Systems and method for automatically generating structures, such as target volumes, in a treatment image using structure-guided deformation to propagate the structures from a planning image onto the subsequently acquired treatment image.

The present invention provides an oral image marker detection method for detecting a marker, which is the reference when an oral scan image and a CT image are matched, the method comprising the steps of: aligning oral scan images in the horizontal plane direction; estimating a marker candidate area from the aligned oral scan images and detecting an undivided marker from the marker candidate area; and dividing the undivided marker to estimate a marker in the undivided marker, and estimating the area from the upper surface of the undivided marker to a certain depth in the lower direction to be the marker.

Described herein are systems, methods, and non-transitory computer readable media for performing an alignment between a first vehicle sensor and a second vehicle sensor. Two-dimensional (2D) data indicative of a scene within an environment being traversed by a vehicle is captured by the first vehicle sensor such as a camera or a collection of multiple cameras within a sensor assembly. A three-dimensional (3D) representation of the scene is constructed using the 2D data. 3D point cloud data also indicative of the scene is captured by the second vehicle sensor, which may be a LiDAR. A 3D point cloud representation of the scene is constructed based on the 3D point cloud data. A rigid transformation is determined between the 3D representation of the scene and the 3D point cloud representation of the scene and the alignment between the sensors is performed based at least in part on the determined rigid transformation.

A computer-implemented method for processing a 3-D point cloud data and an associated image data to enrich the 3-D point cloud data with relevant portions of the image date. The method comprises generating a 3-D point cloud data tensor representative of information contained in the 3-D point cloud data and generating an image tensor representative of information contained in the image data; and then analyzing the image tensor to identify a relevant data portion of the image information relevant to the at least one object candidate. The method further includes amalgamating the 3-D point cloud data tensor with a relevant portion of the image tensor associated with the relevant data portion of the image information to generate an amalgamated tensor associated with the surrounding area and storing the amalgamated tensor to be used by a machine learning algorithm (MLA) to determine presence of the object in the surrounding area.

There is provided with an image processing apparatus. An obtaining unit obtains a first image serving as a read image of an inspection target medium having undergone printing, and a second image serving as a read image of a reference medium representing a target print result. An inspection unit inspects a defect on the inspection target medium based on the first image and the second image by performing inspection at inspection settings different between a print region and a peripheral region of the inspection target medium.

The present invention generally relates to an apparatus and a method for obtaining a registration error map representing a level of sharpness of an image. Many methods are known which allow determining the position of a camera with respect to an object, based on the knowledge of a 3D model of the object and the intrinsic parameters of the camera. However, regardless of the visual servoing technique used, there is no control in the image space and the object may get out of the camera field of view during servoing. It is proposed to obtain a registration error map relating to an image of the object of interest generated by computing an intersection of a re-focusing surface obtained from a 3D model of said object of interest and a focal stack based on acquired four-dimensional light-field data relating to said object of interest.

The present application discloses a method and an apparatus of obstacle three-dimensional position acquisition for a roadside computing device, and relates to the fields of intelligent transportation, cooperative vehicle infrastructure, and autonomous driving. The method may include: acquiring pixel coordinates of an obstacle in a to-be-processed image; determining coordinates of a bottom surface center point of the obstacle according to the pixel coordinates; acquiring a homography relationship between a ground surface corresponding to the to-be-processed image and a ground surface corresponding to a template image; transforming the coordinates of the bottom surface center point of the obstacle into coordinates on the template image according to the homography relationship; and determining three-dimensional coordinates of the bottom surface center point of the obstacle according to the coordinates obtained by transformation and a ground equation corresponding to the template image. By use of the solution of the present application, implementation costs can be saved, and the accuracy is better.

A range of images projected by a first projector group and a range of images projected by a second projector group are set to coincide with each other. A first setting screen including a first region for setting positions of images projected by a respective plurality of projectors included in the first projector group and a second region for setting positions of images projected by a respective plurality of projectors included in the second projector group is displayed. Operation by a user on the first setting screen is received. The positions of the images projected by the respective plurality of projectors included in the first projector group and the positions of the images projected by the respective plurality of projectors included in the second projector group are set.

This disclosure provides systems, methods, and apparatus detecting defects in a substrate. An image of the substrate is compared with a reference image to identify potential defects. Images corresponding to the potential defects are processed sequentially by a set of classifiers to generate a set of images that include a defect. The set of classifiers can be arranged to have increasing accuracy. A subset of the images corresponding to the potential defects is processed by a type classifier that can determine the type, size, and location of the defect in the images. The defects can be further processed to determine the severity of the defects based on the location of the defects on the substrate.

Three-dimensional (3D) point cloud generation using a stationary laser scanner and a mobile scanner. The method includes scanning a first part of a surrounding with the stationary laser scanner, obtaining a first 3D point cloud, scanning a second part of the surrounding with the mobile scanner, obtaining a second 3D point cloud, whereby there is an overlap region of the first part and the second part, and aligning the second 3D point cloud to the first 3D point cloud to form a combined 3D point cloud. The positional accuracy of points of the second 3D point cloud is increased by automatically referencing second scanner data of the overlap region, generated by the mobile scanner, to first scanner data of the overlap region, generated by the stationary laser scanner. Therewith, deformations of the second 3D point cloud and its alignment with the first 3D point cloud are corrected.