Methods and systems for implementing artificial intelligence enabled metrology are disclosed. An example method includes segmenting a first image of structure into one or more classes to form an at least partially segmented image, associating at least one class of the at least partially segmented image with a second image, and performing metrology on the second image based on the association with at least one class of the at least partially segmented image.

The present application is applicable to the field of video processing. Provided are a target tracking method for a panoramic video, a readable storage medium, and a computer device. The method comprises: using a tracker to track and detect a target to be tracked to obtain a predicted tracking position of said target in the next panoramic video frame, calculating the reliability of the predicted tracking position, and using an occlusion detector to calculate an occlusion score of the predicted tracking position; determining whether the reliability of the predicated tracking position is greater than a preset reliability threshold value, and determining whether the occlusion score of the predicted tracking position is greater than a preset occlusion score threshold value; and using a corresponding tracking strategy according to the reliability and the occlusion score. By means of the present application, whether a tracking failure is caused by the loss of a target or occlusion can be determined, such that a corresponding tracking recovery strategy can be used, and tracking can be automatically recovered when tracking fails, thereby achieving the effect of performing tracking continuously for a long time. In addition, the method of the present invention has a low operation complexity and a good real-time performance.

An illustrative augmented reality tracking system obtains a volumetric feature descriptor dataset that includes: 1) a plurality of feature descriptors associated with a plurality of views of a volumetric target, and 2) a plurality of 3D structure datapoints that correspond to the plurality of feature descriptors. The system also obtains an image frame captured by a user equipment (UE) device. The system identifies a set of image features depicted in the image frame and detects, based on a match between the set of image features depicted in the image frame and a set of feature descriptors of the plurality of feature descriptors, that the volumetric target is depicted in the image frame. In response to this detecting and based on 3D structure datapoints corresponding to matched feature descriptors, the system determines a spatial relationship between the UE device and the volumetric target. Corresponding methods and systems are also disclosed.

A method for identifying, locating, and mapping targets of interest using unmanned aerial vehicle (UAV) camera footage in GPS-denied environments. In one embodiment, the method comprises obtaining UAV visual data, passing the UAV visual data through a convolutional neural network (CNN) in order to detect targets of interest based on visual features disposed in the UAV visual data, wherein the detection by the CNN defines reference points and pixel coordinates for the UAV visual data, applying a geometric transformation to known and defined pixel coordinates to obtain real-world orthogonal positions; and projecting the detected targets of interest onto an orthogonal map based on the obtained real-world orthogonal positions, all without GPS data.

A computer-implemented method may identify attributes of electronic images and display the attributes. The method may include receiving one or more electronic medical images associated with a pathology specimen, determining a plurality of salient regions within the one or more electronic medical images, determining a predetermined order of the plurality of salient regions, and automatically panning, using a display, across the one or more salient regions according to the predetermined order.

Exemplary vehicle positioning method and apparatus, and a non-transitory computer-readable storage medium can be provided which can be used and/or implemented for, e.g., acquiring an image of surroundings of a vehicle; performing feature extraction on the image to obtain at least one keypoint in the image and acquiring a visual feature descriptor corresponding to each keypoint of the at least one keypoint; performing element recognition on the image to obtain at least one natural positioning element in the image and acquiring identification information corresponding to each natural positioning element of the at least one natural positioning element; and positioning the vehicle based on the visual feature descriptor corresponding to each keypoint and the identification information corresponding to each natural positioning element.

An image processing apparatus includes a processor configured to: display an image on a display device; calculate, for each of small regions set in the image, a degree of importance based on characteristics of the image; and display a degree-of-importance map on the display device in such a manner that the degree-of-importance map is superimposed on a subject region of the image, the degree-of-importance map visually representing a relative relationship between the degrees of importance of the small regions.

This description relates to image feature extraction. In some examples, a system can include a keypoint detector and a feature list generator. The keypoint detector can be configured to upsample a keypoint score map to produce an upsampled keypoint score map. The keypoint score map can include feature scores indicative of a likelihood of at least one feature being present at keypoints in an image. The feature list generator can be configured to identify a subset of keypoints of the keypoints in the image using the feature scores of the up sampled keypoint score map, determine descriptors for the subset of keypoints based on a feature description map, and generate a keypoint descriptor map for the image based on the determined descriptors.

A computer-implemented method for annotating images is disclosed. The computer-implemented method includes generating a saliency map corresponding to an input image, wherein the input image is an image that requires annotation, generating a behavior saliency map, wherein the behavior saliency map is a saliency map formed from an average of a plurality of objects contained within respective bounding boxes of a plurality of sample images, generating a historical saliency map, wherein the historical saliency map is a saliency map formed from an average of a plurality of tagged objects in the plurality of sample images, fusing the saliency map corresponding to the input image, the behavior saliency map, and the historical saliency map to form a fused saliency map, and generating, based on the fused saliency map, a bounding box around an object in the input image.

A method for 2D semantic keypoint detection and tracking is described. The method includes learning embedded descriptors of salient object keypoints detected in previous images according to a descriptor embedding space model. The method also includes predicting, using a shared image encoder backbone, salient object keypoints within a current image of a video stream. The method further includes inferring an object represented by the predicted, salient object keypoints within the current image of the video stream. The method also includes tracking the inferred object by matching embedded descriptors of the predicted, salient object keypoints representing the inferred object within the previous images of the video stream based on the descriptor embedding space model.