System and method for detection of pre-drowning and drowning events based on underwater images are disclosed.

System and method for detection of pre-drowning and drowning events based on underwater images are disclosed.

A method for detecting unmanned aerial vehicles (UAV) includes detecting an unknown flying object in a monitored zone of air space. An image of the detected unknown flying object is captured. The captured image is analyzed to classify the detected unknown flying object. A determination is made, based on the analyzed image, whether the detected unknown flying object comprises a UAV.

A method for detecting unmanned aerial vehicles (UAV) includes detecting an unknown flying object in a monitored zone of air space. An image of the detected unknown flying object is captured. The captured image is analyzed to classify the detected unknown flying object. A determination is made, based on the analyzed image, whether the detected unknown flying object comprises a UAV.

Systems and methods for construction zone segmentation are provided. The system aligns image level features between a source domain and a target domain based on an adversarial learning process while training a domain discriminator. The target domain includes construction zones scenes having various objects. The system selects, using the domain discriminator, unlabeled samples from the target domain that are far away from existing annotated samples from the target domain. The system selects, based on a prediction score of each of the unlabeled samples, samples with lower prediction scores. The system annotates the samples with the lower prediction scores.

Systems and methods for construction zone segmentation are provided. The system aligns image level features between a source domain and a target domain based on an adversarial learning process while training a domain discriminator. The target domain includes construction zones scenes having various objects. The system selects, using the domain discriminator, unlabeled samples from the target domain that are far away from existing annotated samples from the target domain. The system selects, based on a prediction score of each of the unlabeled samples, samples with lower prediction scores. The system annotates the samples with the lower prediction scores.

The present disclosure relates to a method for scanning a patient in an imaging system. The imaging system may include one or more cameras directed at the patient. The method may include obtaining a position of each of the camera(s) relative to the imaging system. The method may also include obtain image data of the patient captured by the camera(s), wherein the image data may correspond to a first view with respect to the patient. The method may further include generating projection image data of the patient based on the image data and the position of each of the camera(s) relative to the imaging system, wherein the projection image data may correspond to a second view with respect to the patient different from the first view. The method may further include generating control information for scanning the patient based on the projection image data of the patient.

The present disclosure relates to a method for scanning a patient in an imaging system. The imaging system may include one or more cameras directed at the patient. The method may include obtaining a position of each of the camera(s) relative to the imaging system. The method may also include obtain image data of the patient captured by the camera(s), wherein the image data may correspond to a first view with respect to the patient. The method may further include generating projection image data of the patient based on the image data and the position of each of the camera(s) relative to the imaging system, wherein the projection image data may correspond to a second view with respect to the patient different from the first view. The method may further include generating control information for scanning the patient based on the projection image data of the patient.

A scalable tracking system processes video of a space to track the positions of people within a space. The tracking system determines local coordinates for the people within frames of the video and then assigns these coordinates to time windows based on when the frames were received. The tracking system then combines or clusters certain local coordinates that have been assigned to the same time window to determine a combined coordinate for a person during that time window.

The invention relates to a computer-implemented method for automatically detecting anatomical structures (3) in a medical image (1) of a subject, the method comprising applying an object detector function (4) to the medical image, wherein the object detector function performs the steps of: (A) applying a first neural network (40) to the medical image, wherein the first neural network is trained to detect a first plurality of classes of larger-sized anatomical structures (3a), thereby generating as output the coordinates of at least one first bounding box (51) and the confidence score of it containing a larger-sized anatomical structure; (B) cropping (42) the medical image to the first bounding box, thereby generating a cropped image (11) containing the image content within the first bounding box (51); and (C) applying a second neural network (44) to the cropped medical image, wherein the second neural network is trained to detect at least one second class of smaller-sized anatomical structures (3b), thereby generating as output the coordinates of at least one second bounding box (54) and the confidence score of it containing a smaller-sized anatomical structure.