A vision inspection system includes a sorting platform having an upper surface supporting parts for inspection, wherein the parts are configured to be loaded onto the upper surface of the sorting platform in a random orientation. The vision inspection system includes an inspection station including an imaging device. The vision inspection system includes a vision inspection controller receiving images and processing the images based on an image analysis model to determine inspection results for each of the parts. The vision inspection controller has a shape recognition tool configured to recognize the parts in the field of view regardless of the orientation of the parts on the sorting platform. The vision inspection controller has an AI learning module operated to customize and configure the image analysis model based on the images received from the imaging device.

Various aspects of a system and a method for determining a position of one or more multi-dimensional objects are disclosed herein. In accordance with an embodiment, the system may include a memory and a processor. The processor may be configured to obtain, from a plurality of satellite images, shadow data of a first multi-dimensional object from one or more multi-dimensional objects on a visible surface. The processor may be configured to obtain, from a server, base elevation data and height data of the first multi-dimensional object. The processor may be further configured to generate a Digital Elevation Model (DEM) of the plurality of multi-dimensional objects. The processor may be further configured to determine a position of a second multi-dimensional object of the plurality of multi-dimensional objects on the visible surface, based on the generated DEM.

The present disclosure discloses a photography-based 3D modeling system and method, and an automatic 3D modeling apparatus and method, including: (S1) attaching a mobile device and a camera to the same camera stand; (S2) obtaining multiple images used for positioning from the camera or the mobile device during movement of the stand, and obtaining a position and a direction of each photo capture point, to build a tracking map that uses a global coordinate system; (S3) generating 3D models on the mobile device or a remote server based on an image used for 3D modeling at each photo capture point; and (S4) placing the individual 3D models of all photo capture points in the global three-dimensional coordinate system based on the position and the direction obtained in S2, and connecting the individual 3D models of multiple photo capture points to generate an overall 3D model that includes multiple photo capture points.

An example method includes obtaining, a virtual model of a portion of an anatomy of a patient obtained from a virtual surgical plan for an orthopedic joint repair surgical procedure to attach a prosthetic to the anatomy; identifying, based on data obtained by one or more sensors, positions of one or more physical markers positioned relative to the anatomy of the patient; and registering, based on the identified positions, the virtual model of the portion of the anatomy with a corresponding observed portion of the anatomy.

An example method includes obtaining, a virtual model of a portion of an anatomy of a patient obtained from a virtual surgical plan for an orthopedic joint repair surgical procedure to attach a prosthetic to the anatomy; identifying, based on data obtained by one or more sensors, positions of one or more physical markers positioned relative to the anatomy of the patient; and registering, based on the identified positions, the virtual model of the portion of the anatomy with a corresponding observed portion of the anatomy.

An electronic device and a method for tracking an object thereof are provided. The electronic device identifies whether there is a first object being tracked, when obtaining an image and rotation information of a camera of the electronic device, corrects state information of the first object using the rotation information, when there is the first object, detects a second object matched to the first object from the image based on the corrected state information, and tracks a position of the second object using an object tracking algorithm.

In some examples, an unmanned aerial vehicle (UAV) employs one or more image sensors to capture images of a scan target and may use distance information from the images for determining respective locations in three-dimensional (3D) space of a plurality of points of a 3D model representative of a surface of the scan target. The UAV may compare a first image with a second image to determine a difference between a current frame of reference position for the UAV and an estimate of an actual frame of reference position for the UAV. Further, based at least on the difference, the UAV may determine, while the UAV is in flight, an update to the 3D model including at least one of an updated location of at least one point in the 3D model, or a location of a new point in the 3D model.

In some examples, an unmanned aerial vehicle (UAV) employs one or more image sensors to capture images of a scan target and may use distance information from the images for determining respective locations in three-dimensional (3D) space of a plurality of points of a 3D model representative of a surface of the scan target. The UAV may compare a first image with a second image to determine a difference between a current frame of reference position for the UAV and an estimate of an actual frame of reference position for the UAV. Further, based at least on the difference, the UAV may determine, while the UAV is in flight, an update to the 3D model including at least one of an updated location of at least one point in the 3D model, or a location of a new point in the 3D model.

A method for generating a parallax-tolerant panoramic image includes obtaining a point cloud captured by a depth sensor, the point cloud representing a support structure bearing a set of objects; obtaining a set of images of the support structure and the set of objects, the set of images captured by an image sensor from a plurality of positions alongside a length of the support structure; generating a mesh structure using the point cloud, the mesh structure including a plurality of patches and representing a surface of the support structure and the set of objects; for each patch in the mesh structure, selecting an image from the set of images and projecting the selected image to the mesh patch; and generating an orthographic projection of the mesh structure onto a shelf plane of the support structure.

A method for generating a parallax-tolerant panoramic image includes obtaining a point cloud captured by a depth sensor, the point cloud representing a support structure bearing a set of objects; obtaining a set of images of the support structure and the set of objects, the set of images captured by an image sensor from a plurality of positions alongside a length of the support structure; generating a mesh structure using the point cloud, the mesh structure including a plurality of patches and representing a surface of the support structure and the set of objects; for each patch in the mesh structure, selecting an image from the set of images and projecting the selected image to the mesh patch; and generating an orthographic projection of the mesh structure onto a shelf plane of the support structure.