Embodiments of the present disclosure disclose a method and apparatus for extracting information, a device and a storage medium, relate to the field of image processing technology. The method may include: acquiring a location template corresponding to a category of a target document image; determining key point locations on the target document image; generating a transformation matrix based on the key point locations on the target document image and key point locations on the location template; determining locations of information corresponding to the target document image, based on locations of information on the location template and the transformation matrix; and extracting information at the locations of information corresponding to the target document image to obtain information in the target document image.

Systems and methods are disclosed, including a non-transitory computer readable medium storing computer executable instructions that when executed by a processor cause the processor to identify a first image, a second image, and a third image, the first image overlapping the second image and the third image, the second image overlapping the third image; determine a first connectivity between the first image and the second image; determine a second connectivity between the first image and the third image; determine a third connectivity between the second image and the third image, the second connectivity being less than the first connectivity, the third connectivity being greater than the second connectivity; assign the first image, the second image, and the third image to a cluster based on the first connectivity and the third connectivity; conduct a bundle adjustment process on the cluster of the first image, the second image, and the third image.

A method and a device for 3D shape matching based on a local reference frame are proposed. After acquiring a 3D point cloud and feature points in the method, the feature point set is projected to a plane, and feature transformation is performed on the projected points by using at least one factor from the distances between the 3D points and the feature points, the distances between the 3D points and the projected points, and the average distances between the 3D points and its 1-ring neighboring points to acquire a point distribution with a larger variance in a certain direction than the projected point set, and the local reference frame is determined based on the transformed point distribution. The 3D local feature descriptor established based on this local reference frame can encode the 3D local surface information more robustly, so as to obtain a better 3D shape matching effect.

A method for controlling personal protective equipment. The method includes positioning of a person equipped with personal protective equipment next to an electrical enclosure, wherein a specific personal protective equipment requirement is defined for the electrical enclosure. The method further includes scanning the personal protective equipment in a contact-less manner while the person is next to the electrical enclosure. Thereby scanned personal protective equipment information are generated. The method further includes comparing the scanned personal protective equipment information with the specific personal protective equipment requirement and evaluating, based on the comparison, whether the personal protective equipment is in accordance with the specific personal protective equipment requirement. The method further includes providing a feedback indicating whether or not the personal protective equipment is in accordance with the specific personal protective equipment requirement.

Systems and methods for automatically excluding artifacts from an analysis of a biological specimen image are disclosed. An exemplary method includes obtaining an immunohistochemistry (IHC) image and a control image, determining whether the control image includes one or more artifacts, upon a determination that the control image includes one or more artifacts, identifying one or more artifact regions within the IHC image by mapping the one or more artifacts from the control image to the IHC image, and performing image analysis of the IHC image where any identified artifact regions are excluded from the image analysis.

The present application discloses a verification method and device for a modeling route, an unmanned vehicle, and a storage medium, which relate to the technical field of computer vision and intelligent transportation. A specific implementation of the method in the present application lies in: acquiring a filtering threshold of a target road section, where the filtering threshold is related to image feature points corresponding to the target road section; verifying a modeling route corresponding to the target road section through the filtering threshold to obtain a verification result. According to the present application, availability of the modeling route can be directly verified with the filtering threshold while there is no need to verify the modeling route through manual driving of the vehicle, thereby effectively increasing the verification efficiency, protecting the vehicle from travelling along an unavailable modeling route and improving the driving experience.

A method for classifying measuring points of a point cloud ascertained by at least one sensor, in particular, a point cloud ascertained from a LIDAR sensor, a radar sensor and/or a camera sensor, via a control unit. Local surface vectors to adjacent measuring points are ascertained for each measuring point of the point cloud. For each local surface vector, respectively one angle is calculated between the local surface vectors with respect to a gravity vector. A maximal surface vector having a maximal angle with respect to the gravity vector and a standardized surface vector are ascertained for each measuring point of the point cloud based on the calculated angles. Each measuring point of the point cloud includes a standardized surface vector and/or includes a maximal surface vector having an angle with respect to the gravity vector above a limiting value being classified as a non-ground point.

The present disclosure is directed to a software tool that engages in a pattern matching technique. In one implementation, the software tool retrieves a two-dimensional drawing and identifies walls as lines, rotates the drawing until a threshold number of lines are aligned with either the X or Y axes, discards lines that are not aligned with either the X or Y axis, identifies intersection points, identifies a subset of intersection points that have a maxima or minima coordinate, constructs a data library indicative of the relative positions of the points in the identified subset; and compares the constructed data libraries for the two-dimensional drawing to data libraries constructed for another two-dimensional drawing.

Embodiments provide an image processing method, program, and apparatus, for using a database of data manifestations of objects to identify those objects in image data representing a domain or space containing objects to be identified. Embodiments leverage 3D vector field representations of both the domain or space, and of the objects, to perform the recognition. Embodiments annotate the image data with information relating to the identified objects, and/or replace portions of the image data with a data manifestation of the recognised object imported from the database.

In various examples, one or more deep neural networks (DNNs) are executed to regress on control points of a curve, and the control points may be used to perform a curve fitting operation—e.g., Bezier curve fitting—to identify landmark locations and geometries in an environment. The outputs of the DNN(s) may thus indicate the two-dimensional (2D) image-space and/or three-dimensional (3D) world-space control point locations, and post-processing techniques—such as clustering and temporal smoothing—may be executed to determine landmark locations and poses with precision and in real-time. As a result, reconstructed curves corresponding to the landmarks—e.g., lane line, road boundary line, crosswalk, pole, text, etc.—may be used by a vehicle to perform one or more operations for navigating an environment.