Abstract: Method and system for grading a medical image. For example, a system for grading a medical image comprising a grading network configured to provide a grading result corresponding to the medical image based on at least the medical image and/or a list of lesion candidates generated by a lesion identification network.

Abstract: An example device includes a memory to store the video data, and processing circuitry in communication with the memory. The processing circuitry is configured to compare a value of a dimension of a current block of the stored video data to a value of a corresponding dimension of a neighboring block of the current block to obtain a relative dimension value. The processing circuitry is further configured to determine, based on the relative dimension value, that the current block is to be partitioned according to a prediction tree (PT) portion of a multi-type tree-based partitioning scheme. The PT portion comprises partitioning according to one of a binary tree structure or a center-side triple tree structure. The processing circuitry is further configured to partition, based on the determination, the current block according to the PT portion, to form a plurality of sub-blocks.

Abstract: A package includes a sensor die, and an encapsulating material encapsulating the sensor die therein. A top surface of the encapsulating material is substantially coplanar with or higher than a top surface of the sensor die. A plurality of sensing electrodes is higher than the sensor die and the encapsulating material. The plurality of sensing electrodes is arranged as a plurality of rows and columns, and the plurality of sensing electrodes is electrically coupled to the sensor die. A dielectric layer covers the plurality of sensing electrodes.

Abstract: Techniques related to indirect sparse simultaneous localization and mapping (SLAM) are discussed. Such techniques include adaptively positioning a virtual camera relative to an estimated position of a physical camera within an environment to be mapped, projecting a depth error to an image plane corresponding to the adaptive camera position, and using the projected depth error to update a mapping of the environment.

Abstract: A method of video decoding performed in a video decoder includes receiving compressed geometric data corresponding to a three dimensional (3D) space. The method further includes generating a resolution occupancy map. The method further includes reconstructing a point cloud using the compressed geometric data and the resolution occupancy map. The method further includes performing a smoothing operation on one of the (i) reconstructed point cloud and (ii) the generated resolution occupancy map.

Abstract: A method for optimizing image capture of a scene by a swarm of drones including a root drone and first and second level-1 drones involves the root drone following a predetermined trajectory over the scene, capturing one or more root keyframe images, at a corresponding one or more root drone orientations and root drone-to-scene distances. For each root keyframe image: the root drone generates a ground mask image for that root keyframe image, and applies that ground mask image to the root keyframe image to generate a target image. The root drone then analyzes the target image to generate first and second scanning tasks for the first and second level-1 drones to capture a plurality of images of the scene at a level-1 drone-to-scene distance smaller than the root drone-to-scene distance; and the first and second level-1 drones carry out the first and second scanning tasks respectively.

Abstract: A method for modifying a trailer detection routine for a vehicle trailer detection system includes identifying a trailer in image data via a trailer detection model and activating a detection training routine for the trailer. The method further includes capturing the image data of the trailer from a plurality of perspectives relative to a trailer heading of the trailer and controlling an update procedure configured to generate a modified trailer detection model operable to detect the trailer.

Abstract: A positioning method includes acquiring an image of an area where a target object is located at a first time instant and multiple frames of point cloud data of an area where the target object is located at multiple time instants, wherein the multiple frames of point cloud data include first point cloud data of the area where the target object is located at the first time instant. The method also includes determining a point cloud map according to the multiple frames of point cloud data and acquiring a target feature vector according to the first point cloud data, the point cloud map and the image. The method further includes determining a positioning result of the target object according to the target feature vector.

Abstract: A system and method for image recognition registration of an athlete into a sporting event. The athlete is registered in the sporting event using image recognition technology. A digital commencement image of the athlete taken by a camera (106) as the athlete crosses a starting line. The digital commencement image is compared with a stored profile image of the athlete to identify the athlete and enter them into the event without the need for the athlete to pre-register for the particular event. Enhanced recognition techniques incorporating pattern recognition may be used to increase identity accuracy.

Abstract: A mapping system receives sensor data from an unmanned aerial vehicle. The mapping system further receives images from a camera of the unmanned aerial vehicle. The mapping system determines an altitude of the camera based on the sensor data. The mapping system calculates a footprint of the camera based on the altitude of the camera and a field of view of the camera. The mapping system constructs a localized map based on the images and the footprint of the camera.

