Abstract: The disclosure relates to a method for warehouse storage-location monitoring, a computer device, and a storage medium. The method includes the following. Video data of a warehouse storage-location area is obtained, and a target image corresponding to the warehouse storage-location area is obtained based on the video data, where the warehouse storage-location area includes an area of a storage-location and an area around the storage-location. The target image is detected based on a category detection model, to determine a category of each object appearing in the target image. A detection result is obtained by detecting a status of each object based on the category of each object. The detection result is transmitted to a warehouse scheduling system, where the detection result is used for the warehouse scheduling system to monitor the warehouse storage-location area.

Abstract: Embodiments of the present application disclose a local map updating method and apparatus, and an electronic device, including: acquiring key-frame point cloud data, where the key-frame point cloud data includes point cloud coordinate points; matching the key-frame point cloud data with a prior map to determine target voxels, in the prior map, corresponding to the point cloud coordinate points; and updating a point cloud probability distribution corresponding to each target voxel according to a first map coordinate point and a second map coordinate point included in each target voxel to obtain a target map, where the first map coordinate point is obtained by converting the point cloud coordinate point corresponding to the target voxel, and the second map coordinate point is a coordinate point included in the target voxel in the prior map.

Abstract: A method for cargo counting, a computer equipment, and a storage medium are provided in the disclosure. The method includes the following. Three-dimensional (3D) point cloud data of a set of cargoes within a preset placement region is obtained based on a cargo-counting instruction. Whether the set of cargoes are in a first placement state is determined according to the 3D point cloud data. Based on a determination that the set of cargoes are in the first placement state, a quantity of the set of cargoes is calculated.

Abstract: Provided is a path planning method of a handling device, a handling device, and an electronic device. The method includes: based on position information of a target goods placement area in a truck bed and an initial position, determining a handling path corresponding to the target goods placement area, where the handling path includes a first handling path including a turn-around position and a loading-unloading position corresponding to the target goods placement area; sending the handling path to a target handling device such that the target handling device, based on the first handling path, travels to the turn-around position in a forward mode, and rotates at the turn-around position to enable a bearing assembly of the rotated target handling device to face toward the target goods placement area, and then travels, in a backward mode, to the loading-unloading position to perform goods loading or unloading operations.

Abstract: The present disclosure discloses a handling device and a method applied to the handling device, a controller and a plurality of 3D laser radars are arranged on a vehicle body of the handling device, the controller is in communication connection with the plurality of 3D laser radars, the plurality of 3D laser radars are configured to acquire 3D point cloud data of environment regions in a plurality of different orientations of the vehicle body, and the controller may determine the pose of the handling device according to the plurality of 3D point cloud data to position the handling device.

Abstract: By providing a 3D laser radar and a camera on a fork assembly of the handling device, the 3D laser radar and the camera can move relative to a device body of the handling device along with the fork assembly. When the 3D laser radar and the camera move along with the fork assembly to a position corresponding to an object, the 3D laser radar collects 3D point cloud data of the object and the camera collects image data of the object, and a controller of the handling device can determine, based on the 3D point cloud data, pose information of the object, and determine, based on the image data, a fork insertion position of the object, and control, based on the pose information and the fork insertion position, the fork assembly to align with the fork insertion position of the object to pick up the object.

Abstract: A method and apparatus for forklift pickup, a computer device, and a storage medium are provided in the disclosure. The method includes the following. Observational data of a truck parking area is obtained by observing the truck parking area. Point cloud data of at least one truck is obtained in the observational data, and point cloud data of each of the at least one carrier is obtained in the point cloud data of the at least one truck. A relative pose of each of at least one carrier is determined based on the point cloud data of each of the at least one carrier. A pickup priority is determined based on the point cloud data of each of the at least one carrier. A forklift is controlled to perform pickup according to the relative pose of each of the at least one carrier and the pickup priority.

Abstract: A method and apparatus for forklift pickup, a computer device, and a storage medium are provided in the disclosure. The method includes the following. Observational data of a truck parking area is obtained by observing the truck parking area. Point cloud data of at least one truck is obtained in the observational data, and point cloud data of each of the at least one carrier is obtained in the point cloud data of the at least one truck. A relative pose of each of at least one carrier is determined based on the point cloud data of each of the at least one carrier. A pickup priority is determined based on the point cloud data of each of the at least one carrier. A forklift is controlled to perform pickup according to the relative pose of each of the at least one carrier and the pickup priority.

Abstract: An information processing method of an embodiment is a processing method of information acquired by imaging performed by a medical image diagnostic apparatus, the information processing method includes the steps of: acquiring noise data by imaging a phantom using a medical imaging apparatus; based on first subject projection data acquired by the imaging performed by a medical image diagnostic modality of a same kind as the medical image diagnostic apparatus and the noise data, acquiring synthesized subject data in which noise based on the noise data is added to the first subject projection data; and acquiring a noise reduction processing model by machine learning using the synthesized subject data and second subject projection data acquired by the imaging performed by the medical image diagnostic modality.

