Abstract: A method and apparatus for estimating the depth of a binocular image, a device, a program, and a medium are provided. The method includes: inputting a to-be-processed binocular image into a predetermined neural network to obtain a parallax image and a confidence score image thereof, where a discrete parameter of each pixel in a preset distribution in the parallax image has a negative correlation with a confidence score of the pixel; and obtaining a depth image of the to-be-processed binocular image according to the parallax image.

Abstract: An image processing method and apparatus, an electronic device, and a storage medium are provided. The method includes: determining a first image and first location information of a target region according to a reference image; cropping the target region to obtain a cropped image; performing noise reduction on the cropped image, and determining second location information of a face region and third location information of a body region; determining fourth location information of the face region and fifth location information of the body region in the first image according to the first location information, the second location information, and the third location information; and processing the first image according to the fourth location information and the fifth location information.

Abstract: A face image processing method includes: performing face detection on an image to be processed, and obtaining at least one face region image included in the image to be processed and face attribute information in the at least one face region image; and for the at least one face region image, processing an image corresponding to a first region and/or an image corresponding to a second region in the face region image at least according to the face attribute information in the face region image, wherein the first region is a skin region, and the second region includes at least a non-skin region.

Abstract: A method for image processing includes: performing human shape detection for a target image to obtain a human shape detection result, the target image being acquired in a present scene in real time; determining a region of interest in the target image according to the human shape detection result of the target image; and determining, based on brightness distribution in the region of interest, a target parameter value for image acquisition in the present scene.

Abstract: A method and an apparatus for verifying an identity document, an electronic device, and a storage medium include: collecting an identity document image of a current identity document by means of a first camera; recognizing characters in the identity document image to obtain first identity document data; obtaining second identity document data of the identity document by means of an identity document reader; and verifying authenticity of the identity document according to the first identity document data and the second identity document data.

Abstract: An image retrieval method includes: respectively performing a dimension reduction operation on convolutional layer features of an image to be retrieved to obtain dimension-reduced features; clustering the dimension-reduced features to obtain a plurality of clustering features; performing feature fusion on the plurality of clustering features to obtain a global feature; and retrieving, on the basis of the global feature, the image to be retrieved from a database.

Abstract: A face image processing method includes: performing face detection on an image to be processed, and obtaining at least one face region image included in the image to be processed and face attribute information in the at least one face region image; and for the at least one face region image, processing an image corresponding to a first region and/or an image corresponding to a second region in the face region image at least according to the face attribute information in the face region image, wherein the first region is a skin region, and the second region includes at least a non-skin region.

Abstract: The present disclosure relates to a target object tracking method and apparatus, an electronic device, and a storage medium. The method includes: obtaining a first reference image of a target object; determining time information and location information of the target object in an image to be analyzed according to the first reference image, the image to be analyzed including the time information and the location information; determining a trajectory of the target object according to the time information and the location information of the target object; and generating tracking information for tracking the target object according to the trajectory of the target object. Embodiments of the present disclosure obtain highly-accurate tracking information of the target object according to the trajectory of the target object determined in the image to be analyzed by using the first reference image of the target object, such that the success rate of target object tracking is improved.

Abstract: A method for determining face image quality includes: obtaining pose angle information and/or size information of a face in an image; and obtaining quality information of the face in the image on the basis of the pose angle information and/or the size information of the face.

Abstract: An image clustering method and apparatus, an electronic device, and a storage medium are provided. The method includes: obtaining a face feature and an attribute feature of each image in an image set; performing clustering processing on the image set by using a similarity of the face feature of each image in the image set to obtain at least one first class group, images included in each first class group being predicted images including a same object; determining a similarity matrix for each first class group by using the face features and the attribute features of the images in each first class group; and re-clustering the images in each first class group based on the similarity matrix corresponding to each first class group and the face features of the images in each first class group to obtain at least one second class group, images in the second class group being optimized predicted images having a same object.

Abstract: A method for recommending collocating dress includes: obtaining a first dress image from an input image; obtaining a first dress feature of the first dress image; obtaining, based on the first dress feature, a second dress feature matching the first dress feature from a collocation database, the second dress feature being a dress feature of a second dress image; and obtaining, based on the second dress feature, a dress image identical to and/or similar to the second dress image from a recommendation database as a collocating dress image. Based on the method in the embodiments, at least one collocation recommendation is provided for a dress image, and the recommendation result is more objective and is more in accordance with user demands.

