Abstract:
The present disclosure discloses a method and system for identifying a part of a vehicle and a vehicle inspection system. The method includes: acquiring a vehicle body image sequence of a vehicle to be identified; reconstructing the vehicle body by using a first vehicle body reconstruction model generated through a deep learning algorithm and on the basis of the vehicle body image sequence, so as to acquire a vehicle body reconstruction image of the vehicle to be identified; and identifying a boundary identifier of the vehicle to be identified on the basis of the vehicle body reconstruction image of the vehicle to be identified.
Abstract:
A method of evaluating an image quality for an imaging system and the imaging system are provided. The method may comprise: acquiring an image to be evaluated which is generated by the imaging system; extracting a plurality of sub-images from the image; obtaining a coefficient vector indicating a degree of sparsity by applying a sparse decomposition on the plurality of sub-images based on a pre-set redundant sparse representation dictionary; and performing a linear transformation on the coefficient vector so as to obtain an evaluation value for the image quality. The sparse dictionary is learned by only using a few high quality perspective images, and then the image quality is evaluated based on the sparse degree of the image which is obtained by using the sparse dictionary, thereby achieving a convenient and rapid no-reference image quality evaluation.
Abstract:
Disclosed is method and apparatus for creating a statistical average model of an enamel-dentine junction. The method includes steps of: acquiring CT image data of a tooth; segmenting the CT image data to obtain a surface of an enamel-dentine junction; segmenting the obtained surface using a curvature-based clustering algorithm to remove a bottom of the enamel-dentine junction; spherical-parameterizing, by means of spherical harmonic analysis, the surface of the enamel-dentine junction after removal of the bottom; and aligning different samples of the tooth to obtain a statistical average model.
Abstract:
The present disclosure relates to a fluoroscopic inspection system for automatic classification and recognition of cargoes. The system includes: an image data acquiring unit, configured to perform scanning and imaging for a container by using an X-ray scanning device to acquire a scanned image; an image segmenting unit, configured to segment the scanned image into small regions each having similar gray scales and texture features; a feature extracting unit, configured to extract features of the small regions; a training unit, configured to generate a classifier according to annotated images; and a classification and recognition unit, configured to recognize the small regions by using the classifier according to the extracted features, to obtain a probability of each small region pertaining to a certain category of cargoes, and merge small regions to obtain large regions each representing a category.
Abstract:
Disclosed are a retrieving system and a retrieving method based on content of fluoroscopic images, the retrieving system comprising: a pre-classifying module, configured to pre-classify fluoroscopic images; an image content feature extracting module, configured to perform feature extraction for contents of the fluoroscopic images; an image representing module, configured to construct an image representation vector; a retrieving module, configured to construct a result of preliminary candidates; a diversified filtering module, configured to filter the result of preliminary candidates, select an image subset capable of covering a plurality of article categories, and thereby construct a diversified retrieval result; a correlation feedback regulating module, configured to receive information feedback on the retrieval result from a user, and update the retrieval model; and an interacting module, configured to display the retrieval result, and collect feedback on user's satisfaction of the retrieval result.
Abstract:
The present disclosure provides method and apparatus for marking a target in a 3D image. The method include steps of: acquiring Computed Tomography (CT) image data of a scene; rendering a 3D image of the scene using ray casting based on the CT image data; removing a transparent region from the 3D image based on a fixed 2D transfer function; and marking the target in the 3D image.
Abstract:
This invention provides a scan method, scan system and radiation scan controller, and relates to the field of radiation. The scanning method includes obtaining detection data of an object to be inspected under radiation scanning using a detector, adjusting an accelerator output beam dose rate and/or an output electron beam energy level of a radiation emission device according to the detection data. With this method, working conditions of the accelerator of the radiation emission device may be adjusted according to the detection data detected by the detector, so that for a region having a larger mass thickness, a higher output beam dose rate or a higher electron beam output energy level is adopted to guarantee satisfied imaging technical indexes, for a region having a smaller mass thickness, a lower output beam dose rate or a lower electron beam output energy level is adopted to reduce the environmental dose level while guaranteeing satisfied imaging technical indexes.
Abstract:
A method and device for reconstructing a CT image and a storage medium are disclosed. CT scanning is performed on an object to be inspected to obtain projection data on a first scale. Projection data on a plurality of other scales is generated from the projection data on the first scale. Projection data on each scale is processed on the corresponding scale by using a first convolutional neural network to obtain processed projection data, and a back-projection operation is performed on the processed projection data to obtain a CT image on the corresponding scale. CT images on the plurality of scales are fused to obtain a reconstructed image of the object to be inspected.
Abstract:
The present disclosure provides a method for recognizing an article using a multi-energy spectrum X-ray imaging system and a multi-energy spectrum X-ray imaging system. The method comprises: recognizing an application scenario and/or priori information of the article; selecting a parameter mode suitable for the article from a plurality of parameter modes stored in the multi-energy spectrum X-ray imaging system based on the recognized application scenario and/or priori information; and recognizing the article using the selected parameter mode, wherein the plurality of parameter modes are obtained by optimizing system parameters of the multi-energy spectrum X-ray imaging system under a specific condition using a training sample library for various articles.
Abstract:
An inspection method and system for inspecting whether there is any liquor in goods is provided. The method includes: acquiring a radiation image of goods being inspected; processing on the radiation image to obtain an ROI; inspecting on the ROI using a liquor goods inspection model to determine if the ROI of the radiation image contains liquor goods. The above solution performs liquor inspection on scanned images of goods, especially containers, so as to intelligently assist the image inspectors.