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.

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.

In an example, a digital image comprising a representation of multiple physical objects is received at a client computer. The digital image is copied into a temporary canvas. The digital image is then analyzed to identify a plurality of potential object areas, each of the potential object areas having pixels with colors similar to the other pixels within the potential object area. A minimum bounding region for each of the identified potential object areas is identified, the minimum bounding region being a smallest region of a particular shape that bounds the corresponding potential object area. The pixels within a selected minimum bounding region are cropped from the digital image. The pixels within the selected minimum bounding region are then sent to an object recognition service on a server to identify an object represented by the pixels within the selected minimum bounding region.

In an example, a digital image comprising a representation of multiple physical objects is received at a client computer. The digital image is copied into a temporary canvas. The digital image is then analyzed to identify a plurality of potential object areas, each of the potential object areas having pixels with colors similar to the other pixels within the potential object area. A minimum bounding region for each of the identified potential object areas is identified, the minimum bounding region being a smallest region of a particular shape that bounds the corresponding potential object area. The pixels within a selected minimum bounding region are cropped from the digital image. The pixels within the selected minimum bounding region are then sent to an object recognition service on a server to identify an object represented by the pixels within the selected minimum bounding region.

A throwing position acquisition method and apparatus, a computer device, and a storage medium. The method includes: acquiring image frames of a target video; acquiring a projectile position in each image frame; acquiring a trajectory starting point position of the target object based on the projectile position in each image frame; acquiring, based on projectile positions corresponding to at least one group of image frames in the image frames, a first height value corresponding to a case that the target object is thrown; and acquiring a throwing position of the target object based on the first height value and the trajectory starting point position of the target object.

A method for detecting a display panel defect, including: collecting a panel image of a to-be-detected display panel, a plurality of first pixels of the display panel corresponding to a plurality of second pixels in the panel image; converting the panel image into a binary image; dilating each bright spot region in the binary image such that adjacent bright spot regions communicate with each other to form at least one closed communication region in the binary image; determining a region of interest mask image in the binary image in accordance with the at least one closed communication region; determining a region of interest in accordance with the region of interest mask image and the panel image; and performing feature identification on the region of interest to determine a defect of the display panel.

A novel disparity computation technique is presented which comprises multiple orthogonal disparity maps, generated from approximately orthogonal decomposition feature spaces, collaboratively generating a composite disparity map. Using an approximately orthogonal feature set extracted from such feature spaces produces an approximately orthogonal set of disparity maps that can be composited together to produce a final disparity map. Various methods for dimensioning scenes and are presented. One approach extracts the top and bottom vertices of a cuboid, along with the set of lines, whose intersections define such points. It then defines a unique box from these two intersections as well as the associated lines. Orthographic projection is then attempted, to recenter the box perspective. This is followed by the extraction of the three-dimensional information that is associated with the box, and finally, the dimensions of the box are computed. The same concepts can apply to hallways, rooms, and any other object.

A novel disparity computation technique is presented which comprises multiple orthogonal disparity maps, generated from approximately orthogonal decomposition feature spaces, collaboratively generating a composite disparity map. Using an approximately orthogonal feature set extracted from such feature spaces produces an approximately orthogonal set of disparity maps that can be composited together to produce a final disparity map. Various methods for dimensioning scenes and are presented. One approach extracts the top and bottom vertices of a cuboid, along with the set of lines, whose intersections define such points. It then defines a unique box from these two intersections as well as the associated lines. Orthographic projection is then attempted, to recenter the box perspective. This is followed by the extraction of the three-dimensional information that is associated with the box, and finally, the dimensions of the box are computed. The same concepts can apply to hallways, rooms, and any other object.