Abstract: The present disclosure relates to an image processing method and apparatus. The method comprises: performing face and body feature extraction on an image to be processed to obtain image features including face features and/or body features, and the image to be processed comprises a first image and a second image; performing a face clustering operation, according to the face features extracted from the first image, to obtain a face clustering result; performing a body clustering operation on the second image to obtain a body clustering result, according to the face clustering result and the body features extracted from the first and second images, no face feature has been extracted from the second image; and obtaining, according to the face clustering result and the body clustering result, a clustering result for the image to be processed. The method can improve the recall rate while ensuring the accuracy of the clustering result.

Abstract: The present disclosure relates to an image processing method and apparatus, an electronic device and a storage medium. The method comprises: extracting features from a to-be-processed image to so as to obtain a first feature of the image; determining the image category of the image according to the first feature and category center features of multiple reference image categories in a feature library; and in a case where the image category of the image is the first category of the multiple reference image categories, updating the category center feature of the first category according to the first feature and multiple pieces of feature information of the first category in the feature library. The method may improve the speed and accuracy of image retrieval.

Abstract: Target matching method and apparatus, electronic device, and storage medium, including: extracting feature vector of each frame in query image sequence and feature vector of each frame in candidate image sequence; determining self-expression feature vector of query image sequence, collaborative expression feature vector of the query image sequence, self-expression feature vector of candidate image sequence, and collaborative expression feature vector of candidate image sequence based on feature vector of each frame in query image sequence and feature vector of each frame in candidate image sequence; determining similarity feature vector between query image sequence and candidate image sequence based on self-expression feature vector of query image sequence, collaborative expression feature vector of query image sequence, self-expression feature vector of candidate image sequence, and collaborative expression feature vector of candidate image sequence; and determining matching result between query image sequen

Abstract: An image acquisition device includes a main body and a camera. A rotating portion is provided on the main body. The camera is arranged on the rotating portion. A knob portion is provided on the rotating portion at a lateral side of the camera in the axial direction. The knob portion can drive the rotating portion to rotate.

Abstract: An image style transform method includes: acquiring an initial image to be subjected to style transform; inputting a gradient of the initial image to an image style transform model, and obtaining a feature map of the initial image in a gradient domain from the image style transform model, where the image style transform model is obtained by being trained in the gradient domain based on a pixel-wise loss and a perceptual loss; and performing image reconstruction according to the feature map of the initial image in the gradient domain to obtain a style image.

Abstract: An image processing method includes: a color feature extracted from a first image is acquired; a customized mask feature is acquired, the customized mask feature being configured to indicate a regional position of the color feature in the first image; and the color feature and the customized mask feature are input to a feature mapping network to perform image attribute edition to obtain a second image.

Abstract: The present disclosure relates to a target object tracking method and apparatus, an electronic device, and a storage medium. The method includes: obtaining a first reference image of a target object; determining time information and location information of the target object in an image to be analyzed according to the first reference image, the image to be analyzed including the time information and the location information; determining a trajectory of the target object according to the time information and the location information of the target object; and generating tracking information for tracking the target object according to the trajectory of the target object. Embodiments of the present disclosure obtain highly-accurate tracking information of the target object according to the trajectory of the target object determined in the image to be analyzed by using the first reference image of the target object, such that the success rate of target object tracking is improved.

Abstract: The present disclosure relates to a method, a device, and a storage medium for vehicle intelligent driving control. The vehicle intelligent driving control method comprises: collecting, by means of a vehicle-mounted camera of a vehicle, a video stream of a road image of a scene where the vehicle is; detecting a target object in the road image to obtain a bounding box of the target object; determining, in the road image, a free space for the vehicle; adjusting the bounding box of the target object according to the free space; and carrying out intelligent driving control on the vehicle according to the adjusted bounding box. The bounding box of the target object can be used to identify the position and determine the actual position of the target object more precisely, such that intelligent driving control can be carried out on the vehicle more accurately.