Abstract: According to some embodiments, a method comprises obtaining a group of reconstructed-image data; converting the group of reconstructed-image data to a derivative of Radon space, thereby generating Radon-space data, wherein the Radon-space data have a radial sampling pattern in the derivative of Radon space; and generating resampled data by inputting the Radon-space data into a first trained machine-learning model for resampling Radon-space data, wherein the resampled data have a cone-beam-projection-geometry-shaped sampling pattern in the derivative of Radon space.

Abstract: A method for scatter estimation in a CT including a detector having multiple detector pixels includes: obtaining projection data by scanning an imaging object; reconstructing image data from the projection data; estimating, based on the projection data, a first scatter distribution; selecting, based on the first scatter distribution, a first subset of the pixels; calculating, based on the projection data and the image data, a second scatter distribution with respect to the selected first subset, the second scatter distribution having higher accuracy than the first scatter distribution; acquiring, based on the second scatter distribution, a third scatter distribution with respect to a second subset of the pixels, the third scatter distribution having higher spatial resolution than the second scatter distribution.

Abstract: A method for determining material-cage stacking, a computer device, and a storage medium are provided. The method includes the following. A material-cage image is obtained by photographing a first stacking apparatus of a first material cage and a second stacking apparatus of a second material cage. The stacking apparatuses of the two material cages in the material-cage image can be recognized respectively with two detection models. The first stacking result is obtained by obtaining location information of the stacking apparatuses of the two material cages with the first detection model, and the second stacking result is obtained with the second detection model.

Abstract: A method for determining material-cage stacking, a computer device, and a storage medium are provided. The method includes the following. A material-cage image is obtained by photographing a first stacking apparatus of a first material cage and a second stacking apparatus of a second material cage. The stacking apparatuses of the two material cages in the material-cage image can be recognized respectively with two detection models. The first stacking result is obtained by obtaining location information of the stacking apparatuses of the two material cages with the first detection model, and the second stacking result is obtained with the second detection model.

Abstract: A method for determining material-cage stacking, a computer device, and a storage medium are provided. The method includes the following. A material-cage image is obtained by photographing a first stacking apparatus of a first material cage and a second stacking apparatus of a second material cage. The stacking apparatuses of the two material cages in the material-cage image can be recognized respectively with two detection models. The first stacking result is obtained by obtaining location information of the stacking apparatuses of the two material cages with the first detection model, and the second stacking result is obtained with the second detection model.

Abstract: An apparatus, method, and computer-readable medium for improving image quality of a medical volume.

Abstract: Devices, systems, and methods obtain first radiographic-image data reconstructed based on a set of projection data acquired in a radiographic scan; apply one or more trained machine-learning models to the set of projection data and the first radiographic-image data to obtain a set of parameters for a scatter kernel; input the set of parameters and the set of projection data into the scatter kernel to obtain scatter-distribution data; and perform scatter correction on the set of projection data using the scatter-distribution data, to obtain a set of corrected projection data.

Abstract: According to some embodiments, a method comprises obtaining a set of projection data acquired from a CT scan of an object; generating, based on the set of projection data, one or more sets of preliminary scattering data; and performing X-ray scatter correction by inputting the obtained set of projection data and the generated one or more sets of preliminary scattering data into a trained machine-learning model for extracting X-ray scatter components from the set of projection data.

Abstract: The present invention relates to a composition comprising 1) an aqueous dispersion of multistage opacifying polymer particles with a superposing binder layer; 2) a colorant; and 3) inorganic opacifying pigment particles. The composition of the present invention is useful for one coat hide paint formulations.

Abstract: A method and a device for cargo inventorying and a storage medium are provided in the disclosure. A storage-space image is obtained, where the storage-space image is obtained by performing image acquisition on at least one storage-space where cargoes are stored. A detection result is obtained by performing inference detection on the storage-space image. In addition, a result of cargo inventorying on the storage space is obtained by emphasizing cargoes in the storage-space image according to the detection result. In the disclosure, the detection result can be produced automatically by performing inference detection on the storage-space image. Based on the detection result produced automatically, it is also able to automatically emphasize the cargoes in the storage-space image and realize automatic inventory of cargoes by programmed automatic-control, thereby improving the efficiency of cargo inventorying.

Abstract: A method of processing information acquired by imaging performed by a medical image diagnostic apparatus, the method including but not limited to at least one of (A) acquiring a training image volume including at least one three-dimensional object having an embedded three-dimensional feature having a first cross-sectional area in a first three-dimensional plane; selecting a second cross-sectional area in a second three-dimensional plane containing the embedded three-dimensional feature, wherein the second cross-sectional area is larger than the first cross-sectional area; and training an untrained neural network with an image of the second cross-sectional area generated from the training image volume; and (B) acquiring a first set of training data; determining a first distribution of tissue density information from the first set of training data; generating from the first set of training data a second set of training data by performing at least one of a tissue-density shifting process and a tissue-density sca