A method of processing an immersive video according to the present disclosure includes performing pruning for an input image, generating an atlas based on patches generated by the pruning and generating a cropped atlas by removing a background region of the atlas.

There is provided a computer implemented method of automatic segmentation of three dimensional (3D) anatomical region of interest(s) (ROI) that includes predefined anatomical structure(s) of a target individual, comprising: receiving 3D images of a target individual, each including the predefined anatomical structure(s), each 3D image is based on a different respective imaging modality. In one implementation, each respective 3D image is inputted into a respective processing component of a multi-modal neural network, wherein each processing component independently computes a respective intermediate, and the intermediate outputs are inputted into a common last convolutional layer(s) for computing the indication of segmented 3D ROI(s). In another implementation, each respective 3D image is inputted into a respective encoding-contracting component a multi-modal neural network, wherein each encoding-contracting component independently computes a respective intermediate output. The intermediate outputs are inputted into a single common decoding-expanding component for computing the indication of segmented 3D ROI(s).

A predetermined image correction process is automatically performed on an image, and information for identifying that the predetermined image correction process has been performed is displayed in a state that an image having undergone the predetermined image correction process is being displayed.

Use of improved image acquisition for a medical dose preparation system. The medical dose preparation system may include a work station for capturing medical dose preparation images (e.g., to document preparation of a mediation dose). The medical dose preparation image may be captured by a video data stream processor capable of performing an auto cropping technique on a video data stream received from an image device. Accordingly, memory resources may be more efficiently employed while maintaining high quality medical dose preparation images.

The training apparatus (2000) performs a first phase training and a second phase training of a discriminator (10). The discriminator (10) acquires a ground-view image and an aerial-view image, and determines whether the acquired ground-view image matches the acquired aerial-view image. The first phase training is performed using a ground-view image and a first level negative example of aerial-view image. The first level negative example of aerial-view image includes scenery of a different type from scenery in the ground-view image. The second phase training is performed using the ground-view image and a second level negative example of aerial-view image. The second level negative example of aerial-view image includes scenery of a same type as scenery in the ground-view image.

An artificial intelligence apparatus for calibrating an output position of a display panel according to an embodiment includes a camera configured to capture an image displayed by the display panel; and a processor configured to: transmit a signal for outputting a position reference image to the display panel, receive, via the camera, a captured image for the display panel, calculate an output position offset for the display panel in a predetermined unit based on the position reference image and the captured image, determine an output position calibration value for the display panel using the calculated output position offset, and transmit the determined output position calibration value to the display panel.

A method and apparatus for aiding a police officer in writing a report by creating a depiction of an incident scene is provided herein. During operation a drone will photograph an incident scene from above. Relevant objects will be identified and a depiction of the incident scene will be created by overlaying the relevant photographed real world objects onto a map retrieved from storage. The depiction of the incident scene will be made available to police officers to increase the officers' efficiency in report writing. More particularly, officers will no longer need to re-create the depiction of the incident scene by hand. Instead, the officer will be able to use the depiction of the incident scene.

An apparatus for learning an image of a vehicle camera and a method thereof are provided to apply a result of deep learning to all vehicles regardless of the color of a vehicle and the mounting angle (e.g., yaw, roll and pitch) of a camera. The apparatus includes an image input device that inputs an image photographed by a camera mounted on a vehicle, and a controller that masks a fixed area in the image input from the image input device with a pattern image, converts the masked image into a plurality of images having different views, and performs deep learning by using the masked image and the converted plurality of images.

A sample of blood is placed on a bilirubin test strip and plasma separated from the red blood cells. The bilirubin test strip is located on a test card along with a set of calibration images, the colors of the calibration images being associated with known plasma bilirubin levels. A photograph is taken of the test card. The bilirubin level of the blood sample is determined by, within the photograph, interpolating the color of the plasma and the colors of the closest colored calibration images.

Disclosed are systems and methods for improving interactions with and between computers in content searching, hosting and/or providing systems supported by or configured with devices, servers and/or platforms. The disclosed systems and methods provide an image processing framework that sub-divides computer vision techniques into three computationally efficient steps: detection, classification and matching. These steps provide an improved image processing framework that can analyze live stream data of a media file, in real-time, in order to identify and track specific digital objects depicted therein. This enables not only image processing detection results, but also the capabilities of augmenting the video stream with additional data related to the detected object.