Abstract: The present disclosure relates to an image processing method and apparatus, an electronic device, and a storage medium. The method includes: acquiring a first image and a first attention map of a first target object in the first image, where the first attention map indicates the position of the first target object in the first image; acquiring a second image and a second mask image of the second image, where pixel values of pixels in the second mask image are equal; and obtaining, according to first input information and second input information, a first prediction result that indicates the position of the first target object in the second image, where the first input information is determined according to the first image and the first attention map, and the second input information is determined according to the second image and the second mask image.

Abstract: Image association methods, systems and apparatuses are provided. The method includes: obtaining a first image and a second image, where the first image is obtained by capturing a scene by a first image capture device at a first view, the second image is obtained by synchronously capturing the scene by a second image capture device at a second view, and the first view is different from the second view; determining an epipole of the first image capture device on a plane of the second image; determining a projection point of a first target point in a first bounding box on the second image, where the first bounding box is a bounding box of a target in the first image; and determining an association bounding box of the first bounding box in the second image according to the epipole and the projection point.

Abstract: Embodiments of the present specification provide a target recognition method, apparatus, and system. The method comprises: obtaining an image recognition result and a radio frequency recognition result of target objects in a target region, and then determining the distribution of the target objects in the target region according to the radio frequency recognition result and the image recognition result. Since the radio frequency recognition result and the image recognition result are fused, the target objects in the target region can be accurately recognized, so as to improve the recognition accuracy.

Abstract: Disclosed are a method and apparatus for filtering images and an electronic device. The method includes: obtaining a first image, where the first image is an image frame in a video stream obtained by collecting images of a target area; obtaining a first detection result of a target object in the first image by detecting the first image; determining a state of a target object with to-be-determined state according to the first detection result of the target object in the first image and a second detection result of the target object with to-be-determined state; and determining a quality level of an image in a bounding box of the target object with to-be-determined state according to the state of the target object with to-be-determined state, where the bounding box of the target object with to-be-determined state is determined according to the first detection result of the target object with to-be-determined state.

Abstract: Embodiments of the present disclosure provide methods and apparatuses for obtaining sample images, and electronic devices. The method includes: acquiring images of stacked bodies, where the stacked bodies in different images have different item information, and the item information comprises: an attribute and a stacking mode of a stacked body; and taking an acquired image as a sample image when the acquired image meets an image quality condition.

Abstract: The present disclosure relates to an image processing method, an image processing device, and a storage medium. The method comprises: acquiring an image to be processed; acquiring, by performing an encoding processing on the image to be processed, probability distribution data of features of a person object in the image to be processed as target probability distribution data, the features being used for identifying an identity of the person object; and acquiring, by performing retrieving in a database using the target probability distribution data, images in the database having probability distribution data matching the target probability distribution data as a target image. Corresponding device, processor and storage medium are also disclosed.

Abstract: Provided are a method and apparatus for processing a laser radar based sparse depth map, a device and a medium. The method for processing a laser radar based sparse depth map includes: inputting, into a neural network, the laser radar based sparse depth map; and acquiring, by the neural network, at least two feature maps, each of a respective different scale, for the laser radar based sparse depth map, performing valid point feature fusion for each of the at least two feature maps of the different scales, and processing the at least two feature maps having subjected to the valid point feature fusion, to obtain a processed depth map, wherein a number of valid points in the processed depth map is greater than a number of valid points in the laser radar based sparse depth map.

Abstract: The present disclosure relates to a method and apparatus for recognizing stacked objects, an electronic device, and a storage medium. The method for recognizing stacked objects includes: obtaining a to-be-recognized image, wherein the to-be-recognized image includes a sequence formed by stacking at least one object along a stacking direction; performing feature extraction on the to-be-recognized image to obtain a feature map of the to-be-recognized image; and recognizing a category of the at least one object in the sequence according to the feature map. The embodiments of the present disclosure may implement accurate recognition of the category of stacked objects.

Abstract: The present disclosure relates to a method and apparatus for recognizing a sequence in an image, an electronic device, and a storage medium. The method includes: performing feature extraction on a to-be-processed image to obtain a first feature map of the to-be-processed image, where the to-be-processed image includes a sequence formed by stacking at least one object along a stacking direction; determining a region feature of each segmented region in the to-be-processed image based on the first feature map, where all segmented regions are obtained by dividing the to-be-processed image into k regions along the stacking direction, k is a set number of objects stacked along the stacking direction, and k is an integer greater than 1; and determining a category of each object in the sequence based on the region feature of each segmented region. Embodiments of the present disclosure may implement recognition of stacked objects in a sequence.

