Train a machine learning model, using an image-based knowledge graph of tropical cyclone data, for implementing a surface field modeling architecture that produces images of at least surface wind fields and surface rainfall fields from images of at least tropical cyclone tracks and pressure intensities. Generate model images of a modeled surface wind field and a modeled surface rainfall field by providing images of at least a user-generated tropical cyclone track and pressure intensity to the trained machine learning model.

A data storage device and method for efficient image searching are provided. In one embodiment, a data storage device is provided comprising a memory and a controller. The controller is configured to store a plurality of images and a plurality of keys in the memory, wherein each key of the plurality of keys is generated from a respective image of the plurality of images; receive, from a host, a key generated from a target image desired by the host; and return, to the host, an image from the stored plurality of images that is associated with a key that matches the key received from the host. Other embodiments are provided.

The disclosure herein relates to systems, methods, and devices for medical image analysis, diagnosis, risk stratification, decision making and/or disease tracking. In some embodiments, the systems, devices, and methods described herein are configured to analyze non-invasive medical images of a subject to automatically and/or dynamically identify one or more features, such as plaque and vessels, and/or derive one or more quantified plaque parameters, such as radiodensity, radiodensity composition, volume, radiodensity heterogeneity, geometry, location, perform computational fluid dynamics analysis, facilitate assessment of risk of heart disease and coronary artery disease, enhance drug development, determine a CAD risk factor goal, provide atherosclerosis and vascular morphology characterization, and determine indication of myocardial risk, and/or the like. In some embodiments, the systems, devices, and methods described herein are further configured to generate one or more assessments of plaque-based diseases from raw medical images using one or more of the identified features and/or quantified parameters.

In one embodiment, a method includes receiving sensor data of an environment of the vehicle generated by one or more sensors of the vehicle, the sensors comprising a camera, identifying, based on the sensor data, one or more objects in a field of view of the camera and one or more object types that correspond to the one or more objects, determining one or more target histograms that correspond to the object types, generating a processed image based on an image captured by the camera, wherein the processed image has a histogram based on the target histograms, and using the processed image to determine state information associated with the objects. The processed image may be generated by processing the image captured by the camera using a histogram matching algorithm to generate the histogram of the processed image based on the target histograms.

The present disclosure provides a method and apparatus for detecting a region of interest in a video, a device and a storage medium. The method may include: acquiring a current to-be-processed frame from a picture frame sequence of a video; detecting a region of interest (ROI) in the current to-be-processed frame, in response to determining that the current to-be-processed frame is a detection picture frame, to determine at least one ROI in the current to-be-processed frame; and updating a to-be-tracked ROI, based on the ROI in the current to-be-processed frame and a tracking result determined by a pre-order tracking picture frame; and tracking the current to-be-processed frame based on the existing to-be-tracked ROI, in response to determining that the current to-be-processed frame is a tracking picture frame, to determine at least one tracking result as the ROI of the current to-be-processed frame.

A liveness determining method and apparatus are provided. The liveness determining apparatus includes an optical sensor including at least one optical source; a memory configured to store registered color information; and at least one processor configured to: obtain, from the optical sensor, an input fingerprint image of an object corresponding to the at least one optical source, obtain input color information from the input fingerprint image, compare the input color information and the registered color information, and determine liveness of the object based on a result of the comparing.

An image processor and a method therein to provide a target image for evaluation with an object detector. The method comprises: obtaining a source image captured by a camera and depicting an object, and applying an inverse pixel transform to each target pixel of a target image to determine one or more source pixels located at a position in the source image corresponding to a position of each target pixel in the target image. The method further comprises assigning, to each target pixel, a target pixel value determined based on one or more source pixel values of the determined one or more source pixels located at the corresponding position, thereby is a size c in the target image of the depicted object of a specific object type normalized in at least one size dimension. Thereafter, the target image is fed to an object detector for evaluation.

A data collection apparatus comprises a camera configured to obtain first and second recordings of at least a portion of a medicament delivery device, said portion including a first component and a second component, the first component being configured to move relative to the second component as medicament is expelled from the medicament delivery device. The data collection apparatus also comprises a processing arrangement configured to determine, from said first and second recordings, positions in the first and second recordings of the first component relative to the second component, and determine an amount of medicament that has been expelled by the medicament delivery device by comparing the position, in the first recording, of the first component relative to the second component with a position, in the second recording, of the first component relative to the second component.

A deep learning based compression (DLBC) system trains multiple models that, when deployed, generates a compressed binary encoding of an input image that achieves a reconstruction quality and a target compression ratio. The applied models effectively identifies structures of an input image, quantizes the input image to a target bit precision, and compresses the binary code of the input image via adaptive arithmetic coding to a target codelength. During training, the DLBC system reconstructs the input image from the compressed binary encoding and determines the loss in quality from the encoding process. Thus, the models can be continually trained to, when applied to an input image, minimize the loss in reconstruction quality that arises due to the encoding process while also achieving the target compression ratio.

A recording medium records a program that causes a computer to execute processing of: obtaining pieces of three-dimensional point data observed a mobile body; calculating a feature amount according to a distance between a position of the mobile body and each of three-dimensional points indicated by the pieces of three-dimensional point data; and specifying a reference point that corresponds to the position of the mobile body from among reference points in a mobile space of the mobile body which are registered in a database that registers a combinations each of the respective reference points and the feature amounts according to the distance between the position of the respective reference points and each of three-dimensional points indicated by a pieces of three-dimensional point data observed from the position of the respective reference points, based on the calculated feature amount and the database.