Provided are a method and a device of binding security inspection information, an electronic apparatus and a storage medium, which may be applied to the field of security inspection technology. In the method of binding security inspection information of the present disclosure, the pedestrian detection frame is acquired by detecting the video stream, and each pedestrian detection frame has the coordinate information and the tracking information. According to the coordinate information and the tracking information of the pedestrian detection frame, the baggage security inspection information, the pedestrian security inspection information, and the identity information are bound to acquire all security inspection information of a detected person, so that a security inspection efficiency is improved.

A luggage detection device is configured to detect luggage by generating computed tomography (CT) imaging slices. For each of the CT imaging slices, the luggage detection device is configured to identify at least one region within the CT imaging slice for removal based on at least one predefined rule, to remove pixel data associated with the at least one identified region within the CT imaging slice, to generate a pixel count representing a number of pixels in the modified CT imaging slice that include a value above a threshold pixel value, and to generate an object indicator based on a determination that the generated pixel count is above a threshold pixel count. The luggage detection device is further configured to display at least one of the plurality of CT image slices based on the presence of the corresponding baggage indicator.

Among other things, one or more systems and/or techniques for segmenting a representation of a sheet object from an image are provided herein. To identify elements of an image (e.g., pixels and/or voxels) representative of sheet objects, a constant false alarm rate (CFAR) score and a topological score are computed for respective elements being analyzed. The CFAR score indicates a relationship between an element and a neighborhood of elements when viewed as a collective unit. The topological score indicates a relationship between the element and a neighborhood of elements when viewed neighbor-by-neighbor. When the CFAR score is within a specified range of CFAR scores and the topological score is within a specified range of topological scores, the element is labeled as being associated with a sheet object. A connected component labeling (CCL) approach may be used to group elements labeled as being associated with a sheet object.

Systems and methods are described, and an example method includes a training an artificial intelligence (AI) classifier of scanned items, including obtaining a training set of sample raw scans. The set includes a population of sample in-class raw scans, which include blocks of sensor data from scans of regions having in-class objects, and the set includes a population of sample not-in-class raw scans, which include blocks of sensor data from scan of regions without in-class objects. The example includes applying the AI classifier to sample raw scans in the training set, measuring errors in the results, and updating classifier parameters based on the errors, until detecting a training completion state.

Some embodiments are directed to a system that includes a processor and memory circuitry (PMC) that is configured to: obtain an image of an item acquired by an acquisition device; perform a first detection using a first software module implementing at least one first deep neural network to detect at least one given area of the image including at least part of a given element of the item enabling concealment of a prohibited object; perform a second detection including using a second software module implementing at least one second neural network to detect whether the given area includes a prohibited object; and perform an action upon detection of a presence of a prohibited object in the image, wherein the detection is based at least on an output of the second detection.

Disclosed is a dual-energy ray imaging method and system. The method comprises: calculating the mass thicknesses of the materials in the overlapped area of two materials by using a calibrated surface fitting method, and then decomposing a pair of original high-energy and low-energy data for this pixel into two high-low-energy data sets corresponding to the two materials, and finally calculating and acquiring the composition result of different materials for each pixel. The disclosure is especially advantageous in that the problem of error recognition of materials due to the two overlapped materials can be eliminated and the stratified imaging of multiple materials can be achieved, thereby improving the accuracy of the substance recognition and reducing the rate of false positive and false negative which is very important to the applications in the field of security check and anti-smuggling.

A search object and m-number of first local features respectively constituted by a feature vector of 1 to i dimensions of local areas of m-number of feature points in an image of the search object are stored, feature points are extracted from the image, second local features respectively constituted by a feature vector of 1 dimension to j dimensions are generated with respect to local areas of n-number of feature points, a smaller number of dimensions among the number of dimensions i of the first local features and the number of dimensions j of the second local features is selected, and an existence of the search object in the image in the video is recognized when a prescribed ratio of the m-number of first local features up to the selected number of dimensions corresponds to the n-number of second local features up to the selected number of dimensions.

A CT system and method thereof are discloses. The system includes: a fixed multi-plane multi-source X-ray generation device and a control system thereof that provide X-ray source used in luggage inspection; a single-energy, pseudo-dual-energy or spectral detector system and data transfer system that receive perspective data of X ray penetrating the luggage, and transfer the data to a computer for processing; a conveyor and a control system thereof that control a speed for moving the luggage forth and back, and perform tomogram scanning; and a host computer system that performs tomogram reconstruction and provides man-machine interaction. The system takes full advantage of characteristics, such as high speed and stability, brought by the distributed ray sources which replace the normal slip ring technology. The system also adopts the idea of inverse-geometry CT, and reduces detector area and cost by increasing the number of ray sources. With the reduction of detector area, cone-beam artifacts and cup-shape artifacts caused by scattering are also reduced, and influence of the oblique effect on registration of dual-energy data is suppressed.

The present invention relates to an automatic method to determine the authenticity of a product.

Objects in a voxel based computer generated three dimensional environment are identified by traversing adjacent voxels meeting a predetermined criterion with respect to a scalar metadata value associated with each voxel, such as opacity or density. These adjacent voxels may be explored in accordance with a tree-crawling algorithm such as a breadth first or depth first algorithm. Once all adjacent cells meeting the criterion are identified, these are determined to represent a discrete object, and displayed as such. The starting point for the traversing process may be the voxel closest to a virtual camera position along the line of sight of that virtual camera meeting the criterion.