Various implementations disclosed herein provide feedback to a user during object scanning based on how well sensor data of the scanned object has been captured. During object scanning a user may move an electronic device with sensors (e.g., cameras, depth sensors, etc.) around an object to capture sensor data for use in generating a final 3D model of the object. Live feedback during the scanning process is enabled by assessing how well the captured sensor data represents different portions of the object. In some implementations, a 3D model is generated, updated, and assessed based on the sensor data live during the scanning process. This live 3D model may be coarser (i.e., having fewer details) than the final 3D model.

An information processing apparatus includes an identification unit configured to identify a partial region of an input image, and a processing unit configured to perform image processing for reducing degradation of the input image on the input image by inference using a neural network. The processing unit is configured to change the image processing between the partial region and another region.

A system is configured to perform operations includes accessing a set of model points of a model of an anatomic structure of a patient, the model points being associated with a model space. A set of measured points of the anatomic structure of the patient are collected, the measured points being associated with a patient space. The set of model points are registered to the set of measured points using a first set of initial parameters to generate a first transformation. One or more sets of perturbed initial parameters are generated based on the first set of initial parameters. One or more perturbed registration processes are performed to register the set of model points to the set of measured points using the one or more sets of perturbed initial parameters respectively to generate corresponding perturbed transformations. A registration quality indicator is generated based on the first transformation and the one or more perturbed transformations.

A computer-implemented method comprising: receiving a 3D image including an object depicted in the image, the 3D image comprising an ordered set of 2D images; determining a contour around the object in a first of said 2D images; and determining a contour around the object in a second of said 2D images, the second 2D image being non-contiguous with the first in said ordered set, having an intermediate region comprising one or more intermediate ones of said 2D images between the first and second 2D images within said ordered set. In each of the first and second 2D images, inside of the contour is classified as foreground and outside of the contour is classified as background. The method further comprises performing a 3D geodesic distance computation to classify points in the intermediate region as foreground of background.

An information processing device that determines a monitoring target range of a predetermined place includes an acquisition unit configured to acquire information on a line drawn by a user with respect to image data in which the predetermined place is imaged, and a determination unit configured to determine a range indicated by an area on the line as the monitoring target range, a thickness of the line being increased.

A method and system utilizes an imaging device that generates images of target tissue of a patient during a surgical procedure that acts on the target tissue imaged by the imaging device. The method and system enables visual detection of patient movement during the surgical procedure by marking at least one spatial attribute of one or more identifiable features of the target tissue illustrated in an image presented in a display window. Prior to acting on the target tissue, a visual indicator of the spatial attribute(s) is superimposed on one or more subsequent images captured by the imaging device and displayed to the operator. The operator can visually compare a position of the visual indicator to a position of the operator-identified feature in order to detect movement of the patient during the procedure. The system and methodology also facilitates realignment that corrects for detected patient movement.

A system may be configured to facilitate a medical procedure. Some embodiments may: acquire a scan corresponding to a region of interest (ROI); capture an image of a patient in real-time; identify, via a trained machine learning (ML) model using the acquired scan and the captured image, a Kambin's triangle; and overlay, on the captured image, a representation of the identified Kambin's triangle.

Systems, methods, and apparatus for identifying, tracking, and disrupting UAVs are described herein. Sensor data can be received from one or more portable countermeasure devices or sensors. The sensor data can relate to an object detected proximate to a particular airspace. The system can analyze the sensor data relating to the object to determine a location of the object and determine that the object is flying within the particular airspace based at least in part on location data. A portable countermeasure device can be identified that corresponds to the location of the object. The system can transmit information about the object to the identified portable countermeasure device. The portable countermeasure device can transmit additional data relating to the object to the system.

A method (500) for 3D modeling of one or more regions of interest. The method includes obtaining (s502) information indicating that a person's gaze is fixed. Advantageously, the method further includes, in response to obtaining the information indicating that the person's gaze is fixed, initiating (s504) an active scan of a region of interest (ROI), wherein the ROI is a region in which the person's gaze is directed.

A medical image processing apparatus according to an embodiment includes processing circuitry. The processing circuitry is configured to obtain a medical image. The processing circuitry is configured to calculate a first blood flow direction on the basis of a structure of a region of interest rendered in the medical image. The processing circuitry is configured to calculate a second blood flow direction on the basis of a structure in the surroundings of the region of interest. The processing circuitry is configured to identify a condition of the region of interest, on the basis of the first blood flow direction and the second blood flow direction.