A method may comprise receiving, via a processor, a first indication that an object is in a first zone of interest of a first sensor in the plurality of sensors; receiving, via the processor, a second indication that the object is in a second zone of interest of a second sensor in the plurality of sensors; and determining, via the processor, whether the first sensor or the second sensor is falsely detecting the object within the respective zone of interest.

An object tracking method and system in multiple non-linear distortion lenses are provided. A deep learning method is used for training an object identification model, an object comparison model, and a coordinate mapping model. The object identification model and the object comparison model are used for identifying and comparing objects with non-linear distortion respectively in order to find a plurality of groups of corresponding object information for the same objects in visions of multiple image capturing apparatuses. The coordinate mapping model is used for verifying the plurality of groups of corresponding object information, and finding all position mappings in visions of multiple image capturing apparatuses via the verified plurality of groups of corresponding object information.

A method of detecting clouds in an acquired aerial image includes determining a region of a reference aerial image corresponding to a region of an acquired aerial image. For each of a plurality of locations over the region of the acquired aerial image and corresponding to a plurality of locations over the region of the reference aerial image, the mutual information of one or more variables associated with the location in the acquired aerial image and one or more variables associated with the corresponding location in the reference aerial image is calculated. Using the mutual information calculated for each of the plurality of locations over the region of the acquired aerial image, it is determined when the acquired aerial image displays a cloud at the location in the region of the acquired aerial image.

Various methods for reducing artifacts in OCT images of an eye are described. In one exemplary method, three dimensional OCT image data of the eye is collected. Motion contrast information is calculated in the OCT image data. A first image and a second image are created from the motion contrast information. The first and the second images depict vasculature information regarding one or more upper portions and one or more deeper portions, respectively. The second image contains artifacts. Using an inverse calculation, a third image is determined that can be mixed with the first image to generate the second image. The third image depicts vasculature regarding the same one or more deeper portions as the second image but has reduced artifacts. A depth dependent correction method is also described that can be used in combination with the inverse problem based method to further reduce artifacts in OCT angiography images.

A system and various methods for processing an image to produce a hierarchical image representation model, segment the image model using shape criteria to produce positive and negative training data sets as well as a search-space data set comprising shapes matched to a search query provided as input, and using the training data sets to train a machine learning model to improve recognition of shapes that are similar to an input query without being exact matches, to improve object recognition.

The present disclosure is directed to a method and device for managing medical data. The method may include receiving medical image data of a plurality of patient cases acquired by at least one image acquisition device. The method may further include determining diagnosis results, by a processor, of the medical image data using an artificial intelligence method. The method may also include determining, by the processor, priority scores for the medical image data based on the respective diagnosis results, and sorting, by the processor, the medical image data based on the priority score. The method may yet further include presenting a queue of the medical image data on a display according to the sorted order.

Described is a system for controlling a device based on streaming data analysis using blind source separation. The system updates a set of parallel processing pipelines for two-dimensional (2D) tensor slices of streaming tensor data in different orientations, where the streaming tensor data includes incomplete sensor data. In updating the parallel processing pipelines, the system replaces a first tensor slice with a new tensor slice resulting in an updated set of tensor slices in different orientations. At each time step, a cycle of demixing, transitive matching, and tensor factor weight calculations is performed on the updated set of tensor slices. The tensor factor weight calculations are used for sensor data reconstruction, and based on the sensor data reconstruction, hidden sensor data is extracted. Upon recognition of an object in the extracted hidden sensor data, the device is caused to perform a maneuver to avoid a collision with the object.

A method of extracting an image of a port wharf through multispectral interpretation includes: first, extracting a blurred coastline by assigning values to grayscale values; then, performing a smoothing and noise removal processing on a remote sensing image in a targeted area to extract edge information; sequentially, establishing a multispectral database of a targeted port wharf; and extracting a port wharf using a projected eigenvector, performing an MAF transformation on the regularized kernel function again, projecting multivariate observed values to original eigenvectors, and identifying a remote sensing image area corresponding to the original eigenvector smaller than a transformation variance as a port wharf to be extracted, and then carrying out a validation operation.

A method of extracting an image of a port wharf through multispectral interpretation includes: first, extracting a blurred coastline by assigning values to grayscale values; then, performing a smoothing and noise removal processing on a remote sensing image in a targeted area to extract edge information; sequentially, establishing a multi spectral database of a targeted port wharf; and extracting a port wharf using a projected eigenvector, performing an MAF transformation on the regularized kernel function again, projecting multivariate observed values to original eigenvectors, and identifying a remote sensing image area corresponding to the original eigenvector smaller than a transformation variance as a port wharf to be extracted, and then carrying out a validation operation.

An object tracking method and system in multiple non-linear distortion lenses are provided. A deep learning method is used for training an object identification model, an object comparison model, and a coordinate mapping model. The object identification model and the object comparison model are used for identifying and comparing objects with non-linear distortion respectively in order to find a plurality of groups of corresponding object information for the same objects in visions of multiple image capturing apparatuses. The coordinate mapping model is used for verifying the plurality of groups of corresponding object information, and finding all position mappings in visions of multiple image capturing apparatuses via the verified plurality of groups of corresponding object information.