A first 2D recording of a specified reference view of an object is captured by a camera and, starting from the first 2D recording, a user's starting location relative to the object is ascertained by a computer vision module. Starting from the origin of a coordinate system as the starting location of the camera, one or more specified and/or settable relative positions in the vicinity of the object and/or in the object are determined as one or more locations for the respective perspective of the camera for taking at least one second 2D recording. The respective location in an object view on a display of the camera is displayed by a respective first augmented reality marker on the ground and/or on the object. The alignment of the camera with regard to angle and rotation with the perspective corresponding to the respective location is performed in this case by second augmented reality markers as auxiliary elements.

A processor-implemented method of detecting a moving object includes: estimating a depth image of a current frame; determining an occlusion image of the current frame by calculating a depth difference value between the estimated depth image of the current frame and an estimated depth image of a previous frame; determining an occlusion accumulation image of the current frame by adding a depth difference value of the occlusion image of the current frame to a depth difference accumulation value of an occlusion accumulation image of the previous frame; and outputting an area of a moving object based on the occlusion accumulation image.

A processor-implemented method of detecting a moving object includes: estimating a depth image of a current frame; determining an occlusion image of the current frame by calculating a depth difference value between the estimated depth image of the current frame and an estimated depth image of a previous frame; determining an occlusion accumulation image of the current frame by adding a depth difference value of the occlusion image of the current frame to a depth difference accumulation value of an occlusion accumulation image of the previous frame; and outputting an area of a moving object based on the occlusion accumulation image.

An image processing apparatus that generates an image of a mixed world by superimposing at least one virtual object on an image of a real world includes an acquisition unit configured to acquire information related to a motion status of a user of the image processing apparatus, a determination unit configured to determine a position for placing the at least one virtual object in the image of the real world, based on the motion status acquired by the acquisition unit, and an image processing unit configured to generate an image in which the at least one virtual object is placed at the position determined by the determination unit. The determination unit determines a position corresponding to a direction in which the user moves as the position for placing the at least one virtual object.

An image processing apparatus that generates an image of a mixed world by superimposing at least one virtual object on an image of a real world includes an acquisition unit configured to acquire information related to a motion status of a user of the image processing apparatus, a determination unit configured to determine a position for placing the at least one virtual object in the image of the real world, based on the motion status acquired by the acquisition unit, and an image processing unit configured to generate an image in which the at least one virtual object is placed at the position determined by the determination unit. The determination unit determines a position corresponding to a direction in which the user moves as the position for placing the at least one virtual object.

In some examples, while receiving captures of a first real world object, an electronic device displays a representation of a real world environment and a representation of the first real world object. In some examples, in response to receiving a first capture of a first portion of the first real world object and in accordance with a determination that the first capture satisfies one or more object capture criteria, the electronic device modifies a visual characteristic of the first portion of the representation of the first real world object. In some examples, an electronic device receives a request to capture the first real world object, and in response to the request, the electronic device determines a bounding volume around the representation of the first real world object and displays a plurality of capture targets on a surface of the bounding volume.

In some examples, while receiving captures of a first real world object, an electronic device displays a representation of a real world environment and a representation of the first real world object. In some examples, in response to receiving a first capture of a first portion of the first real world object and in accordance with a determination that the first capture satisfies one or more object capture criteria, the electronic device modifies a visual characteristic of the first portion of the representation of the first real world object. In some examples, an electronic device receives a request to capture the first real world object, and in response to the request, the electronic device determines a bounding volume around the representation of the first real world object and displays a plurality of capture targets on a surface of the bounding volume.

A dimension measurement apparatus that automatically corrects a deviation of a contour without operator determination. The dimension measurement apparatus includes: a model learning unit that obtains a learning cross-sectional image and learning labels attached to different regions of the learning cross-sectional image and generates a deep learning model for image region division using the learning cross-sectional image and the learning labels; a model estimation unit that applies the model to a newly input target image and labels each independent region; a contour correction unit that detects a contour of each region using the target image and the labels, sets a representative point on the contour of the region, moves each representative point according to a movement rule, and repeats movement of the representative point until contour correction is that measures a dimension of a device complete; and a dimension measurement unit cross-sectional structure using the corrected contour.

A dimension measurement apparatus that automatically corrects a deviation of a contour without operator determination. The dimension measurement apparatus includes: a model learning unit that obtains a learning cross-sectional image and learning labels attached to different regions of the learning cross-sectional image and generates a deep learning model for image region division using the learning cross-sectional image and the learning labels; a model estimation unit that applies the model to a newly input target image and labels each independent region; a contour correction unit that detects a contour of each region using the target image and the labels, sets a representative point on the contour of the region, moves each representative point according to a movement rule, and repeats movement of the representative point until contour correction is that measures a dimension of a device complete; and a dimension measurement unit cross-sectional structure using the corrected contour.

A first 2D recording of a specified reference view of an object is captured by a camera and, starting from the first 2D recording, a user's starting location relative to the object is ascertained by a computer vision module. Starting from the origin of a coordinate system as the starting location of the camera, one or more specified and/or settable relative positions in the vicinity of the object and/or in the object are determined as one or more locations for the respective perspective of the camera for taking at least one second 2D recording. The respective location in an object view on a display of the camera is displayed by a respective first augmented reality marker on the ground and/or on the object. The alignment of the camera with regard to angle and rotation with the perspective corresponding to the respective location is performed in this case by second augmented reality markers as auxiliary elements.