A smart, human-centered technique that uses artificial intelligence and mixed reality to accelerate essential tasks of the inspectors such as defect measurement, condition assessment and data processing. For example, a bridge inspector can analyze some remote cracks located on a concrete pier, estimate their dimensional properties and perform condition assessment in real-time. The inspector can intervene in any step of the analysis/assessment and correct the operations of the artificial intelligence. Thereby, the inspector and the artificial intelligence will collaborate/communicate for improved visual inspection. This collective intelligence framework can be integrated in a mixed reality supported see-through headset or a hand-held device with the availability of sufficient hardware and sensors. Consequently, the methods reduce the inspection time and associated labor costs while ensuring reliable and objective infrastructure evaluation. Such methods offer contributions to infrastructure inspection, maintenance, management practice, and safety for the inspection personnel.

The present teaching relates to system, method, medium for in-situ perception in an autonomous driving vehicle. A plurality of types of sensor data are received, which are acquired by a plurality of types of sensors deployed on the vehicle to provide information about surrounding of the vehicle. Based on at least one model, one or more surrounding items are tracked from a first of the plurality of types of sensor data acquired by a first type sensors. At least some of the tracked items are automatically labeled via cross validation and are used to locally adapt, on-the-fly, the at least one model. Model update information is received which from a model update center, which is derived based on the labeled at least some items. The at least one model is updated using the model update information.

Exemplary embodiments of the present disclosure are directed towards system and method for generating scores and assigning quality index to videos on digital platform, comprising computing device that comprises video uploading module configured to allow user to record and upload videos on computing device, thereby transferring user uploaded videos to server over network. Server comprising video evaluating module configured to receive user uploaded videos and identifying video frames, thereby identifying different criteria. Video evaluating module configured to evaluate different criteria assigning scores to video frames and computing plurality of metrics of video frames based on assigned scores, then calculates mean and median values of metrics and assign mean and median values of video frame vectors, and combine video frame vectors of each video frame to obtain final video vector. Video evaluating module configured to assign weight to each value of final video vector to identify video quality index.

An approach is provided for dynamic obstacle data in a collision probability map. The approach, for example, involves monitoring a flight of an aerial vehicle through a three-dimensional (3D) space that is partitioned into 3D shapes of varying resolutions. The approach also involves detecting an entry of the aerial vehicle into one 3D shape of the plurality of 3D shapes. The approach further involves, on detecting an exit of the aerial vehicle form the one 3D shape, recording a 3D shape identifier (ID) of the one 3D shape and at least one of a first timestamp indicating the entry, a second timestamp indicating the exit, a duration of stay in the one 3D shape, dimensions of the aerial vehicle, or a combination thereof as a dynamic obstacle observation record. The approach further involves transmitting the dynamic obstacle observation record to another device (e.g., a server for creating the collision probability map).

A smart, human-centered technique that uses artificial intelligence and mixed reality to accelerate essential tasks of the inspectors such as defect measurement, condition assessment and data processing. For example, a bridge inspector can analyze some remote cracks located on a concrete pier, estimate their dimensional properties and perform condition assessment in real-time. The inspector can intervene in any step of the analysis/assessment and correct the operations of the artificial intelligence. Thereby, the inspector and the artificial intelligence will collaborate/communicate for improved visual inspection. This collective intelligence framework can be integrated in a mixed reality supported see-through headset or a hand-held device with the availability of sufficient hardware and sensors. Consequently, the methods reduce the inspection time and associated labor costs while ensuring reliable and objective infrastructure evaluation. Such methods offer contributions to infrastructure inspection, maintenance, management practice, and safety for the inspection personnel.

A method may include obtaining an infrared image of an object and determining a difference of Gaussian image that represents features of the infrared image that have spatial frequencies within a spatial frequency range defined by a first Gaussian operator and a second Gaussian operator. The method may also include identifying one or more blob regions within the difference of Gaussian image. Each blob region of the one or more blob regions includes a region of connected pixels in the difference of Gaussian image. The method may further include, based on identifying the one or more blob regions within the difference of Gaussian image, determining that the infrared image represents the object illuminated by a pattern projected onto the object by an infrared projector.

Disclosed is an approach of on-device partial recognition that includes performing partial recognition on an image of a document captured by a mobile device to detect and/or recognize a specific area (e.g., barcodes, non-relevant text, etc.) and filling the recognized area with a solid color. Because the solid color area has a maximum compression ratio, this approach can lead to image size reduction and increased network throughput for client-server based data recognition where further processing such as advanced data extraction is performed at the server side. The approach can be enforced with neural network algorithms to exclude non-relevant information (e.g., logos, phrases, words, etc.).

An all-optical Diffractive Deep Neural Network (D.sup.2NN) architecture learns to implement various functions or tasks after deep learning-based design of the passive diffractive or reflective substrate layers that work collectively to perform the desired function or task. This architecture was successfully confirmed experimentally by creating 3D-printed D.sup.2NNs that learned to implement handwritten classifications and lens function at the terahertz spectrum. This all-optical deep learning framework can perform, at the speed of light, various complex functions and tasks that computer-based neural networks can implement, and will find applications in all-optical image analysis, feature detection and object classification, also enabling new camera designs and optical components that can learn to perform unique tasks using D.sup.2NNs. In alternative embodiments, the all-optical D.sup.2NN is used as a front-end in conjunction with a trained, digital neural network back-end.

A method and a system for distributing load in a network including a requesting node and a set of external processing nodes are disclosed. The method comprises sending an Internet Control Message Protocol (ICMP) message to each external processing node of the set of external processing nodes. The method further comprises identifying one or more external processing nodes from the set of external processing nodes as responding nodes based on a receipt of response to a corresponding ICMP message thereto. The method further comprises selecting an external processing node from the identified responding nodes as a most suitable external processing node having capability to fulfill a video processing request submitted by the requesting node. The method further comprises sending a transmission package from the requesting node to the selected external processing node for processing thereof. The method further comprises receiving a binary response from the selected external processing node.

A system and method of face recognition comprising multiple phases implemented in a parallel architecture. The first phase is a normalization phase whereby a captured image is normalized to the same size, orientation, and illumination of stored images in a preexisting database. The second phase is a feature extraction/distance matrix phase where a distance matrix is generated for the captured image. In a coarse recognition phase, the generated distance matrix is compared with distance matrices in the database using Euclidean distance matches to create candidate lists, and in a detailed recognition phase, multiple face recognition algorithms are applied to the candidate lists to produce a final result. The distance matrices in the normalized database may be broken into parallel lists for parallelization in the feature extraction/distance matrix phase, and the candidate lists may also be grouped according to a dissimilarity algorithm for parallel processing in the detailed recognition phase.