a second area is received.

Abstract: A computer produces predictions throughout a raster field in response to point data, by obtaining a partially empty matrix of point data, filling a matrix of extrapolated raster data by dilating the point data in a first convolutional neural network, and generating a matrix of aggregate raster data by combining the extrapolated raster data with organic raster data in a second convolutional neural network.

Abstract: The information processing apparatus (2000) includes a feature point detection unit (2020), a determination unit (2040), an extraction unit (2060), and a comparison unit (2080). A feature point detection unit (2020) detects a plurality of feature points from the query image. The determination unit (2040) determines, for each feature point, one or more object images estimated to include the feature point. The extraction unit (2060) extracts an object region estimated to include the object in the query image in association with the object image of the object estimated to be included in the object region, on the basis of the result of the determination. The comparison unit (2080) cross-checks the object region with the object image associated with the object region and determines an object included in the object region.

Abstract: A convolutional neural network performs: a first masking process of masking a pixel region not to be inspected, by computing pixel values of corresponding pixels of an inspection image and of a mask image; an intermediate process for extracting a feature image from the inspection image that has been subjected to the first masking process; and a second masking process of masking the pixel region not to be inspected, by computing the pixel values of corresponding pixels of the inspection image that has been subjected to the intermediate process and of the mask image that has been subjected to a process identical to the intermediate process.

Abstract: Eyewear providing an interactive augmented reality experience between two users of eyewear devices to perform a shared group object manipulation task. During the shared group task, each user of the eyewear controls movement of a respective virtual object in a virtual scene based on a portion of the virtual scene the user is gazing at. Each user can also generate a verbal command to generate a virtual object that interacts with one or more of the other virtual objects.

Abstract: Systems and methods may be used to generate and use a structured form representation and structured metadata. The structured form representation and structured metadata may include information relevant to a particular context and may be used to update document templates, import new documents and update document versions into software, automate data entry for document completion, update records to include new and or updated information, and provide other functionality of an information service.

Abstract: An aspect provides a computer-implemented method for compiling software code. The method comprises: receiving software code to compile; receiving a set of parameters associated with settings and software employed to compile the software code; forming a first hash of the set of parameters to establish a unique identification of the set of parameters used to compile the software code; and associating the first hash with the compiled code. A further aspect provides a computer-implemented method of checking compatibility of compiled software code.

Abstract: An information processing apparatus includes a determination unit configured to determine a printing area and a non-printing area in image data, and an execution unit configured to execute first multiplexing processing by changing a U component in a YUV color space with respect to an area determined as a printing area in the image data and to execute second multiplexing processing by changing a Y component in a CMYK color space with respect to an area determined as a non-printing area in the image data.

Abstract: Provided are an image processing apparatus and an image processing method that enable a strobe image using a 3D model to be generated. A strobe model in which 3D models of an object at a plurality of times generated from a plurality of viewpoint images captured from a plurality of viewpoints are disposed in a three-dimensional space is generated. When the strobe model is generated, a target object that is a target in which the 3D model is disposed in the strobe model is set according to a degree of object relevance indicating relevance with a key object serving as a reference for disposition of the 3D model in the strobe model.

Abstract: In one aspect, a computerized method for anti-counterfeiting solution using a machine learning (ML) model includes the step of providing a pre-defined set of feature detection rules, a pre-defined set of edge detection rules, a pre-defined threshold percentage, an original seal, an original fingerprint of the original seal, and a pre-trained fingerprint identification model. The pre-trained fingerprint identification model is trained by a specified ML algorithm using one or more digital images of the original seal. With a digital camera of a scanning device, the method scans a seal whose authenticity is to be determined. The seal is used to secure a transportation container. The method uses the pre-defined set of feature detection rules to detect and extract an extracted feature image at a specified position on the seal. The method breaks down the extracted feature image of the seal into a ‘kn’ number of sub-images by forming a ‘k’ rows x ‘n’ columns of a grid of the extracted feature image.

Abstract: Disclosed are a drinking under influence (DUI) detection system and a DUI detection method based on an MQ3 sensor and an ultra wide band (UWB) radar. The system includes an alcohol detection module, a respiratory detection module, a signal matching module and a drunk driving alarm module, and each module cooperates to complete a detection. The MQ3 sensor-based alcohol detection module is used for alcohol signal capture, alcohol sequence processing and DUI threshold detection; the UWB radar-based respiratory detection module is used for respiratory signal capture, respiratory signal processing and passenger separation and positioning; the signal matching module is used for periodic signal alignment, sequence feature matching and drinker identity confirmation; and the DUI alarm module is used for a system alarm prompt, a data visualization interface and a DUI information uploading.

