An angled optical pattern is decoded. To decode an optical pattern imaged at an angle, an area of interest of an image is received. A start line and an end line of the optical pattern are estimated. Corners of the optical pattern are localized. A homography is calculated based on the corners. And a scanline of the optical pattern is rectified based on the homography.

A method for assigning a number of lanes and a direction of travel on a path includes receiving location data including a plurality of location points, projecting the plurality of location points on to an aggregation axis perpendicular to a centerline of the vehicle path, grouping the plurality of location points as projected onto the aggregation axis into one or more clusters, and determining the number of vehicle lanes of the vehicle path based on a count of the one or more clusters.

A Method, apparatus, device, and storage media for image processing are provided. The method include: acquiring a set of image sequences, the set of image sequences including a plurality of image sequences; determining a first similarity measurement between image sequences in the set of image sequences; dividing the set of image sequences into one or more subset of image sequences based on a first similarity measurement; and determining, in each subset of image sequences, degrees of correlation between images in one image sequence of the subset of image sequences and images in other image sequences of the subset of image sequences.

A Method, apparatus, device, and storage media for image processing are provided. The method include: acquiring a set of image sequences, the set of image sequences including a plurality of image sequences; determining a first similarity measurement between image sequences in the set of image sequences; dividing the set of image sequences into one or more subset of image sequences based on a first similarity measurement; and determining, in each subset of image sequences, degrees of correlation between images in one image sequence of the subset of image sequences and images in other image sequences of the subset of image sequences.

A test support method includes a step of obtaining a pre-change image and a post-change image to be displayed on a monitoring and control system, a step of extracting, from the post-change image, multiple symbols that have changed from corresponding symbols in the pre-change image, a step of adding order information to the multiple symbols extracted, and a step of outputting a test image in which the order information is added to the multiple symbols.

A test support method includes a step of obtaining a pre-change image and a post-change image to be displayed on a monitoring and control system, a step of extracting, from the post-change image, multiple symbols that have changed from corresponding symbols in the pre-change image, a step of adding order information to the multiple symbols extracted, and a step of outputting a test image in which the order information is added to the multiple symbols.

The present technology relates to an information processing apparatus, an information processing method, and a program capable of improving the efficiency and accuracy of clustering. The information processing apparatus estimates a presence area of an object and the number of objects on the basis of a captured image, generates point cloud data from distance measurement information acquired by a distance measuring sensor, and recognizes the object by determining a point cloud, which is a target of clustering, in the point cloud data generated and the number of clusters on the basis of the presence area of the object and the number of the objects, which are estimated, and performing clustering on the point cloud data. The present technology can be applied to an autonomous mobile robot system.

The present technology relates to an information processing apparatus, an information processing method, and a program capable of improving the efficiency and accuracy of clustering. The information processing apparatus estimates a presence area of an object and the number of objects on the basis of a captured image, generates point cloud data from distance measurement information acquired by a distance measuring sensor, and recognizes the object by determining a point cloud, which is a target of clustering, in the point cloud data generated and the number of clusters on the basis of the presence area of the object and the number of the objects, which are estimated, and performing clustering on the point cloud data. The present technology can be applied to an autonomous mobile robot system.

The techniques described herein relate to computerized methods and apparatus for grouping images of objects based on semantic and visual information associated with the objects. The techniques described herein further relate to computerized methods and apparatus for training a machine learning model for object recognition.

The techniques described herein relate to computerized methods and apparatus for grouping images of objects based on semantic and visual information associated with the objects. The techniques described herein further relate to computerized methods and apparatus for training a machine learning model for object recognition.