A contextual visual-based synthetic-aperture radar (SAR) target detection method and apparatus, and a storage medium, belonging to the field of target detection is described. The method includes: obtaining an SAR image; and inputting the SAR image into a target detection model, and positioning and recognizing a target in the SAR image by using the target detection model, to obtain a detection result. In the present disclosure, a two-way multi-scale connection operation is enhanced through top-down and bottom-up attention, to guide learning of dynamic attention matrices and enhance feature interaction under different resolutions. The model can extract the multi-scale target feature information with higher accuracy, for bounding box regression and classification, to suppress interfering background information, thereby enhancing the visual expressiveness. After the attention enhancement module is added, the detection performance can be greatly improved with almost no increase in the parameter amount and calculation amount of the whole neck.

In various examples, live perception from sensors of a vehicle may be leveraged to generate object tracking paths for the vehicle to facilitate navigational controls in real-time or near real-time. For example, a deep neural network (DNN) may be trained to compute various outputs—such as feature descriptor maps including feature descriptor vectors corresponding to objects included in a sensor(s) field of view. The outputs may be decoded and/or otherwise post-processed to reconstruct object tracking and to determine proposed or potential paths for navigating the vehicle.

Examples herein disclose capturing an image of printed text and a handwritten annotation and determining a topic as related to the printed text in the captured image. The examples disclose identifying a resource based on the handwritten annotation.

An information processing apparatus capable of displaying an image on a predetermined display unit, includes: a reception unit that receives a written input on an image according to an operation of a user in a state where the image is displayed on the display unit; a generation unit that generates a written object according to the written input received by the reception unit; a reference detection unit that detects a reference direction of the image displayed on the display unit; a correction unit that corrects the written object on the basis of the reference direction detected by the reference detection unit; and a display control unit that displays the written object generated by the generation unit.

An image recognition approach employs both computer generated and manual image reviews to generate image tags characterizing an image. The computer generated and manual image reviews can be performed sequentially or in parallel. The generated image tags may be provided to a requester in real-time, be used to select an advertisement, and/or be used as the basis of an internet search. In some embodiments generated image tags are used as a basis for an upgraded image review. A confidence of a computer generated image review may be used to determine whether or not to perform a manual image review.

A system and method for calibrating a machine vision system on the undercarriage of a rail vehicle while the rail vehicle is in the field is presented. The system enables operators to calibrate the machine vision system without having to remove the machine vision system from the undercarriage of the rail vehicle. The system can capture, by a camera of an image capturing module, a first image of a target. The image capturing module and a drum can be attached to a fixture and the target can be attached to the drum. The system can also determine a number of lateral pixels in a lateral pitch distance of the image of the target, determining a lateral object pixel size based on the number of lateral pixels, and determining a drum encoder rate based on the lateral object pixel size. The drum encoder rate can be programmed into a drum encoder.

Methods, systems, and apparatus, including computer programs encoded on computer-storage media, for underwater feed movement detection. In one aspect, the method may include the actions of obtaining images captured at different time points, where the images are captured by a camera and indicate feed that has been dispersed by a feeder for aquatic livestock inside an enclosure; determining, for each image, respective locations of the feed indicated by the image; determining, from the respective locations of the feed, a respective movement of the feed over the different time points; determining, based on the respective feed movement of the feed over the different time points, water current movement within the enclosure for the aquatic livestock; and outputting an indication of the water current movement.

A medical scan triaging system is operable to train a computer vision model and to generate abnormality data indicating abnormality probabilities for medical scans via the computer vision model. A first subset of medical scans is determined by identifying medical scans with abnormality probabilities greater than a first probability value of a triage probability threshold. A second subset of medical scans is determined by identifying medical scans with abnormality probabilities less than the first probability value. An updated first subset of medical scans is determined by identifying medical scans with abnormality probabilities greater than a second probability value of an updated triage probability threshold. An updated second subset of the plurality of medical scans is determined by identifying medical scans with a abnormality probabilities less than the second probability value. The updated first subset of medical scans is transmitted to client devices.

An unmanned aerial vehicle (UAV) assessment and reporting system may conduct micro scans of a wide variety of property types. A risk zone within which the UAV may navigate during the micro scan may include a plurality of virtual tags that identify navigational hazards relevant to the navigation of the UAV at a specific location or specify scan actions to be implemented while the UAV is at the specific location. Scan data from any of a wide variety of sensor types may be compared with profile data using computer vision techniques to identify characteristics, defects, damage, construction materials, and the like. A rule set evaluator may evaluate tags and/or matched profile data to determine adaptive actions to modify the navigation or scanning process of the UAV.

Aspects of the subject disclosure may include, for example, a method performed by a processing system including a processor, including receiving, from an augmented reality device, image data associated with a visual apparatus, determining whether the image data indicates a marker, and, responsive to determining that the image date indicates the marker, determining a first characteristic associated with a user of the augmented reality device, and sending a notification to an advertising server responsive to determining the image data includes the marker, where the advertising server sends content data to the augmented reality device responsive to the notification, and where the content data is selected by the advertising server according to the first characteristic associated with the user of the augmented reality device. Other embodiments are disclosed.