Abstract: Disclosed is a process for performance monitoring and feedback for facial recognition systems. A first image for image matching from a camera capture device at a first location is received for purposes of image matching. A highest match confidence score of the first image to a particular stored enrollment image is determined. One or more image or user characteristics associated with the first image or first user is identified. The identified image or user characteristics and highest match confidence score are added to a facial recognition monitoring and feedback model. Subsequently, a particular one of the stored image or user characteristics consistently associated with a below-threshold highest match confidence score is identified, and a notification is displayed or transmitted including an indication of an identified facial recognition low match pattern and identifying the particular one of the stored image characteristics or user characteristics.

Abstract: An apparatus and method to train an image recognition model to accurately estimate a location of a reference point for each class of landmark is disclosed. The apparatus and method use the image recognition model, which is trained based on calculating a class loss and a class-dependent localization loss from training data based on an image recognition model and training the image recognition model using a total loss comprising the class loss and the localization loss.

Abstract: Systems and methods for learning and recognizing features of an image are provided. A point detector identifies points in an image where there are two-dimensional changes. A geometric feature evaluator overlays at least one mesh on the image and analyzes geometric features on the at least one mesh. An internal calibrator transforms data from the point detector and the geometric feature evaluator into a three-dimensional point figure of the image, and a depth evaluator determines a final shape of the image. A three-dimensional object model of the image is constructed. The image could be a human face or body. Exemplary systems and methods can construct and learn features of a human face based on a partial view where part of the face is covered. Systems and methods can unlock a mobile device based on recognition of the features of the user's face.

Abstract: A vehicle computer comprises a processor and a memory. The memory stores instructions executable by the processor to detect debris flying above a roadway, to input vehicle sensor data to a first classifier that outputs a source of the debris, and based on the source of the debris, to compare sensor data representing the debris to stored reference data to determine a type of physical material included in the debris. The memory stores instruction to input the type of physical material and an environmental condition to a second classifier that outputs a risk assessment, and to actuate the vehicle based on the risk assessment.

Abstract: The disclosure relates to systems, methods and programs for geometrically constrained, unsupervised training of convolutional autoencoders on unlabeled images for extracting eye landmarks. Disclosed systems for unsupervised deep learning of gaze estimation in eyes' image data are implementable in a computerized system. Disclosed methods include capturing an unlabeled image comprising the eye region of a user; and training a plurality of convolutional autoencoders on the unlabeled image comprising the eye region of a user using an initial geometrically regularized loss function to determine a plurality of eye landmarks.

Abstract: A video game processing program product causes a user terminal to realize functions to control progress of a video game. The functions include: an updating function configured to update a position of a character; a first displaying function configured to display, in a normal mode, a normal game screen; and a second displaying function configured to display, in a photographing mode, a photographing game screen based on a photographing operation. In a first case where a user operates the user character to change position while the photographing screen is displayed in the photographing mode, a change in position of the user character does not affect a status of the normal mode. The status of the normal mode of the video game is configured to be updated in a second case while the photographing screen is displayed in the photographing mode.

Abstract: Systems for sorting seeds are disclosed, as well as batches of seeds that have been sorted using the systems.

Abstract: Disclosed in some examples are methods, systems, devices, and machine-readable mediums which encode data into a geometric representation for more efficient and secure processing. For example, data may be converted from a binary representation to a geometric representation using an encoding dictionary. The encoding dictionary specifies one or more geometric shapes used in the encoding. The geometrically encoded data may comprise one or more identifiers that specify one or more of the shapes of the encoding dictionary that best match one or more detected features in an image corresponding to the data. In some examples, the geometrically encoded data may also comprise one or more transformations of the one or more shapes to reduce error in the geometric encoding.

Abstract: A device is configured to classify data. Its operation involves providing (210) data samples including one or more of: image data, radar data, acoustic data, and/or lidar data to a processing unit. The data samples include at least one test sample, including positive samples and negative samples. Each positive sample has been determined to contain data relating to at least one object to be detected including at least one pedestrian, car, vehicle, truck or bicycle. Each negative sample has been determined not to contain data relating to the at least one object to be detected. These determinations regarding the positive samples and the negative samples are provided as input data, validated by a human operator, and/or provided by the device itself through a learning algorithm. A first plurality of groups is generated (220) by the processing unit implementing an artificial neural network, wherein at least some of the first plurality of groups are assigned a weighting factor.