Abstract: Systems and methods for digitized document image data spillage recovery are provided. One or more memories may be coupled to one or more processors, the one or more memories including instructions operable to be executed by the one or more processors. The one or more processors may be configured to capture an image; process the image through at least a first pass to generate a first contour; remove a preprinted bounding region of the first contour to retain text; generate one or more pixel blobs by applying one or more filters to smudge the text; identify the one or more pixel blobs that straddle one or more boundaries of the first contour; resize the first contour to enclose spillage of the one or more pixel blobs; overlay the text from the image within the resized contour; and apply pixel masking to the resized contour.

Abstract: A computer-implemented method for tracking multiple targets includes identifying a plurality of targets based on a plurality of images obtained from an imaging device carried by an unmanned aerial vehicle (UAV) via a carrier, determining a target group comprising one or more targets from the plurality of targets, and controlling at least one of the UAV or the carrier to track the target group.

Abstract: Methods and apparatus related to generating a model for an object encountered by a robot in its environment, where the object is one that the robot is unable to recognize utilizing existing models associated with the robot. The model is generated based on vision sensor data that captures the object from multiple vantages and that is captured by a vision sensor associated with the robot, such as a vision sensor coupled to the robot. The model may be provided for use by the robot in detecting the object and/or for use in estimating the pose of the object.

Abstract: Aspects of the present disclosure include methods, systems, and non-transitory computer readable media that perform the steps of capturing capture one or more images comprising a plurality of visual features of an inspected device, transmitting the one or more images to a server comprising a neural network, wherein the neural network analyzes the plurality of visual features to identify the inspected device and the server identifies the checklist comprising tasks or questions associated with an inspection process of the inspected device, receiving the checklist associated from the server, and displaying the checklist.

Abstract: The present invention relates to the field of medical monitoring, and in particular non-contact, video-based monitoring of pulse rate, respiration rate, motion, and oxygen saturation. Systems and methods are described for capturing images of a patient, producing intensity signals from the images, filtering those signals to focus on a physiologic component, and measuring a vital sign from the filtered signals.

Abstract: A client device assists in identifying user consumption of information. The client device comprises a hardware processor; a screen; memory storing computer instructions that when executed perform capturing a series of screen image snapshots being presented on the screen; reducing resolution of each screen image snapshot in the series of screen image snapshots; capturing metadata associated with each screen image snapshot in the series of screen image snapshots, the metadata at least including a timestamp; identifying a duplicate in the series of screen image snapshots; discarding the duplicate from the series of screen image snapshots; and uploading the series of captured screen image snapshots to a processing server for processing.

Abstract: Provided are a training apparatus, a recognition apparatus, a training method, a recognition method, and a program that can accurately recognize what an object represented in an image associated with depth information is. An object data acquiring section acquires three-dimensional data representing an object. A training data generating section generates a plurality of training data each representing a mutually different part of the object on the basis of the three-dimensional data. A training section trains a machine learning model using the generated training data as the training data for the object.

Abstract: Described is a method that involves operating an unmanned aerial vehicle (UAV) to begin a flight, where the UAV relies on a navigation system to navigate to a destination. During the flight, the method involves operating a camera to capture images of the UAV's environment, and analyzing the images to detect features in the environment. The method also involves establishing a correlation between features detected in different images, and using location information from the navigation system to localize a feature detected in different images. Further, the method involves generating a flight log that includes the localized feature. Also, the method involves detecting a failure involving the navigation system, and responsively operating the camera to capture a post-failure image. The method also involves identifying one or more features in the post-failure image, and determining a location of the UAV based on a relationship between an identified feature and a localized feature.

Abstract: A system and method for generating recommendations during image capture are provided. The method comprises obtaining a camera live stream from a camera in a user device, the camera live stream including image data of a particular product; determining one or more baseline image features common to images of one or more products of a collection based at least on image analysis of image data of the images of the one or more products of the collection; comparing the one or more baseline image features to one or more image features of the image data of the particular product to generate one or more recommendations; and generating, for display together with the camera live stream on the user device, the one or more recommendations.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer-storage media, for navigation using planar landmarks. In some implementations, images depicting an area of a property captured by a camera are obtained. Landmarks in the area are identified based on the images. An expected stability for each of the landmarks are determined. A map for the area is generated based on the expected stability for each of the landmarks. The map is transmitted to one or more electronic devices.