A method involves use of multiple convolutional neural networks and multiple segmentation masks to programmatically generate a stool rating for a digital image of a diaper with stool. The method includes generating, by a first convolutional neural network, a first mask representing an identification of an area of the digital image that corresponds to stool, and a second mask representing an identification of an area of the digital image that corresponds to a diaper. The method further includes generating a third mask representing an intersection of the first and second masks, and generating a modified digital image utilizing the third mask. The method further includes determining, by a second convolutional neural network, a stool rating for the digital image of the diaper with stool by utilizing the modified digital image as input for the second convolutional neural network.

The present teaching relates to system, method, medium for in-situ perception in an autonomous driving vehicle. A plurality of types of sensor data acquired continuously by a plurality of types of sensors deployed on the vehicle are first received, where the plurality of types of sensor data provide information about surrounding of the vehicle. Based on at least one model, one or more items are tracked from a first of the plurality of types of sensor data acquired by one or more of a first type of the plurality of types of sensors, wherein the one or more items appear in the surrounding of the vehicle. At least some of the one or more items are then automatically labeled on-the-fly via either cross modality validation or cross temporal validation of the one or more items and are used to locally adapt, on-the-fly, the at least one model in the vehicle.

The present disclosure provides a system for monitoring users/workplaces in real-time. The system includes video monitoring device(s) for capturing video frames of a user/workplace. The system also includes a client monitoring module including: a transceiving module for receiving credentials from the user; a user registration and authentication module for authenticating the user; and a processing module for: analysing the video frames in real-time to determine if object of interest(s) is present and accordingly determining at least one action of interest of the at least one object of interest; and determining if the object of interest is passing a predefined threshold. When object of interest passes the predefined threshold frames, the client monitoring module may analyse the video frames and take a preventive action or may send the video frames to a server for further analysis and preventive action.

Aspects relate to systems and methods for parsing and correlating solicitation video content. An exemplary system includes a computing device configured to receive a solicitation video related to a subject, where the solicitation video includes at least an image component and at least an audio component, where the audio component includes audible verbal content related to at least an attribute of the subject, transcribe at least a keyword as a function of the audio component, and associate the subject with at least a job description as a function of the at least a keyword.

Computer-implemented methods and systems of updating user identifiers in an image-sharing environment include features for facilitating blocking, permitting, sharing and/or modifying content such as images and videos. User identification vectors providing data representative of a user and information about one or more facial characteristics of the user are broadcasted by a modular computing device. Information about one or more additional characteristics of the user (e.g., body characteristics and/or contextual characteristics) as determined from images of the user obtained by one or more image capture devices are received. An updated user identification vector including the information about one or more additional characteristics of the user is stored at and subsequently broadcasted by the modular computing device.

An imaging system for creating an image of a target object comprising a mirror mounted on a gimbal and arranged to rotate about at least one axis, a gimbal drive unit configured to control the orientation of the gimbal, and a camera having its optical axis directed onto the mirror in order that an image reflected in the mirror is within a field of view of the camera, wherein the control unit is arranged to position the gimbal such that a reflection of a target object is within a field of view of the camera.

A system for digitizing gauges, lights and other human-readable machine gauges and functions and status without interfering with the operation of the machine or requiring re-working or interfering with the existing machine wiring, signaling, electrical or mechanical elements or operating modes, or adding new digitizing equipment to the machine.

Embodiments of the present disclosure provide methods, apparatus, systems, computing devices, computing entities, and/or the like for detecting objects located in an area of interest. In accordance with one embodiment, a method is provided comprising: receiving, via an interface provided through a general instance on a cloud environment, imaging data comprising raw images collected on the area of interest; upon receiving the images: activating a central processing unit (CPU) focused instance on the cloud environment and processing, via the image, the raw images to generate an image map of the area of interest; and after generating the image map: activating a graphical processing unit (GPU) focused instance on the cloud environment and performing object detection, via the image, on a region within the image map by applying one or more object detection algorithms to the region to identify locations of the objects in the region.

The technology described herein obfuscates image content using a local neural network and a remote neural network. The local network runs on a local computer system and a remote classifier runs in a remote computing system. Together, the local network and the remote classifier are able to classify images, while the image never leaves the local computer system. In aspects of the technology, the local network receives a local image and creates a transformed object. The transformed object may be generated by processing the image with a local neural network to generate a multidimensional array and then randomly shuffling data locations within a multidimensional array. The transformed object is communicated to the remote classifier in the remote computing system for classification. The remote classifier may not have the seed used to deterministically scramble the spatial arrangement of data within the multidimensional array.

Systems and methods are described for generating a plurality of three-dimensional (3D) proxy geometries of an object, generating, based on the plurality of 3D proxy geometries, a plurality of neural textures of the object, the neural textures defining a plurality of different shapes and appearances representing the object, providing the plurality of neural textures to a neural renderer, receiving, from the neural renderer and based on the plurality of neural textures, a color image and an alpha mask representing an opacity of at least a portion of the object, and generating a composite image based on the pose, the color image, and the alpha mask.