An approach for determining similar text documents. The approach can calculate a first set of vectors for a first cluster of text documents and a first comparison vector for a text document of interest. The approach can select a subset of text documents from the cluster of text documents based on comparing the vectors from the first set of vectors to the first comparison vector and picking a predetermined number of closest comparison text documents. The approach can calculate a second set of vectors for the subset of documents and second comparison vector for the document of interest. The approach can generate similarity ratings for the subset of documents based on pairwise comparisons of the second comparison vector and the second set of vectors. The approach can generate a ranked list of the second cluster of text documents based on the similarity ratings.

An example method includes receiving data to be provided to an application using a scoring model for calculating a score, determining that the data is incompatible with a current feature set of the scoring model applied by the application, receiving a next best model of features in response to the determining that the data is incompatible with the current feature set, executing the application to calculate the score with the data and the features of the next best model, and generating an output in accordance with the score.

The present disclosure relates to computer implemented methods and systems for processing of workorder evidence. The workorder has associated objects of interest and metadata describing at least one workorder attribute. Workorder evidence to be processed comprises an image. The image is validated to assess whether the image is associated with the workorder. Validation comprises one or both of comparing image attributes to workorder attributes to detect a match, and comparing the image to previous images to ensure that the image is not a match for a previous image. The system or method detects whether an object of interest is depicted in the image using an object detection algorithm. The workorder evidence is classified depending on whether the image is detected as valid and the object or interest is detected in the image.

A method for detecting objects and labeling the objects with distances in an image includes steps of: obtaining a thermal image from a thermal camera, an RGB image from an RGB camera, and radar information from an mmWave radar; adjusting the thermal image based on the RGB image to generate an adjusted thermal image, and generating a fused image based on the RGB image and the adjusted thermal image; generating a second fused image based on the fused image and the radar information; detecting objects in the images, and generating, based on the fused image, another fused image including bounding boxes marking the objects; and determining motion parameters of the objects.

Systems and methods for image-based perception. The methods comprise: obtaining, by a computing device, images captured by a plurality of cameras with overlapping fields of view; generating, by the computing device, spatial feature maps indicating locations of features in the images; defining, by the computing device, predicted cuboids at each location of an object in the images based on the spatial feature maps; and assigning, by the computing device, at least two cuboids of said predicted cuboids to a given object when predictions from images captured by separate cameras of the plurality of cameras should be associated with a same detected object.

A system and method for better matching of two digital fingerprints when the two digital fingerprints are acquired under different conditions that uses the set of points of interest of the first and second digital fingerprints to perform the matching. A transform pyramiding process is performed using the first and second set of points of interest and a hierarchy of transforms to determine if the first and second set of points of interest are true matches despite the different conditions.

A method of processing an image, a method of training a multi-task processing model, and an electronic device, which relate to a field of an automatic driving technology, in particular to a field of high-definition map technology. The method of processing an image includes: processing a to-be-processed image to obtain a feature point of the to-be-processed image, a feature point descriptor map of the to-be-processed image, and a dense descriptor map of the to-be-processed image; determining a pair of matched feature points between the to-be-processed image and a reference image based on the feature point and the feature point descriptor map; and determining a pair of matched pixels between the to-be-processed image and the reference image based on the dense descriptor map.

An object positioning system and an object positioning method are provided. The object positioning system includes a sensing device, a storage device and a processing device. The sensing device collects point cloud data obtained from a scene including a target object. The processing device inputs surrounding area data centered on a key point and a preset feature descriptor to a neural network to calculate a scene feature descriptor of the scene. The processing device performs feature matching between the scene feature descriptor and the preset feature descriptor, and calculates a position of the target object in an actual space. The invention utilizes the feature extraction capability of the neural network to effectively improve the accuracy and stability of target object identification and positioning.

An image processing apparatus acquires a captured image, sets a plurality of parameters for generating a matching model, generates a matching model for detecting a target object based on the acquired captured image and the set plurality of parameters, and identifies an item of a common parameter for common use by a plurality of target objects and an item of an individual parameter to be determined for each of the plurality of target objects from the plurality of parameters in a case where the plurality of parameters is set and a plurality of matching models is generated for each of the plurality of target objects.

An information processing system that acquires image data captured by an image capturing device; identifies a density of distribution of a plurality of feature points in the acquired image data; and controls a display to display guidance information based on the density of the distribution of the plurality of feature points.