Provided are a method and apparatus for detecting cargo in a container image using container wall background removal. The method of detecting cargo in a container image according to an aspect of the present invention includes: receiving a backscatter X-ray target container image captured on a target container; acquiring a backscatter X-ray empty target container wall image of an empty container that corresponds to the target container and a capturing condition of the backscatter X-ray target container image; generating a difference image in which a wall background of the target container is removed based on the backscatter X-ray target container image and the backscatter X-ray empty target container wall image; and detecting cargo included in the target container based on the difference image.

Various embodiments are directed toward systems and methods relating to security screening. For example, a screening system includes chamber scanners oriented to scan different heights corresponding to different respective portions of a user, to identify whether the user is carrying an item that is to be divested. The screening system also includes a divestment scanner configured to obtain a detailed item characterization of the item divested from the user.

An apparatus includes an extract unit configured to extract features of a first image based on an electromagnetic wave in a first frequency band, an acquire unit configured to acquire motion information about the features, a classify unit configured to classify the features into a first group and a second group based on the motion information, and a remove unit configured to remove, from the first image, a signal corresponding to the feature belonging to the first group.

Information on an area specified as having an article is acquired, synthesis is performed with X-ray transmission amounts acquired by a plurality of sensors in the same area (for example, the same background and the same luggage), and material information is estimated from the X-ray transmission amount synthesized again to generate a color image. As a synthesis target, for example, combination information of articles accumulated during operation is used, and articles having many combinations are used as combination of articles included in the same luggage. A color image generated by synthesis is used as learning data.

A method for natural language interaction includes recording speech provided by a human user. The recorded speech is translated into a machine-readable natural language input relating to an interaction topic. An interaction timer is maintained that tracks a length of time since a last machine-readable natural language input referring to the interaction topic was translated. Based on a current value of the interaction timer being greater than an interaction engagement threshold, a message relating to the interaction topic is delivered with a first natural language phrasing that includes an interaction topic reminder. Based on the current value of the interaction timer being less than the interaction engagement threshold, the message relating to the interaction topic is delivered with a second natural language phrasing that lacks the interaction topic reminder.

Among other things, one or more systems and/or techniques for classifying an item disposed within an object are provided herein. A three-dimensional image of the object (e.g., a bag) is segmented into a set of item representations (e.g., laptop, thermos, etc.). An item is identified from the set of item representations based upon item features of the item, such as the laptop that could be used to conceal an item of interest such as an explosive. A region comprising a three-dimensional image of the item is divided into a set of sub-regions (e.g., a first sub-region encompassing a screen, a second sub-region encompassing a motherboard, etc.). The item is classified as a potential first type of item (e.g., an explosive laptop) when any sub-region has a number of voxels, with computed tomography (CT) values within a range of known CT values for a first type of item, exceeding a threshold.

An alert output timing control apparatus is configured to hold an analysis target image and time information indicating a period of time time to be spent until alert is output, and output the analysis target image to a display apparatus. When the analysis target image is input to a model and it is determined that an alert target object is included in a photographic object of the analysis target image, the alert output timing control apparatus obtains a period of time time from the time information and performs control so that alert is output at timing that arrives when the obtained period of time time elapses since given timing after the analysis target image is output.

In an example, a method includes: based on a starting set of real image data of a set of one or more original images obtained using a detection process of a detection system, identifying elements of the real image data which are picture or volume elements; performing data augmentation on the identified elements to produce one or more augmented images; replacing the set of one or more original images with a set of the one or more augmented images; analyzing the set of one or more augmented images (which may be supplemented with additional real data) using the detection process; and evaluating a detection response of the detection system for each augmented image of the set of one or more augmented images. The analyzing and evaluating may be performed by using an emulator for the detection system which was used to obtain the one or more original images.

According to one embodiment, a processing system includes a concatenating unit configured to form a concatenated image by concatenating m images based on received signals acquired by two or more radars, and a determination unit configured to determine whether or not there is an object, based on the concatenated image. “m” is an integer equal to or more than 2.

A system and method for automatically detecting prohibited objects in a compartment at a security checkpoint includes receiving a three-dimensional representation of a compartment from an imaging device connected to the computing system, identifying, by the processor, a region within the compartment that has a highest likelihood of containing a prohibited object, based on a voxel classification of the three-dimensional representation using a first trained neural network, and classifying, by the processor, shapes of objects located within the identified region using a second trained neural network to determine whether any of the shapes correspond to a shape of a prohibited object known by the second neural network.