Abstract: A multi-machine cooperation method, a scheduling device, and a multi-machine cooperation system are described. The multi-machine cooperation method includes: determining, by a first autonomous robot when detecting an abnormal condition during operation, whether the abnormal condition can be independently processed; and when the abnormal condition cannot be independently processed, sending, by the first autonomous robot, an assistance request to another device in an Internet of Things in which the first autonomous robot is located. In the specification, a multi-machine cooperation operation between autonomous robots or between an autonomous robot and another device can be implemented.

Abstract: During pre-processing, a 3D model of the object is rendered for various poses by arranging virtual point light sources around the lens of a virtual camera. The shadows are used to obtain oriented depth edges of the object illuminated from multiple directions. The oriented depth edges are stored in a database. A camera acquires images of the scene by casting shadows onto the scene from different directions. The scene can include one or more objects arranged in arbitrary poses with respect to each other. The poses of the objects are determined by comparing the oriented depth edges obtained from the acquired images to the oriented depth edges stored in the database. The comparing evaluates, at each pixel, a cost function based on chamfer matching, which can be speed up using downhill simplex optimization.

Abstract: During pre-processing, a 3D model of the object is rendered for various poses by arranging virtual point light sources around the lens of a virtual camera. The shadows are used to obtain oriented depth edges of the object illuminated from multiple directions. The oriented depth edges are stored in a database. A camera acquires images of the scene by casting shadows onto the scene from different directions. The scene can include one or more objects arranged in arbitrary poses with respect to each other. The poses of the objects are determined by comparing the oriented depth edges obtained from the acquired images to the oriented depth edges stored in the database. The comparing evaluates, at each pixel, a cost function based on chamfer matching, which can be speed up using downhill simplex optimization.

Abstract: A method for improving a thoracic diagnostic image for the detection of nodules. Non-lung regions are removed from the diagnostic image to provide a lung image. Vessels and vessel junctions of the lung(s) in the lung image are enhanced according to a first-order partial derivative of each of a plurality of voxels of the lung image. A vessel tree representation is constructed from the enhanced vessels and vessel junctions. The vessel tree representation can be subtracted from the lung image to enhance the visibility of nodules in the lung(s).

Abstract: A method for improving a thoracic diagnostic image for the detection of nodules. Non-lung regions are removed from the diagnostic image to provide a lung image. Vessels and vessel junctions of the lung(s) in the lung image are enhanced according to a first-order partial derivative of each of a plurality of voxels of the lung image. A vessel tree representation is constructed from the enhanced vessels and vessel junctions. The vessel tree representation can be subtracted from the lung image to enhance the visibility of nodules in the lung(s).