The present disclosure provides a table generating method and apparatus, an electronic device, a storage medium and a product. A specific implementation is: recognizing at least one table object in a to-be-recognized image and obtaining a table property respectively corresponding to the at least one table object, where the table property of any table object includes a cell property or a non-cell property; determining at least one target object with the cell property in the at least one table object; determining a cell region respectively corresponding to the at least one target object to obtain cell position information respectively corresponding to the at least one target object; generating a spreadsheet corresponding to the to-be-recognized image according to the cell position information respectively corresponding to the at least one target object.

A navigation system includes an imaging device to image a region including a traveling road, a map information acquisition unit to acquire map information, a position information acquisition unit to acquire position information on the mobile body, a traveling road conversion unit to convert images of the region into traveling road data, a traveling road combining unit to use a feature point of the traveling road to generate combined data in which traveling road data corresponding to the images are combined, a matching processing unit to match between the combined data and a traveling road to estimate a current position of the mobile body, a navigation information generation unit to generate navigation information, an image combining unit to generate an image in which the image of the region and the navigation information are combined, and a display unit to display the combined image.

Various embodiments utilize application-based processing parameters to dynamically configure a radar-based detection system based upon an operating context of an associated device. A first application with execution priority on a device dynamically configures the radar-based detection system to emit a radar field suitable for a first operating context associated with the first application. The first application can also dynamically configure processing parameters of the radar-based detection system, such as digital signal processing parameters and machine-learning parameters. In some cases, a second application assumes execution priority over the first application, and dynamically reconfigures the radar-based detection system to emit a radar field suitable to a second operating context associated with the second application. Alternately or additionally, the second application can dynamically reconfigure the processing parameters of the radar-based detection system based upon the second operating context of the second application.

A computer implemented method is proposed for classifying one or more embossed and/or engraved markings on a sidewall of a tyre into one or more classes comprising digital image data of the sidewall of the tyre. The method comprises generating a first image channel from a first portion of the digital image data relating to a corresponding first portion of the sidewall of the tyre. Generating the first image channel comprises performing histogram equalisation on the first portion of the digital image data to generate the first image channel. The method further comprises generating a first feature map using the first image channel and applying a first classifier to the first feature map to classify said embossed and/or engraved markings into one or more first classes.

A method for processing breast tissue image data includes processing the image data to generate a set of image slices collectively depicting the patient's breast; for each image slice, applying one or more filters associated with a plurality of multi-level feature modules, each configured to represent and recognize an assigned characteristic or feature of a high-dimensional object; generating at each multi-level feature module a feature map depicting regions of the image slice having the assigned feature; combining the feature maps generated from the plurality of multi-level feature modules into a combined image object map indicating a probability that the high-dimensional object is present at a particular location of the image slice; and creating a 2D synthesized image identifying one or more high-dimensional objects based at least in part on object maps generated for a plurality of image slices.

A method for neural network training, a method for image segmentation, an electronic device and a computer storage medium are provided. The method for neural network training includes: extracting, through a first neural network, a first feature of a first image and a second feature of a second image; fusing, through the first neural network, the first feature and the second feature to obtain a third feature; determining, through the first neural network, a first classification result of overlapped pixels in the first image and the second image according to the third feature; and training the first neural network according to the first classification result and labeled data corresponding to the overlapped pixels.

The present disclosure relates to a method, device, computer readable media, and electronic devices for identifying a traffic light signal from an image. The method for identifying a traffic light signal from an image includes extracting, based on a deep neural network, multiple layers of first feature maps corresponding to different layers of the deep neural network from the image. The method includes selecting at least two layers of the first feature maps having different scales from the multiple layers of the first feature maps. The method includes inputting the at least two layers of the first feature maps to a convolution layer having a convolution kernel matching a shape of a traffic light to obtain a second feature map. The method includes obtaining a detection result of the traffic light signal based on the second feature map.

A picture processing method, apparatus and a storage medium are provided. In the method, a first image comprising a first object and a second image comprising a first garment are acquired; a first fused feature vector is obtained by inputting the first image and the second image to a first model, the first fused feature vector represents a fused feature of the first image and the second image; a second fused feature vector is acquired, the second fused feature vector represents a fused feature of a third image and a fourth image, the third image includes a second object, and the fourth image is an image extracted from the third image and comprises a second garment; and it is determined whether the first object and the second object are a same object according to a target similarity between the first fused feature vector and the second fused feature vector.

Provided is a video-based method and system for accurately estimating heart rate and facial blood volume distribution, and the method mainly comprises the following steps: firstly, carrying out face detection of video frame containing human face, and extracting face image sequence and face key position points sequence in time dimension; secondly, compressing these sequence of face image and face key position points to obtain the facial signals in time dimension; thirdly, estimating facial blood volume distribution by facial signals mentioned in third step; finally, estimating heart rate values by using model based on deep learning technology and the spectrum analysis method respectively, then fusing the estimation results by Kalman filter to promote the accuracy of heart rate estimation.

An image information processing method is provided. The method includes obtaining a to-be-processed image, and preprocessing the to-be-processed image to obtain target image information, extracting facial feature point information from the target image information, and integrating the facial feature point information to a target region image to obtain an integrated image, the target region image being part of a playback image in a preset playback interval of a video, and displaying the integrated image on the video in response to detecting that the video is played to the preset playback interval.