Abstract: Embodiments provide functionality for identifying joints and limbs in images. An embodiment extracts features from an image to generate feature maps and, in turn, processes the feature maps using a single convolutional neural network trained based on a target model that includes joints and limbs. The processing generates both a directionless joint confidence map indicating confidence with which pixels in the image depict one or more joints and a directionless limb confidence map indicating confidence with which the pixels in the image depict one or more limbs between adjacent joints of the one or more joints, wherein adjacency of joints is provided by the target model. To continue, indications of the one or more joints and the one or more limbs in the image are generated using the directionless joint confidence map, the directionless limb confidence map, and the target model. Embodiments can be deployed on mobile and embedded systems.

Abstract: A system described herein may provide a technique for the use of machine learning techniques to perform authentication, such as biometrics-based user authentication. For example, user biometric information (e.g., facial features, fingerprints, voice, etc.) of a user may be used to train a machine learning model, in addition to a noise vector. A representation of the biometric information (e.g., an image file including a picture of the user's face, an encoded file with vectors or other representation of the user's fingerprint, a sound file including the user's voice, etc.) may be iteratively transformed until the transformed biometric information matches the noise vector, and the machine learning model may be trained based on the set of transformations that ultimately yield the noise vector, when given the biometric information.

Abstract: A computer-implemented method and an electrical impedance analyzing system for analyzing electrical impedance measurements of an electrochemical battery, including: a translating of respective electrical impedance measurements that corresponds to subtracting an ohmic alternating current resistance value from the real part of electrical impedance; and a scaling using scaling factors, the absolute values of which are based on an electrical impedance that corresponds to a transition between a translated first subseries and a translated second subseries of the series of electrical impedance measurements, the first subseries being associated with a battery behavior of the electrochemical battery in which the electrical impedance is dominated by diffusion processes, the second subseries being associated with a battery behavior of the electrochemical battery in which the electrical impedance is dominated by charge transfer processes at the electrodes of the battery.

Abstract: Methods, apparatus, and processor-readable storage media for software-defined suspected storage drive failure identification are provided herein. An example computer-implemented method includes implementing at least one software agent in an operating system associated with at least one storage system, wherein the at least one software agent is configured to monitor and process one or more predefined storage drive attributes; obtaining, using the at least one software agent, attribute values for the one or more predefined storage drive attributes from one or more storage drives within the at least one storage system; identifying, using the at least one software agent, at least one suspected failure among the one or more storage drives by processing the obtained attribute values using algorithmic logic; and performing at least one automated action based on the at least one identified suspected failure among the one or more storage drives.

Abstract: A method, an apparatus, and a terminal for recognizing a two-dimensional code are provided. The two-dimensional code includes an image region and an encoding region. The image region and the encoding region have no overlap. The method includes selecting, from pixels of the two-dimensional code, a pixel included in a code element in an encoding region. The method further includes determining a value of the code element in the encoding region according to the pixel included in the code element in the encoding region. The method further includes recognizing the two-dimensional code according to the value of the code element in the encoding region.

Abstract: An autonomous mobile device (AMD) moving in a physical space determines the presence of obstacles using images acquired by a stereocamera and avoids those obstacles. A floor with few visible features is difficult to characterize. The floor and any obstacles thereon may be difficult to characterize due to noise, perspective effect, and so forth. A score is determined that indicates whether a particular pixel in an image is deemed to be associated with a parallel surface (the floor) or a perpendicular surface (an obstacle). The score is computationally inexpensive to calculate and allows for highly accurate and low latency determinations as to the presence of an obstacle that would impede movement of the AMD. Orientation changes in the AMD, such as movement over a bumpy floor, are well tolerated as are floor features such as flooring transitions, ramps, and so forth which the AMD is able to traverse.

Abstract: Embodiments of this application disclose a video data processing method and apparatus, and a storage medium. After historical and current video frames are acquired from a video stream, two point position sets of a human body are obtained from the historical video frame and the current video frame, respectively. A jitter amplitude of a human body key point in the current video frame is analyzed according to the point position set of the human body in the current video frame and the point position set of the human body in the historical video frame; an adjustment reference position of the human body in the current video frame is determined according to the jitter amplitude; and subsequently a human body part below the adjustment reference position in the current video frame is adjusted and the adjusted current video frame is outputted for display as part of the video stream.

