An image recognition device and method retrieve information on a marker. The marker encodes information identifying an asset by encoding the information in a binary pattern as recesses and non-recesses in the marker. The image recognition is performed by imaging the marker, mapping contrast in the image, identifying variations in the contrast, creating a mesh overlaying the image, identifying the present or absence of recesses from the mesh, and reading the binary pattern represented by the recesses in the marker.

Multiple field of view (FOV) systems are disclosed herein. An example system includes a bioptic barcode reader having a target imaging region. The bioptic barcode reader includes at least one imager having a first FOV and a second FOV and is configured to capture an image of a target object from each FOV. The example system includes one or more processors configured to receive the images and a trained object recognition model stored in memory communicatively coupled to the one or more processors. The memory includes instructions that, when executed, cause the one or more processors to analyze the images to identify at least a portion of a barcode and one or more features associated with the target object. The instructions further cause the one or more processors to determine a target object identification probability and to determine whether a predicted product identifies the target object.

Systems and methods for detecting aortic aneurysms using ensemble based deep learning techniques that utilize numerous computed tomography (CT) scans collected from numerous de-identified patients in a database. The system includes software that automates the analysis of a series of CT scans as input (in DICOM file format) and provides output in two dimensions: (1) ranking CT scans by risks of adverse events from aortic aneurysm, (2) providing aortic aneurysm size estimates. A repository of CT scans may be used for training of deep neural networks and additional data may be drawn from localized patient information from institutions and hospitals which grant permission.

A method and a system for determining an indicator of processing quality of an agricultural harvested material using a mobile device is disclosed. A computing unit analyzes image data of a prepared sample of harvested material containing grain components and non-grain components in an analytical routine to determine the indicator of the processing quality of the agricultural harvested material. Further, the computing unit uses a trained machine learning model in the analytical routine to perform at least one step of determining the indicator of the processing quality of the agricultural harvested material.

An electronic document system can allow users to upload a document package containing multiple individual component documents. Each component document includes a subset of a plurality of pages that are included in the document package. The electronic document system identifies a page of each component document by applying a machine learning model to the document package. The electronic document system partitions the document package into the individual component documents based on the identified pages. For each individual component document, the electronic document system identifies a document topic corresponding to the component document by applying another machine learning model. The electronic document system modifies a user interface to display each component document and corresponding document topic.

A memory device is provided including instructions that, when executed, cause one or more processors to perform the steps including receiving a plurality of images acquired by a camera, the plurality of images including a plurality of optical patterns, wherein an optical pattern of the plurality of optical patterns encodes an object identifier. The steps include presenting the plurality of images comprising the plurality of optical patterns on a display, and presenting a plurality of visual indications overlying the plurality of optical patterns in the plurality of images. The steps also include identifying a selected optical pattern of the plurality of optical patterns based on a user action and a position of the selected optical pattern in one or more of the plurality of images. The steps also include decoding the selected optical pattern to generate the object identifier and storing the object identifier in a second memory device.

Device-side validation of screen recordings is disclosed, including: accessing a screen recording of a user's activities on a client device with respect to a task; performing, at the client device, video validation on the screen recording, including by identifying a characteristic marker associated with the task within the screen recording; and in response to the characteristic marker being identified, sending at least a portion of the screen recording to a compressed version of the screen recording to a server for further processing.

A processor of a remote facility executes image generation processing and display control processing. In the image generation processing, it is determined whether a front image includes an image of a mirror portion of a traffic mirror based on data of feature quantity of an object included in the front image. If it is determined that the front image includes the mirror portion image, an image of a preset region including the mirror portion is extracted from the front image. Then, a super-resolution image is generated by super-resolution processing for the image of this preset region. In the display control processing, if the front image includes the image of the mirror portion, the super-resolution image and the front image is displayed on a display of a remote facility.

A system monitors moveable objects using sensor data captured using one or more sensor mounted on a location of the moveable object. The system uses a machine learning based model to predict a risk score indicating a degree of risk associated with the moveable object. The system determines the action to be taken to mitigate the risk based on the risk score. The system transmits information describing the moveable object based on the sensor data to a remote monitoring system. The system may determine the amount of information transmitted, the rate at which information is transmitted, and the type of information displayed based on the risk score. The system performs dignity preserving transformations of the sensor data before transmitting or storing the data.

Systems, methods and techniques for automatically recognizing two or 3-dimensional real world objects with an augmented reality display device (smartphone or glasses etc.), and augmenting or enhancing the display of such real world objects by superimposing virtual images such as a still or video advertisement, an opportunity to buy, a story or other virtual image presentation. In non-limiting embodiments, the real world object includes visible features including visible security features and a recognition process takes the visible security features into account when recognizing the object and/or displaying superimposed virtual images.