Abstract: An image processing method and apparatus, and a computer readable medium are provided. The method includes that includes: a first reference surface on which a virtual object is placed is determined based on an image collected by an image collection device; a to-be-placed virtual object and pose information of the image collection device relative to the first reference surface are acquired; a display size of the to-be-placed virtual object is determined based on the pose information; and the to-be-placed virtual object is rendered on the image according to the display size.

Abstract: Embodiments of the present disclosure disclose forward collision control methods and apparatuses, electronic devices, programs, and media, where the forward collision control method includes: detecting a forward suspected collision object on a road where a current traveling object is located on the basis of a neural network; predicting the collision time between the current traveling object and the suspected collision object; and performing forward collision control on the current traveling object according to the collision time, where the forward collision control includes forward collision warning and/or driving control.

Abstract: The present disclosure relates to an image processing method and apparatus, an electronic device and a storage medium. The method includes performing feature extraction on an image to be processed to obtain a first feature map of the image to be processed and performing weight prediction on the first feature map to obtain a weight feature map of the first feature map. The weight feature map includes weight values of feature points in the first feature map. The method further includes performing feature value adjustment on the feature points in the first feature map based on the weight feature map to obtain a second feature map and determining a processing result of the image to be processed according to the second feature map. Embodiments of the present disclosure may improve the image processing accuracy.

Abstract: The present disclosure relates to a distance measurement method, an intelligent control method and apparatus, an electronic device, and a storage medium. The method includes: obtaining a detection bounding box of a target object in an image photographed by a current object; determining at least one distance measurement point according to the shape of the detection bounding box; and determining a distance between the target object and the current object based on the distance measurement point.

Abstract: A face recognition method and apparatus, and a storage medium are provided. The method includes: performing attribute feature extraction on an image to be processed including a target object to obtain N attribute features of the target object, N being an integer greater than 1; performing attention feature extraction on the image to be processed based on an attention mechanism to obtain N attention features of the target object; clustering the N attention features to obtain M clustered attention features, M being a positive integer and M<N; and determining the face recognition result of the target object according to the N attribute features and the M clustered attention features.

Abstract: A method for detecting a blocking state of an image acquisition device, an electronic device, and a computer storage medium are provided. The method includes: an image acquired by an image acquisition device is obtained; and it is determined, according to the acquired image, whether the image acquisition device is in a blocked state.

Abstract: An image lighting method includes: determining a convolutional neural network corresponding to a lighting operation type of an initial image; obtaining local feature information and global feature information of initial image according to convolutional neural network; obtaining fusion feature information of initial image according to convolutional neural network based on local feature information and global feature information; obtaining a maximum pooling result map and a minimum pooling result map according to convolutional neural network based on a luminance component map of initial image; obtaining a bilateral grid matrix of initial image based on fusion feature information, maximum pooling result map, and minimum pooling result map; and performing affine transformation on initial image according to bilateral grid matrix to obtain a target image, the target image being an image obtained after lighting initial image according to lighting operation type.

Abstract: A method and apparatus for depth estimation of a monocular image, and a storage medium are provided. The method includes: obtaining, through a depth estimation neural network, a global feature of a monocular image according to absolute features of preset regions and relative features among the preset regions in the monocular image; and obtaining a predicted depth map of the monocular image according to the global feature, and the absolute features of preset regions and relative features among the preset regions in the monocular image.

Abstract: A distribution method includes: distributing, when multiple feature maps exist in an image processing model, to each feature map a neuron through which the feature map passes; filtrating, according to importance of neurons of multiple convolution layers in an image processing model, the neurons to obtain a first result; collecting, according to a position attribute of the each neuron in the first result, statistics on a scale of the feature map corresponding to each neuron to obtain a distribution relationship; the distribution relationship indicates a correspondence between the each feature map and the neuron through which the feature map passes; and distributing, according to the distribution relationship, to the each feature map the neuron through which the feature map passes.

