Provided are VR image processing method and apparatus. The method includes: rendering left-eye and right-eye viewpoint regions based on left-eye and right-eye view angles respectively, to obtain left-eye and right-eye viewpoint images; determining a candidate region based on positions of the left-eye and right-eye view angles, and selecting a point in the candidate region as a peripheral image view angle; rendering left-eye and right-eye viewpoint peripheral regions based on the peripheral image view angle, to obtain a same viewpoint peripheral image; and splicing the viewpoint peripheral image with the left-eye viewpoint image and with the right-eye viewpoint image to obtain a left-eye complete image and a right-eye complete image.

The present invention discloses a method for fully automatically detecting chessboard corner points, and belongs to the field of image processing and computer vision. Full automatic detection of chessboard corner points is completed by setting one or a plurality of marks with colors or certain shapes on a chessboard to mark an initial position, shooting an image and conducting corresponding processing, using a homography matrix H calculated by initial pixel coordinates of a unit grid in a pixel coordinate system and manually set world coordinates in a world coordinate system to expand outwards, and finally spreading to the whole chessboard region. The method has the advantages of simple procedure and easy implementation; the principle of expanding outwards by a homography matrix is used, so that the running speed of the algorithm is fast; and the corner points obtained by a robustness enhancement algorithm is more accurate, so that the situation of inaccurate corner point detection in the condition of complex illumination is avoided.

The present disclosure relates to an image processing device and method, and a program that can more easily perform a write operation on an image.

Write information corresponding to a feature of an image of processing target is searched for on the basis of relevant information that associates the write information with the feature of the image. For example, it is furthermore possible to search for write information corresponding to the feature of an image of the processing target on the basis of the learning result of the user's behavior. The present disclosure can be applied to, for example, an information processing device, an image processing device, electronic equipment, an image processing method, a program, or the like.

A method for simultaneous localization and mapping (SLAM) employs dual event-based cameras. Event streams from the cameras are processed by an image processing system to stereoscopically detect surface points in an environment, dynamically compute pose of a camera as it moves, and concurrently update a map of the environment. A gradient descent based optimization may be utilized to update the pose for each event or for each small batch of events.

Disclosed are a method for simultaneous localization and mapping, device, system and storage medium. The method comprises: acquiring a local binocular vision image and a local three-dimensional laser point cloud image that can be collected at present; preprocessing the local binocular vision image and the local three-dimensional laser point cloud image, respectively; acquiring a position and an attitude of a local binocular vision map according to the preprocessed local binocular vision image; fusing the position and attitude of the local binocular vision map with the preprocessed local three-dimensional laser point cloud image to obtain a local fusion map; and performing a global consistency optimization on a global map according to the local fusion map to obtain a dense point cloud map and output a current position information and a current attitude information in real time so as to complete the simultaneous localization and mapping (SLAM), wherein the global map is constructed according to a global three-dimensional laser point cloud image.

A method of generating labeled training images for a machine learning system includes providing a set of labeled images, each of the labeled images in the set of labeled images depicting an instance of a type of object and comprising a label identifying the type of object, providing an unlabeled image including an instance of the object, generating bounding box coordinates for one or more bounding boxes around the instance of the object in the unlabeled image using the labeled images in the set of labeled images as templates, consolidating the one or more bounding boxes into a consolidated bounding box around the instance of the object in the unlabeled image, and labeling the consolidated bounding box according to the type of object to generate a labeled output image including bounding box coordinates of the consolidated bounding box.

A method of a defect detection of a plurality of semiconductor structures arranged on a wafer includes obtaining a microscopic image of the wafer. The microscopic image depicts the plurality of semiconductor structures. The method also includes obtaining, from a database, fingerprint data for each base pattern class of a set of base pattern classes associated with respective one or more semiconductor structures of the plurality of semiconductor structures. The method further includes performing the defect detection based on the fingerprint data and the microscopic image.

A method for monitoring a person performing a physical exercise based on a sequence of image frames showing an exercise activity of the person. The method includes extracting, based on the sequence of image frames, for each image frame a set of body key points using a neural network, the set of body key points being indicative of a posture of the person in the image frame; deriving, based on a subset of the body key points in each image frame, at least one characteristic parameter indicating a progression of a movement of the person; detecting a start loop condition by evaluating the time progression of the at least one characteristic parameter, said start loop condition indicating a transition from a start posture of the person to the movement of the person when performing the physical exercise, wherein a loop of exercising encompasses one single repetition of the physical exercise; detecting an end loop condition by evaluating the time progression of at least one of the characteristic parameters, said end loop condition indicating a transition from the movement of the person when performing the physical exercise to an intermediate posture, wherein, as a result, the start of the loop and the end of the loop are determined; and deriving the time period for a single loop of the physical exercise based on the start of the loop and the end of the loop and evaluating the time period.

A generation device includes: a communication interface via which two-dimensional (2D) images are to be received, the 2D images having been generated by photographing a space from different viewpoints with at least one camera; and a processor connected to the communication interface and configured to determine, according to the 2D images, a matching pattern to match feature points included in two pictures among the 2D images, match the feature points according to the matching pattern in order to generate first three-dimensional (3D) points, the first 3D points indicating respective first positions in the space, generate second 3D point based on the 2D images, the second 3D point indicating a second position in the space, and generate a 3D model of the space based on the first 3D points and the second 3D point.

A sketch-based image retrieval method, device and system, to improve accuracy of image searching from a scene sketch image. For example, the image retrieval method, device and system can be used to retrieve a target scene image from a collection of stored images in a storage (i.e., an image collection). The image retrieval method includes: segmenting the scene sketch image using an image segmentation module into semantic object-level instances, and fine-grained features are obtained for each object instance, generating an attribute graph which integrates the fine-grained features for each semantic object instance detected from the query scene sketch image, generating a feature graph by using a graph encoder module from the attribute graph, and computing a similarity or distance between the feature graphs of the query scene sketch image and the scene images in the image collection by a graph matching module and the most similar scene images are returned.