Abstract: A method for determining a proximity of at least one object using an electronic device is provided. The method includes obtaining hover data from a touch panel of the electronic device. Further, the method includes determining at least one of a first hover or a second hover based on the hover data of the at least one object obtained for the touch panel. Further, the method includes determining the proximity of the at least one object to the touch panel of the electronic device based on at least one of the first hover or the second hover.

Abstract: An information processing apparatus includes a processor configured to, when extracting character information for search information corresponding to a type of a target from an image obtained by photographing the target, receive type information indicating the type of the target, and output first guidance information for providing a guidance to acquire a first image including at least search information associated with the type information and a vicinity of the search information.

Abstract: A system and method for classifying images of an evidence, the method including: generating an unlabeled template based on a first received image, wherein the unlabeled template includes a first set of regions of interest (ROIs); selecting a labeled template that includes a second set of ROIs from a plurality of labeled templates determined to be a best match for the unlabeled template; checking at least an ROI of the first set of ROIs for content that corresponds to a label of an equivalent ROI of the second set of ROIs; and, classifying the first image upon determination that the content checked corresponds to the label according to the labeled template.

Abstract: A mobile communication system based on images, enabling communication between a plurality of mobile devices and servers, wherein the images have associated additional properties in the nature of voice, audio, data and other information. The system further enabling the formation of one or more image networks wherein the images are stored, organized, connected and linked to each other by one or more methods inclusive of one to one connection between images, a hierarchical connection between images and or other methods of connection between images to facilitate efficient image based communication between mobile devices, stationary devices and servers based on the mobile device identification, mobile device specific profiles and user specific profiles.

Abstract: Systems for dynamic profile control are provided. A system may receive first check data that may include a number of checks written over a predetermined time period for an account. The first check data may be analyzed to determine the number of checks written in a predetermined time period and the number may be compared to one or more thresholds. Based on the comparison, a first tier level may be identified for the account. A profile record may be generated including a check profile that may include one or more parameters based on the identified first tier level. The system may then build the profile by adding checks to the profile upon determining that the checks meet predefined criteria. After a predetermined time period has expired, the system may process additional data to determine whether the identified tier level and associated parameters should be modified.

Abstract: A method for box filtering includes obtaining, by a computing device, a form image, and identifying, by the computing device, a region of the form image that includes boxes. Vertical lines in the region of the form image are detected. The boxes in the region are detected according to the plurality of vertical lines, and image content is extracted from the boxes.

Abstract: Systems and methods for obscuring data from a data source include devices and processes that may objectively measure the information loss for a dataset that is caused by applying a privacy policy, and may select and apply a policy to the dataset based on the measured information loss. The systems and methods may measure the information loss for a large dataset by taking a representative sample from the dataset and applying the policy to the sample in order to quantify the information loss. The quantified information loss can be iteratively used to change the policy in order to meet utility and/or privacy goals, and the system can subsequently apply the changed policy to the dataset.

Abstract: An electronic device according to certain embodiments may include a camera, a memory, and at least one processor, wherein the at least one processor is configured to: obtain an image through the camera; obtain an image of at least one object from the obtained image; determine whether the image of the at least one object corresponds to an image of an object stored in the memory; output about a request for information about the at least one object, based at least in part on the determination; and obtain the information about the at least one object, based at least in part on information received from an external source.

Abstract: In some examples, an apparatus includes a management controller for use in a computer system having a processing resource for executing an operating system (OS) of the computer system, the management controller being separate from the processing resource and to perform, based on operation of the management controller within a cryptographic boundary, management of components of the computer system, the management of components comprising power control of the computer system. The management controller is to receive sensor data, perform facial recognition based on the sensor data, and determine whether to initiate a security action responsive to the facial recognition.

Abstract: An information processing apparatus comprising a situation acquiring unit configured to acquire a situation of one movable object, and a storing execution unit configured to store a face image of an occupant on the one movable object, captured by an image-capturing unit mounted on the one movable object when the one movable object is in a predetermined situation, as a face image of the occupant with neutral facial expression is provided.

Abstract: Embodiments of this disclosure provide an action recognition method and apparatus and an electronic equipment. The action recognition apparatus includes: a key-point feature extracting unit configured to process information on key points in a target human body in an image frame, to calculate key-point features of the target human body, the key-point features including a position of a predetermined key point and an angle of a predetermined joint in the target human body; and a first recognizing unit configured to recognize an action of the target human body according to the key-point features, and output a first recognition result.