Abstract: Methods, systems, apparatus, and non-transitory computer readable storage media for image processing are provided. In one aspect, an image processing method includes: generating a garment deformation template image and a first human body template image based on a first semantic segmentation image of a human body in a first image and a target garment image, generating a target garment deformation image by performing deformation on the target garment image based on the garment deformation template image, obtaining a second human body template image by adjusting the first human body template image based on a second semantic segmentation image of the human body in the first image and the garment deformation template image, and transforming the first image into a second image including the human body wearing a target garment based on the target garment deformation image, the second human body template image, and the garment deformation template image.

Abstract: A method and apparatus for detecting an on-duty state of a driver, a device, and a computer storage medium are provided. The method includes: determining that a vehicle-mounted image acquisition device is not blocked; extracting a face image from an image acquired by the vehicle-mounted image acquisition device when a vehicle is in a starting state, where the vehicle-mounted image acquisition device is mounted on the vehicle and is at least configured to acquire an image of a driving position region of the vehicle; and in response to the case that no face image is extracted from the acquired image, determining that the driver is off duty.

Abstract: The present disclosure relates to a data processing method and a related device. The method comprises the following steps of: acquiring a point cloud to be processed which comprises at least one object to be located; determining at least two target areas in the point cloud to be processed, adjusting normal vectors of points in the target areas to significant normal vectors according to initial normal vectors of the points in the target areas, any two of the at least two target areas being different; dividing the point cloud to be processed according to the significant normal vectors of the target areas to acquire at least one divided area; and acquiring a three-dimensional position of a reference point of the object to be positioned according to three-dimensional positions of the point in the at least one divided area.

Abstract: A method for image generation, an electronic device and a storage medium are provided. The method includes: a first image of a target object is generated according to a line image of the target object; structure extraction is performed on the first image to obtain at least one of texture information or orientation information of the first image; and a second image of the target object is generated according to the at least one of the texture information or the orientation information.

Abstract: A dual-view angle image calibration method includes: performing feature matching on a first image pair to obtain a first feature point pair set, the first image pair including two images respectively photographed corresponding to two different view angles of a same scene; obtaining multiple different first fundamental matrices of the first image pair at least according to the first feature point pair set, and obtaining first image deformation information indicating relative deformation between the first image pair after mapping transformation is performed through the first fundamental matrices and the first image pair before the mapping transformation is performed; determining a first optimization fundamental matrix from the multiple first fundamental matrices at least according to the first image deformation information; and calibrating the first image pair according to the first optimization fundamental matrix.

Abstract: A method and an apparatus for identity verification, an electronic device, a computer program, and a storage medium include: obtaining a first image of a document, where the first image contains a first face image; obtaining a second image containing a face of a to-be-verified person; performing face comparison on the first image and the second image to obtain a first comparison result; and obtaining an identity verification result according to the first comparison result.

Abstract: A method for processing audio and video information includes: audio information and video information of an audio and video file are acquired; feature fusion is performed on a spectrum feature of the audio information and a video feature of the video information based on time information of the audio information and time information of the video information to obtain at least one fused feature; it is determined, based on the at least one fused feature, whether the audio information and the video information are synchronous.

Abstract: The present disclosure relates to an image retrieval method and apparatus. The method includes: obtaining an image group including a query image and at least one candidate image; calculating an intermediate similarity between any two images in the image group; performing multi-scale feature fusion based on the intermediate similarity between the any two images to obtain a final similarity between the query image and each candidate image; and determining a target image corresponding to the query image from each candidate image based on the final similarity. The present disclosure can improve precision and robustness of image retrieval.

Abstract: The present disclosure relates to a collision control method and apparatus, an electronic device, and a storage medium. The method includes: detecting a target object in an image photographed by a traveling object; determining a forward collision hazard region of the traveling object; and executing collision control on the traveling object based on a relative relationship between the target object and the forward collision hazard region, where the collision control includes collision warning and/or driving control.

Abstract: A depth estimation method includes: taking a single image as a first image in binocular images, and obtaining a second image in the binocular images based on the first image via a first neural network; and obtaining depth information corresponding to the first image via a second neural network by performing binocular stereo matching on the first image and the second image.