Provided is an image processing apparatus configured to process an image of a fundus of an eye to accurately measure thicknesses of membranes that form a blood vessel wall of an eye. The image processing apparatus includes: an image acquiring unit configured to acquire an image of an eye; a vessel feature acquiring unit configured to acquire membrane candidate points that form an arbitrary wall of a blood vessel based on the acquired image; a cell identifying unit configured to identify a cell that forms the wall of the blood vessel based on the membrane candidate points; and a measuring position acquiring unit configured to identify a measuring position regarding the wall of the blood vessel based on a position of the identified cell.

A user may select or interact with objects in a scene using gaze tracking and movement tracking. In some examples, the scene may comprise a virtual reality scene or a mixed reality scene. A user may move an input object in an environment and be facing in a direction towards the movement of the input object. A computing device may use sensors to obtain movement data corresponding to the movement of the input object, and gaze tracking data including to a location of eyes of the user. One or more modules of the computing device may use the movement data and gaze tracking data to determine a three-dimensional selection space in the scene. In some examples, objects included in the three-dimensional selection space may be selected or otherwise interacted with.

An image segmentation method is disclosed that allows a user to select image component types, for example tissue types and or background, and have the method of the present invention segment the image according to the user's input utilizing the superpixel image feature data and spatial relationships.

An image segmentation method is disclosed that allows a user to select image component types, for example tissue types and or background, and have the method of the present invention segment the image according to the user's input utilizing the superpixel image feature data and spatial relationships.

Provided is an image processing apparatus configured to process an image of a fundus of an eye, which is capable of simply and accurately measuring a distribution of cells that form a blood vessel wall of an eye. The image processing apparatus includes: an image acquiring unit configured to acquire an image of an eye; a vessel feature acquiring unit configured to acquire membrane candidate points that form an arbitrary wall of a blood vessel based on the acquired image; and a cell identifying unit configured to identify a cell that forms the wall of the blood vessel based on the membrane candidate points.

The application discloses a method and system for segmenting a lung image. The method may include obtaining a target image relating to a lung region. The target image may include a plurality of image slices. The method may also include segmenting the lung region from the target image, identifying an airway structure relating to the lung region, and identifying one or more fissures in the lung region. The method may further include determining one or more pulmonary lobes in the lung region.

The application discloses a method and system for segmenting a lung image. The method may include obtaining a target image relating to a lung region. The target image may include a plurality of image slices. The method may also include segmenting the lung region from the target image, identifying an airway structure relating to the lung region, and identifying one or more fissures in the lung region. The method may further include determining one or more pulmonary lobes in the lung region.

Various examples of a system for peanut sorting and grading are disclosed herein. The system for grading peanut maturity, can include: a sample feeder configured to supply individual peanuts to an imaging area; a sorting board comprising a plurality of chutes and a plurality of gates, each chute of the plurality of chutes designated for a grade of peanut; and program instructions to obtain the digital image of the individual peanut; determine the grade of the individual peanut; and sort the individual peanut based on the grade of the individual peanut. A method for grading peanut maturity, can include feeding an individual peanut to an imaging area; obtaining a digital image of the individual peanut; determining a grade of the individual peanut based on an average color; and sorting the individual peanut in a chute of a sorting board based on the grade of the individual peanut.

A method for computing dimensions of an object in a scene includes: controlling, by a processor, a depth camera system to capture at least a frame of the scene, the frame including a color image and a depth image arranged in a plurality of pixels; detecting, by the processor, an object in the frame; determining, by the processor, a ground plane in the frame, the object resting on the ground plane; computing, by the processor, a rectangular outline bounding a projection of a plurality of pixels of the object onto the ground plane; computing, by the processor, a height of the object above the ground plane; and outputting, by the processor, computed dimensions of the object in accordance with a length and a width of the rectangular outline and the height.

A method for computing dimensions of an object in a scene includes: controlling, by a processor, a depth camera system to capture at least a frame of the scene, the frame including a color image and a depth image arranged in a plurality of pixels; detecting, by the processor, an object in the frame; determining, by the processor, a ground plane in the frame, the object resting on the ground plane; computing, by the processor, a rectangular outline bounding a projection of a plurality of pixels of the object onto the ground plane; computing, by the processor, a height of the object above the ground plane; and outputting, by the processor, computed dimensions of the object in accordance with a length and a width of the rectangular outline and the height.