Abstract: A system for fall prevention includes a platform for supporting a patient, the platform including rails and rail sensors, an image capturing device for capturing images of the patient, a patient monitor for interactive communication with the patient, a health monitoring station for receiving rail sensor data and communicating with the patient monitor, and an edge network device co-located with the platform and communicating with the station and the image capturing device. The edge network device receives image data from the image capturing device when the rail sensors indicate that the rail is down and the patient is at risk to themselves, anonymizes the image data to generate a skeleton image, determines a posture associated with the skeleton image, triggers alerts at the station when the patient is predicted to be sitting, and triggers escalated alerts at the station when the patient is predicted to be standing.

Abstract: A computer implemented method for assessing an arterio-venous malformation (AVM) may include, for example, receiving a patient-specific model of a portion of an anatomy of a patient; using a computer processor to analyze the patient-specific model for identifying one or more blood vessels associated with the AVM, in the patient-specific model; and estimating a risk of an undesirable outcome caused by the AVM, by performing computer simulations of blood flow through the one or more blood vessels associated with the AVM in the patient-specific model.

Abstract: The present disclosure proposes a method and an apparatus for recommending sample data. The method may include: inputting a plurality of pieces of sample data to be classified into at least one preset classification model, and acquiring a classifying probability of classifying each piece of sample data into each classification model; acquiring a first distance between each piece of sample data and a classifying boundary of each classification model according to the classifying probability of classifying the piece of sample data into the classification model, in which the classifying boundary of the classification model is configured to distinguish positive and negative sample data; computing a target distance for each piece of sample data according to the first distance between each piece of sample data and the classifying boundary of each classification model.

Abstract: Systems and methods for a weakly supervised action localization model are provided. Example models according to example aspects of the present disclosure can localize and/or classify actions in untrimmed videos using machine-learned models, such as convolutional neural networks. The example models can predict temporal intervals of human actions given video-level class labels with no requirement of temporal localization information of actions. The example models can recognize actions and identify a sparse set of keyframes associated with actions through adaptive temporal pooling of video frames, wherein the loss function of the model is composed of a classification error and a sparsity of frame selection. Following action recognition with sparse keyframe attention, temporal proposals for action can be extracted using temporal class activation mappings, and final time intervals can be estimated corresponding to target actions.

Abstract: A data processing system for performing a semantic analysis of digital ink stroke data implements obtaining the digital ink stroke data representing handwritten text, drawings, or both; analyzing the digital ink stroke data to extract path signature feature information from the digital ink stroke data; analyzing the path signature feature information using a convolutional neural network (CNN) trained to perform a pixel-level sematic analysis of the digital ink stroke data and to output a pixel segmentation map with semantic prediction information for each pixel of digital ink stroke data; analyzing the pixel segmentation map to generate stroke-level semantic information using a pixel-to-stroke conversion model; and processing the digital ink stroke data based on the stroke-level semantic information.

Abstract: A computer-based system and method for generating a current pain assessment of a neonate using facial expressions along with crying sounds, body movement, and vital signs changes and for using the current pain objective assessment to predict future pain objective assessment and assign a future pain probability score by incorporation spatiotemporal data into the multimodal assessment.

Abstract: An intensity transform augmentation system is operable to receive a training set of medical scans. Random intensity transformation function parameters are generated for each medical scan of the training set of medical scans. A plurality of augmented images are generated, where each of the plurality of augmented images is generated by performing a intensity transformation function on one of the training set of medical scans by utilizing the random intensity transform parameters generated for the one of the training set of medical scan. A computer vision model is generated by performing a training step on the plurality of augmented images. A new medical scan is received via the receiver. Inference data is generated by performing an inference function that utilizes the computer vision model on the new medical scan. The inference data is transmitted to a client device for display via a display device.

Abstract: Method and system of training a machine learning neural network (MLNN) monitoring anatomical dynamics of a subject in motion. The method comprises receiving, in a first input layer of the MLNN, from a millimeter wave (mmWave) radar sensing device, mmWave radar point cloud data representing a first gait characteristic; receiving, in a second layer of the MLNN, from the mmWave radar sensing device, mmWave radar point cloud data representing a second gait characteristic; the first and the at least a second input layers being interconnected with an output layer via an intermediate layer having an initial matrix of weights; training a MLNN classifier based on a supervised classification establishing correlation between a degenerative condition of the subject at the output layer and the point cloud data; and adjusting the initial matrix of weights by backpropagation to increase correlation between the degenerative condition and the sets of point cloud data.

Abstract: Various disclosed embodiments are directed to classify or determining an image style of a target image according to a consumer application based on determining a similarity score between the image style of a target image and one or more other predetermined image styles of the consumer application. Various disclosed embodiments can resolve image style transfer destructiveness functionality by making various layers of predetermined image styles modifiable. Further various embodiments resolve tedious manual user input requirements and reduce computing resource consumption, among other things.

