A video co-shooting method, an apparatus, an electronic device, and a computer-readable medium are provided, which involve the field of video processing technology. The method includes: receiving a co-shooting request input by a user based on a first video; in response to the co-shooting request, turning on a video capture apparatus, and acquiring a second video through the video capture apparatus; and fusing the first video with the second video to obtain a target video. In the embodiments of the present disclosure, a video capture apparatus is turned on according to a co-shooting request input by a user based on a first video, a second video is acquired through the video capture apparatus, and the first video is fused with the second video, so as to obtain a target video.

Provided is a method for generating an image label, including: acquiring a partial image of a target image after acquiring the target image with a label to be generated; then, acquiring a plurality of features based on the target image and the partial image, wherein the plurality of features include a first feature of the target image and a second feature of the partial image; and finally, generating a first-type image label of the target image based on the first feature and the second feature.

The present disclosure relates to a PointEFF method for urban object classification with LiDAR point cloud data, and belongs to the field of LiDAR point cloud classification. The method comprises: point cloud data segmentation; End-to-end feature extraction layer construction; External feature fusion layer construction; and precision evaluation. The PointEFF method for urban object classification with LiDAR point cloud data fuses point cloud hand-crafted descriptors with End-to-end features obtained from a network at an up-sampling stage of a model by constructing an External Feature Fusion module, which improves a problem of local point cloud information loss caused by interpolation operation in the up-sampling process of domain feature pooling methods represented by PointNet and PointNet++, greatly improves classification precision of the model in complex ground features, especially in rough surface ground features, and is capable of being better applied to the classification of urban ground features with complex ground feature types.

In the video surveillance system of the present invention, Because the multi-channel surveillance videos are integrated into a virtual surveillance scene for panoramic viewing, it is possible to view surveillance videos from the plurality of channels at the same time, reduce the viewing time, and improve the efficiency. In addition, since the surveillance picture is not much different within the same second or even a few seconds, and in the present invention, one frame of image is extracted at the same time point for a video of the multi-channel surveillance videos, and a next frame of image is extracted after a predetermined time interval, instead of extracting all the images to have a view, and thus it is possible to improve the efficiency and not to miss important video information.

Implementations include receiving an input image including a face, processing the input image through a global transformation network to provide a set of global transformation parameters, applying the set of global transformation parameters to the input image to provide a globally transformed image, processing the globally transformed image through a global representation learning network to provide a set of global features, processing the set of global features through a part localization network to provide a set of part localization parameters, applying the set of part localization parameters to the globally transformed image to provide a locally transformed image, processing the locally transformed image through a part representation learning network to provide a set of local features, and outputting a label representing at least one attribute depicted in the input image based on fusing global feature(s) from the set of global features, and local feature(s) from the set of local features.

An image recognition device sets an overall observation region which surrounds a whole body of an object and partial observation regions which surround characteristic parts of the object respectively to locations in an image which are estimated to include captured images of the object. The device clips images in the overall observation region and the partial observation regions, and calculates similarity degrees between them and previously learned images on the basis of a combination of two image feature amounts. The device calculates an optimum ratio in combining the HOG feature amount and the color distribution feature amount individually for the regions. This ratio is determined by setting a weight parameter i for setting a weight used for combining the HOG feature amount and the color distribution feature amount to be included in a state vector and subjecting the result to complete search by a particle filter.

The present disclosure provides a method for training a neural network that extracts a feature of an image by using data related to a difference between image, and an apparatus using the same. A neural network training method performed by a computing device according to an exemplary embodiment of the present disclosure includes: acquiring a reference image photographed with a first setting with respect to an object and a first comparison image photographed with a second setting with respect to the object; acquiring feature data of the reference image from a first neural network trained by using the reference image; acquiring feature data of a first extract image from a second neural network, wherein the second neural network is trained by using the first extract image formed from data related to a difference between the reference image and the first comparison image; and training a third neural network by using the feature data of the reference image and the feature data of the first extracted image.

An instance segmentation method includes: performing feature extraction on an image via a neural network to output features at at least two different hierarchies; extracting region features corresponding to at least one instance candidate region in the image from the features at the at least two different hierarchies, and fusing region features corresponding to a same instance candidate region, to obtain a first fusion feature of each instance candidate region; and performing instance segmentation based on each first fusion feature, to obtain at least one of an instance segmentation result of the corresponding instance candidate region or an instance segmentation result of the image.

The subject disclosure presents systems and computer-implemented methods for assessing a risk of cancer recurrence in a patient based on a holistic integration of large amounts of prognostic information for said patient into a single comparative prognostic dataset. A risk classification system may be trained using the large amounts of information from a cohort of training slides from several patients, along with survival data for said patients. For example, a machine-learning-based binary classifier in the risk classification system may be trained using a set of granular image features computed from a plurality of slides corresponding to several cancer patients whose survival information is known and input into the system. The trained classifier may be used to classify image features from one or more test patients into a low-risk or high-risk group.

Mechanisms are provided to implement an automated echocardiograph measurement extraction system. The automated echocardiograph measurement extraction system receives medical imaging data comprising one or more medical images and inputs the one or more medical images into a deep learning network. The deep learning network automatically processes the one or more medical images to generate an extracted echocardiograph measurement vector output comprising one or more values for echocardiograph measurements extracted from the one or more medical images. The deep learning network outputs the extracted echocardiograph measurement vector output to a medical image viewer.