Abstract: The invention discloses an apparatus for repairing wheel tread with material increase, which comprises a slide rail, a rack, a case, a supporting device, a drive device, a wheel heating device, an ingot blank heating device and an agitation and output device; the rack is fixedly arranged above the slide rail, an upper end of the case is fixedly connected to an upper portion of the rack, the supporting device and drive device are both slidably connected to the slide rail, an output end of the drive device is fixedly connected to the wheels which are positioned below a semicircular notch and are located eccentrically relative to the semicircular notch. The wheel heating device is fixedly arranged below the case, the ingot blank heating device and the agitation and output device are both fixedly arranged in the case, the ingot blank heating device is communicated with the agitation.

Abstract: The present disclosure relates to an image processing method and device, an electronic apparatus and a storage medium. The method comprises: performing feature extraction on an image to be processed to obtain a first feature map of the image to be processed; splitting the first feature map into a plurality of first sub-feature maps according to dimension information of the first feature map and a preset splitting rule, wherein the dimension information of the first feature map comprises dimensions of the first feature map and size of each dimension; performing normalization on the plurality of first sub-feature maps respectively to obtain a plurality of second sub-feature maps; and splicing the plurality of second sub-feature maps to obtain a second feature map of the image to be processed. Embodiments of the present disclosure can reduce the statistical errors during normalization of a complete feature map.

Abstract: Methods, apparatuses, devices, program products, and media can improve the accuracy rate of searching for a target person. The method includes: obtaining an image of the target person; searching a face image library by using the image of the target person to obtain a first face image template matching the image of the target person, where the face image library includes a plurality of face image templates; and obtaining, according to the first face image template and a pedestrian image library, at least one target pedestrian image template matching the image of the target person, where the pedestrian image library includes a plurality of pedestrian image templates.

Abstract: A method for predicting walking behaviors includes: encoding walking behavior information of at least one target object in a target scene within a historical time period M to obtain a first offset matrix for representing the walking behavior information of the at least one target object within the historical time period M; inputting the first offset matrix into a neural network, and outputting by the neural network a second offset matrix for representing walking behavior information of the at least one target object within a future time period M?; and decoding the second offset matrix to obtain the walking behavior prediction information of the at least one target object within the future time period M?.

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: 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: A key point detection method includes: obtaining first feature maps of a plurality of scales for an input image, scales of the first feature maps having a multiple relationship; performing forward processing on each first feature map through a first pyramid neural network to obtain second feature maps in one-to-one correspondence to the first feature maps, each second feature map having the same scale as that of its respective first feature map; performing reverse processing on each second feature map through a second pyramid neural network to obtain third feature maps in one-to-one correspondence to the second feature maps, each third feature map having the same scale as that of its respective second feature map; and performing feature fusion processing on each third feature map, and obtaining the position of each key point in the input image through the feature map subjected to the feature fusion processing.

Abstract: A tight connection device for the aseptic transfer of a biopharmaceutical product includes: a stationary temporary clamping means keeping the container hermetically clamped against the chamber; a stationary unlocking means capable of switching the container from an initial locked position to an intermediate unlocked position; a stationary locking means; and an annular functional crown capable of being rotated to actuate the stationary unlocking means and the stationary locking means of the container. The stationary unlocking means and the stationary locking means are mechanically linked to the annular functional crown and arranged such that the rotation of the annular functional crown and the end locked position. The device further includes stationary immobilising/release means capable of allowing and preventing the annular functional crown to rotate.

Abstract: A bag for containing a biopharmaceutical product includes at least one fluid input/output port and a measurement opening remote from the fluid port. A probe holder port has a body with an axial through-bore and a peripheral collar with a second fastening zone and defining a fastening plane. A contact probe, extending in the axial through-bore, is assembled on the probe holder port, has an active measurement end portion in contact with the product, and has an end portion opposite the end portion. The probe holder port includes a transition portion between the body and the collar, which extends between a radially central end portion of the collar and a first axial end portion of the body. The first axial end portion is the axial end portion closest to the radially central end portion of the collar. The first axial end portion is disposed on the outer side relative to the fastening plane, and the active measurement end portion is axially disposed on the outer side relative to the fastening plane.

Abstract: A pedestrian re-identification method includes: obtaining a target video containing a target pedestrian and at least one candidate video; encoding each target video segment in the target video and each candidate video segment in the at least one candidate segment separately; determining a score of similarity between the each target video segment and the each candidate video segment according to encoding results, the score of similarity being used for representing a degree of similarity between pedestrian features in the target video segment and the candidate video segment; and performing pedestrian re-identification on the at least one candidate video according to the score of similarity.

Abstract: Embodiments of the present disclosure provide methods and apparatuses for vehicle appearance feature recognition, methods and apparatuses for vehicle retrieval, storage media, and electronic devices. The method for vehicle appearance feature recognition includes: obtaining multiple region segmentation results of a target vehicle from an image to be recognized; extracting global feature data and multiple pieces of region feature data from the image to be recognized based on the multiple region segmentation results; and fusing the global feature data and the multiple pieces of region feature data to obtain appearance feature data of the target vehicle.