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 detection device includes a camera and a detector. The camera captures an image of a human face. The detector detects a position of a human eye based on the captured image output from the camera by template matching.

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 system and method for the detection and tracking of droplets sprayed from an agricultural spray nozzle is provided. The system includes a sensor configured to observe droplets sprayed from the nozzle, a frame extractor module, a droplet shape and size extraction module, a droplet tracking module, and a data log module. The system may provide an artificial intelligence (AI)-enabled framework capable of processing images obtained of droplets, detecting and tracking all droplets appearing across image frames, and determining the droplets' geometric and dynamic data. The system further provides for the integration of deep-learning techniques into an image processing algorithm which enables precise and reliable determination of droplet characteristics. In addition, the deep-learning framework produces consistent results under a variety of uncertain imaging conditions.

Systems and methods for assessing plotting device accuracy in aid of a process for qualifying plotter systems. The system and process involve an ideal (virtual) test pattern consisting of digital data defining a nominal grid augmented by geometric features (e.g., closed two-dimensional geometric shapes which respectively surround intersections or vertices of the nominal grid). The plotting device under test is commanded to print a test plot having a pattern that matches the test pattern, but the test plot may deviate from the test pattern. To measure the amount of deviation, first an image of the test plot on the printed medium is captured using an accurate optical scanner. Then a mathematical measurement process, implemented in a computer vision application (e.g., a software package), is employed to detect deviations of the test plot from the test pattern. A statistical analysis is then performed to determine whether the deviations are within specifications.

A method and system are provided for automatically detecting characteristic points of a herringbone fabric with a view to automatically cutting pieces. The herringbone patterns are formed by V-shaped features with vertices that are aligned along a plurality of parallel axes. The method proceeds with a step of acquiring an image of a segment of the fabric, a detection initialization step including, on the basis of predefined parameters or on the basis of the image, acquiring geometric parameters of the herringbone patterns and defining lines of operation perpendicular to the axes of the herringbone patterns, and a step of determining, in the image, coordinates of points of passage of axes of the herringbone patterns along lines of operation via the optimization of a criterion of symmetry of two mirror sub-images acquired along lines of operation.

An augmented reality processing device is provided, comprising an image capturing circuit and a processor. The processor is connected to the image capturing circuit, and execute operations of: generating an original point cloud image according to the first environment image and a physical object in the first environment image; generating an expanded point cloud image corresponding to the physical object from the second environment image according to the first environment image and the physical object point cloud set, and generating a superimposed point cloud image according to the expanded point cloud image and the original point cloud image; and generating a transformation matrix according to the original point cloud image and the expanded point cloud image, and superimposing a virtual object to the second environment image according to the superimposed point cloud image and the transformation matrix.

Various disclosed embodiments are directed to classify or determining an image style of a target image according to a consumer application based on determining a similarity score between the image style of a target image and one or more other predetermined image styles of the consumer application. Various disclosed embodiments can resolve image style transfer destructiveness functionality by making various layers of predetermined image styles modifiable. Further various embodiments resolve tedious manual user input requirements and reduce computing resource consumption, among other things.

The present invention relates to a detection method for an assembly body multi-view change based on feature matching, comprising the following steps: S1, acquiring a first image and a second image; S2, performing feature point extraction and feature matching on the first image and the second image to obtain a matching pair set, a first unmatched point set of the first image and a second unmatched point set of the second image; S3, acquiring a first to-be-matched area set of the first image according to the first unmatched point set; acquiring a second to-be-matched area set of the second image according to the second unmatched point set; S4, performing feature matching on each first unmatched area and each second unmatched area one by one to obtain a plurality of matching results; and S5, outputting the assembly body change type according to the plurality of matching results.

Devices, methods, and program storage devices for training and leveraging machine learning (ML) models to use in image registration, especially on unaligned multispectral images, are disclosed, comprising: obtaining aligned multispectral image data; generating a first plurality of feature descriptors for features identified in the aligned multispectral image data; generating a training set of feature descriptor pairs based on the first plurality of feature descriptors; and training a ML model based on the training set of feature descriptor pairs, wherein the trained ML model is configured to determine matches between features in unaligned multispectral image data. The techniques may then: obtain unaligned multispectral image data; generate a second plurality of feature descriptors for features identified in the unaligned multispectral image data; and use the trained ML model to determine matches between features in the second plurality of feature descriptors, which matches may be used in performing image registration and/or fusion operations.