According to the present invention, an image recognition system calculates importance of a feature for each target shape recognized in an image and for each type of feature, and determines correctness of a recognition result by comparing the importance with a statistic for each type of feature, for each target shape.

The invention relates to a method for automated calibration of sensors of a vehicle, wherein at least one first passive optical sensor and at least one second active optical sensor are calibrated by a calibration unit based on a matching spatial orientation of recognised environmental features in transformed sensor data of the first sensor and the sensor data captured by the second sensor.

A method for cargo counting, a computer equipment, and a storage medium are provided in the disclosure. The method includes the following. Three-dimensional (3D) point cloud data of a set of cargoes within a preset placement region is obtained based on a cargo-counting instruction. Whether the set of cargoes are in a first placement state is determined according to the 3D point cloud data. Based on a determination that the set of cargoes are in the first placement state, a quantity of the set of cargoes is calculated.

Systems and methods for detecting anomalies in aviation data communication systems (e.g., air traffic control surveillance systems), include a processor receiving device status information. A variational autoencoder receives and optimizes the device status information and determines whether it qualifies as an anomaly. Optimized device status information is compared to either non-anomalous or anomalous device status data in a latent space of the variational autoencoder. The latent space preferably includes an n-D point scatter plot and hidden vector values. The processor optimizes the device status information by generating a plurality of probabilistic models of the device status information and determining which of the plurality of models is optimal. A game theoretic optimization is applied to the plurality of models, and the best model is used to generate the n-D point scatter plot in latent space. An image gradient sobel edge detector preprocesses the device status information prior to optimization.

A machine vision-based tree recognition method and device are provided. The machine vision-based tree recognition method may include obtaining a top view image containing a tree and processing the top view image to obtain pixel position information of a tree center of the tree and tree radius information corresponding to the tree center in the top view image.

Embodiments of an obstacle distribution simulation method, device and terminal based on a probability graph are provided. The method can include: acquiring a plurality of point clouds of a plurality of frames; acquiring real labeling data of an acquisition vehicle at vehicle labeled positions, and acquiring data of a simulation position of the acquisition vehicle; determining the number of obstacles to be simulated at a position to be simulated; extracting real labeling data of the obstacles, and constructing a labeling data set; dividing the labeling data set into a plurality of grids and calculating occurrence probabilities of the plurality of obstacles; selecting the determined number of obstacles to be simulated according to the occurrence probabilities; and acquiring a position distribution of the selected obstacles to be simulated for the position to be simulated based on the real labeling data of the selected obstacles to be simulated.

A nail contour detecting device including a processor, wherein the processor obtains first feature point data of a first nail contour which is a nail contour detected from a first nail image obtained by imaging a nail of a finger or a toe, and second feature point data of a second nail contour which is a nail contour detected from a second nail image obtained by imaging a nail of the same finger or toe as the first nail image; and the processor obtains one nail contour based on the first feature point data and the second feature point data.

A method for generating a skeleton in an image of a cavity of an organ of a body includes receiving a map of the cavity, the map including surface voxels and interior voxels. A subset of the interior voxels is generated, of candidate locations to be on the skeleton. The subset is pruned by removing outlier candidate locations. Using a geometrical model including a statistical model, the candidate locations remaining in the pruned subset are spatially compressed. The compressed candidate locations are connected to produce one or more centerlines of the skeleton. At least the skeleton is displayed to user.

A method in a vehicle is disclosed. The method includes: detecting an object in image data; defining a bounding box that surrounds the object; matching the object to data points in a point cloud from a LiDAR system; determining three-dimensional (3-D) position values from the data points for pixels in the image data; applying statistical operations to the 3-D position values; determining from the statistical operations a nature (real or imitation) of the object; determining a size for the object based on the 3-D position values; determine a shape for the object based on the 3-D position values; recognizing a category for the object using object recognition techniques based on the determined size and shape; and notifying a vehicle motion control system of the size, shape, and category of the object when the nature of the object is real to allow for appropriate driving actions in the vehicle.

A method for acquiring traffic sign information includes acquiring an image of a scene comprising a traffic sign, the image being obtained by photographing the scene, using a photographing apparatus, acquiring first laser data of the scene, the first laser data being obtained by performing laser scanning on the scene, and the first laser data being of a plurality of first laser points, performing spatial clustering on the plurality of first laser points to obtain candidate point sets, acquiring a spatial distribution feature of respective laser points in each of the candidate point sets, determining at least one point set corresponding to the traffic sign in the candidate point sets, based on the spatial distribution feature, extracting image data of the traffic sign, from the image, using the at least one point set corresponding to the traffic sign, and extracting sign information of the traffic sign, from the image data.