A method includes converting time-series data from a plurality of prognostic and health monitoring (PHM) sensors into frequency domain data. One or more portions of the frequency domain data are labeled as indicative of one or more target modes to form labeled target data. A model including a deep neural network is applied to the labeled target data. A result of applying the model is classified as one or more discretized PHM training indicators associated with the one or more target modes. The one or more discretized PHM training indicators are output.

An information processing apparatus, a data generation method, and a program capable of obtaining useful information about a person from video data are provided. An information processing apparatus (1) according to an example embodiment includes a base-information acquisition unit (2) that acquires a plurality of types of pieces of base information based on video data in which at least one person is shown, the pieces of base information being pieces of information used to monitor a person, and a base-information integration unit (3) that generates integrated information by integrating, among the plurality of pieces of base information, those that satisfy a predetermined relation as information of one and the same person.

The present disclosure provides a method, an apparatus, an electronic device, and a storage medium for recommending multimedia resource, and relates to the field of machine learning. The method includes: acquiring features of the multimedia resource based on a convolutional neural network, where the convolutional neural network comprises N convolutional layers, where N is a positive integer; determining user interest information based on an identifier of a recommended user, where the user interest information is corresponding to the feature of each convolutional layer; determining a first feature matrix based on the convolution of convolution kernel and the feature, where the convolution kernel comprises the user interest information; generating user preference data based on the first feature matrix; and recommending the multimedia resource to the recommended user based on the N generated user preference data.

A method, a computer program code, an apparatus for processing environmental data of an environment of a vehicle, a driver assistance system, which makes use of such a method or apparatus, and an autonomous or semi-autonomous vehicle comprising such a driver assistance system. Depth data of the environment of the vehicle is received from at least one depth sensor of the vehicle. Furthermore, thermal data of the environment of the vehicle is received from at least one thermal sensor of the vehicle. The depth data and the thermal data are then fused to generate fused environmental data.

Various implementations disclosed herein include devices, systems, and methods that identify a gesture based on event camera data and frame-based camera data (e.g., for a CGR environment). In some implementations at an electronic device having a processor, event camera data is obtained corresponding to light (e.g., IR light) reflected from a physical environment and received at an event camera. In some implementations, frame-based camera data is obtained corresponding to light (e.g., visible light) reflected from the physical environment and received at a frame-based camera. In some implementations, a subset of the event camera data is identified based on the frame-based camera data, and a gesture (e.g., of a person in the physical environment) is identified based on the subset of event camera data. In some implementations, a path (e.g., of a hand) by tracking a grouping of blocks of event camera events in the subset of event camera data.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for estimating a 3-D pose of an object of interest from image and point cloud data. In one aspect, a method includes obtaining an image of an environment; obtaining a point cloud of a three-dimensional region of the environment; generating a fused representation of the image and the point cloud; and processing the fused representation using a pose estimation neural network and in accordance with current values of a plurality of pose estimation network parameters to generate a pose estimation network output that specifies, for each of multiple keypoints, a respective estimated position in the three-dimensional region of the environment.

A method of fusing frame based image sensors (FBIS) images with dynamic vision sensor (DVS) event data includes concatenating a plurality of image tensors into a single image input tensor; concatenating a plurality of event tensors into a single event input tensor; concatenating the event input tensor and the image input tensor wherein a single input tensor containing data from both the image input tensor and the event input tensor is generated; processing the single input tensor with a fully convolutional neural network (FCNN), wherein in a contracting path of the FCNN, a number of channels is increased, and in an expansion path of the FCNN, the number of channels is decreased; and combining channels wherein an output image tensor is generated with a reduced number of channels.

Repetitive activities can be captured in audio video content. The AV content can be processed in order to predict the number of repetitive activities present in the AV content. The accuracy of the predicted number may be improved, especially for AV content with challenging conditions, by basing the predictions on both the audio and video portions of the AV content.

A method of predicting one or more road segment attributes corresponding to a road segment in a geographical area, the method including: providing trajectory data and satellite image of the geographical area; calculating one or more image channels based on the trajectory data; and using at least one processor, classifying the road segment based on the one or more image channels and the satellite image using a trained classifier into prediction probabilities of the road attributes A data processing system including one or more processors configured to carry out a the method of predicting road attributes. A computer executable code including instructions for predicting one or more road segment attributes according to the method.

The present application relates to an image processing method and system. The method may include: acquiring a sequence of input images containing a target object; and performing multi-resolution fusion on the sequence of input images to generate a single fused image, where pixels of the fused image may include a pixel at a corresponding position of an input image in the sequence of input images, and each pixel of the fused image containing the target object may include a pixel at a corresponding position of an input image in the sequence of input images in which part of the target object is focused.