Abstract: Embodiments relate to a method for generating a video synopsis including receiving a user query; performing an object based analysis of a source video; and generating a synopsis video in response to a video synopsis generation request from a user, and a system therefor. The video synopsis generated by the embodiments reflects the user's desired interaction.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer-storage media, for fish weight estimation based on fish tracks identified in images. In some implementations, a method includes obtaining images of fish enclosed in a fish enclosure, identifying fish tracks shown in the images of the fish, determining a quality score for each of the fish tracks, selecting a subset of the fish tracks based on the quality scores, determining a representative weight of the fish in the fish enclosure based on weights of the fish shown in the subset of the fish tracks, and outputting the representative weight for display or storage at a device connected to the one or more processors.

Abstract: An image processor includes an imaging device that captures an image of a road surface around a vehicle V, and a control portion that detects a marker drawn on the road surface from the captured image. The control portion connects a plurality of broken markers to create a single marker when the detected marker is broken into plural.

Abstract: Disclosed are a method and apparatus for identifying versions of a form. In an example, clients of a medical company fill out many forms, and many of these forms have multiple versions. The medical company operates in 10 states, and each state has a different version of a client intake form, as well as of an insurance identification form. In order to automatically extract information from a particular filled out form, it may be helpful to identify a particular form template, as well as the version of the form template, of which the filled out form is an instance. A computer system evaluates images of filled out forms, and identifies various form templates and versions of form templates based on the images.

Abstract: A low-cost, low-power, stand-alone sensor platform having a visible-range camera sensor, a thermopile array, a microphone, a motion sensor, and a microprocessor that is configured to perform occupancy detection and counting while preserving the privacy of occupants. The platform is programmed to extract shape/texture from images in spatial domain; motion from video in time domain; and audio features in frequency domain. Embedded binarized neural networks are used for efficient object of interest detection. The platform is also programmed with advanced fusion algorithms for multiple sensor modalities addressing dependent sensor observations. The platform may be deployed for (i) residential use in detecting occupants for autonomously controlling building systems, such as HVAC and lighting systems, to provide energy savings, (ii) security and surveillance, such as to detect loitering and surveil places of interest, (iii) analyzing customer behavior and flows, (iv) identifying high performing stores by retailers.

Abstract: Apparatus and methods related to using machine learning to determine depth maps for dual pixel images of objects are provided. A computing device can receive a dual pixel image of at least a foreground object. The dual pixel image can include a plurality of dual pixels. A dual pixel of the plurality of dual pixels can include a left-side pixel and a right-side pixel that both represent light incident on a single dual pixel element used to capture the dual pixel image. The computing device can be used to train a machine learning system to determine a depth map associated with the dual pixel image. The computing device can provide the trained machine learning system.

Abstract: A system is configured to receive video footage of evaporator coil slabs after they exit an automated coil brazer. The system is further configured to convert the video footage to greyscale. The system is further configured to isolate frames from the greyscale video footage. Each frame comprises an image of a different evaporator coil slab. The system is further configured to identify a plurality of feature points in a first frame and a plurality of feature points in a second frame. The system is then configured to determine that a subset of features points in each frame are rotationally invariant. The system is further configured to generate a first digital fingerprint for each frame from a binary feature vector for each point in the subset of feature points determined to be rotationally invariant.

Abstract: A drive recorder according to an embodiment of the present disclosure includes: an imaging unit that is mounted on a vehicle and captures a video of the surroundings of the vehicle; a video recording unit that has, recorded therein, video data captured; a network connecting unit that receives accident information including a time and date when an accident occurred and a place where the accident occurred; and a video retrieving unit that determines whether any video data captured in a predetermined time period and in a predetermined region are available in the video data recorded in the video recording unit, the predetermined time period including the time and date when the accident occurred, the predetermined region including the place where the accident occurred.

Abstract: Systems and methods for full motion video search are provided. In one aspect, a method includes receiving one or more search terms. The search terms include one or more of a characterization of the amount of man-made features in a video image and a characterization of the amount of natural features in the video image. The method further includes searching a full motion video database based on the one or more search terms.

Abstract: An image processing apparatus includes a processor configured to extract a component related to luminance of each of a sample image and a processing target image that is to undergo image processing to match an impression of the processing target image to the sample image, extract feature values of the processing target image and the sample image by attaching to a pixel value of each pixel forming the processing target image and the sample image a weight responsive to the component related to the luminance, and make adjustment to match the feature value of the processing target image to the feature value of the sample image.

Abstract: Methods and systems for detecting and predicting anomalies include processing frames of a video stream to determine values of a feature corresponding to each frame. A feature time series is generated that corresponds to values of the identified feature over time. A matrix profile is generated that identifies similarities of sub-sequences of the time series to other sub-sequences of the feature time series. An anomaly is detected by determining that a value of the matrix profile exceeds a threshold value. An automatic action is performed responsive to the detected anomaly.