An information processing apparatus operable to perform computation processing in a neural network comprises a coefficient storage unit configured to store filter coefficients of the neural network, a feature storage unit configured to store feature data, a storage control unit configured to store in the coefficient storage unit a part of previously obtained feature data as template feature data, a convolution operation unit configured to compute new feature data by a convolution operation between feature data stored in the feature storage unit and filter coefficients stored in the coefficient storage unit, and compute, by a convolution operation between feature data stored in the feature storage unit and the template feature data stored in the coefficient storage unit, correlation data between the feature data stored in the feature storage unit and the template feature data.

Technologies and techniques for supporting an autonomous vehicle wherein objects in surroundings of the vehicle are captured by a sensor system, and wherein objects are identified and recognized by object recognition from captured data relating to the surroundings. When an unknown object is present in the surroundings, the unknown object if found in at least one database, typical properties of the unknown object are determined on the basis of the search result, a recommended course of action is derived for the autonomous vehicle on the basis of the typical properties of the unknown object, and the derived recommended course of action are provided to the vehicle.

Certain aspects involve evaluating modeling algorithms whose outputs can impact machine-implemented operating environments. For instance, a computing system generates, from a comparison of a set of estimated attribute values of an attribute to a set of validation attribute values of the attribute, a discretized evaluation dataset with data values in multiple categories. The computing system computes, for a modeling algorithm used to generate the estimated attribute values, an evaluation metric. The computing system provides a host computing system with access to the evaluation metric, one or more modeling outputs generated with the modeling algorithm, or both. Providing one or more of these outputs to the host computing system can facilitate modifying one or more machine-implemented operations.

Disclosed are a method and apparatus for processing an image, an electronic device and a storage medium. A specific implementation comprises: acquiring a matching association relationship of a feature point in each to-be-modeled image frame in a to-be-modeled image frame set, a plurality of to-be-modeled image frames in the to-be-modeled image frame set belonging to at least two different to-be-modeled image sequences; determining a first feature point set of the each to-be-modeled image frame based on the matching association relationship, the first feature point set including a first feature point, and the first feature point matching a corresponding feature point in a to-be-modeled image frame in a different to-be-modeled image sequence; and selecting, based on a number of the first feature point in the first feature point set in the each to-be-modeled image frame, a to-be-modeled image frame from the to-be-modeled image frame set for a three-dimensional reconstruction.

Described are methods and systems for calibrating simulation models to generate digital twins for physical entities. In some embodiments, a method includes receiving a plurality of datasets for a plurality of corresponding physical entities. A calibration request is enqueued to a calibration requests queue for each received dataset and includes information indicating a dataset and a corresponding physical entity. A plurality of calibration engines and a plurality of corresponding simulation clusters for generating a plurality of calibration results for a plurality of calibration requests dequeued from the calibration requests queue can be deployed. Each calibration result is enqueued to a calibration results queue as the plurality of calibration engines generates the calibration result and a plurality of calibration results dequeued from the calibration results queue in association with the plurality of corresponding physical entities can be stored as information used to generate a plurality of corresponding digital twins.

In some implementations, a service qualification system may receive, from a user device, a set of images that depict a window. The service qualification system may perform an image-based analysis of the set of images to determine a reflectivity score associated with the window. The reflectivity score may be indicative of a quality associated with a signal of the service being received through the window. The service qualification system may determine, based on the reflectivity score, a service qualification metric that is indicative of a capability of receiving the service within the unit. The service qualification system may perform an action associated with the service qualification metric.

The present application relates to an image-fusion method and apparatus, a computing and processing device and a storage medium. The method includes: based on a same one target scene, acquiring a plurality of exposed images of different exposure degrees; acquiring a first exposed-image fusion-weight diagram corresponding to each of the exposed images, wherein the first exposed-image fusion-weight diagram contains fusion weights corresponding to pixel points of the exposed image; acquiring a region area of each of overexposed regions in each of the exposed images; for each of the exposed images, by using the region area of each of the overexposed regions in the exposed image, performing smoothing filtering to the first exposed-image fusion-weight diagram corresponding to the exposed image, to obtain a second exposed-image fusion-weight diagram corresponding to the exposed image; and according to each of the second exposed-image fusion-weight diagrams, performing image-fusion processing to the plurality of exposed images, to obtain a fused image. Accordingly, the present application can balance the characteristics of the different overexposed regions, and prevent the losing of the details of the small overexposed regions, to enable the obtained fused image to be more realistic.

The disclosure relates to a method including: capturing a sequence of images of a scene with a camera at different focus positions according to a predetermined focus schedule that specifies a chronological sequence of focus positions of the camera, extracting image features of captured images, after having extracted and stored image features from said captured images, processing a captured image whose image features have not yet been extracted, said processing comprising extracting image features from the currently processed image and storing the extracted image features, said processing further comprising aligning image features stored from the previously captured images with the image features of the currently processed image, and generating a multi-dimensional tensor representing the image features of the processed images aligned to the image features of the currently processed image, and generating a two-dimensional depth map using the focus positions in the predetermined focus schedule and the generated multi-dimensional tensor.

The present invention provides a method for correcting an anomalous pixel and an apparatus. The method for correcting the anomalous pixel comprises: calculating a matching index of pixels in a first point cloud map and a second point cloud map; finding out an anomalous pixel based on the matching index of the pixels and a correction threshold; and calculating a correction column difference, and correcting the anomalous pixel based on the correction column difference and the matching index of the pixels. The anomalous pixel in the point cloud map is retained and corrected, thus the pixel integrity of an image is guaranteed; and a mode for correcting the anomalous pixel is simple, requires fewer calculating steps, and exhibits high anomalous pixel correction efficiency. The apparatus provided by the present invention comprises a first camera, a second camera, an image processing unit, an indexing unit, a calculating unit, a judging unit, and a correction execution unit, and is used for improving the efficiency of correcting an anomalous pixel in an image.

A system and a method for object detection using augmented training dataset. The system includes a computing device, which is configured to: provide a two-dimensional (2D) image, extract feature vectors and estimate depths of 2D image pixels, generate a point cloud using the feature vectors and the depths, and project the point cloud using a new camera pose to obtain a projected image. The 2D image has a bounding box enclosing an object and labeled with the object. Each pixel within the bounding box is named bounding box pixel, each point in the point cloud corresponding to the bounding box pixel is named bounding box point, each image pixel in the projected image corresponding to the bounding box point is named projected bounding box pixel, and a projected bounding box is defined using the projected bounding box pixels and labeled with the object.