Douglas Q. Abraham

A New Concept of Airport Security Screening

Airport security measures serve to protect the traveling public, crew and aircraft. According to the TSA over 600 million passengers travel on commercial airlines and more than 700 million pieces of luggage are screened each year [2]. With such a large number of people traveling, airports and aircraft have become natural targets for terrorists. We propose a new concept of airport security screening measures that connects all airport security screening equipment for passengers and their belongings together from the onset of their check-in. Utilizing historical and profiling passenger information and the global security threat level to adapt and optimize the screening level and process for each screening device, it will streamline all check-in activities while drastically improving the overall quality of screening.

3D threat image projection

Automated Explosive Detection Systems utilizing Computed Tomography perform a series X-ray scans of passenger bags being checked in at the airport, and produce various 2-D projection images and 3-D volumetric images of the bag. The determination as to whether the passenger bag contains an explosive and needs to be searched manually is performed through trained Transportation Security Administration screeners following an approved protocol. In order to keep the screeners vigilant with regards to screening quality, the Transportation Security Administration has mandated the use of Threat Image Projection on 2-D projection X-ray screening equipment used at all US airports. These algorithms insert visual artificial threats into images of the normal passenger bags in order to test the screeners with regards to their screening efficiency and their screening quality at determining threats. This technology for 2-D X-ray system is proven and is widespread amongst multiple manufacturers of X-ray projection systems. Until now, Threat Image Projection has been unsuccessful at being introduced into 3-D Automated Explosive Detection Systems for numerous reasons. The failure of these prior attempts are mainly due to imaging queues that the screeners pickup on, and therefore make it easy for the screeners to discern the presence of the threat image and thus defeating the intended purpose. This paper presents a novel approach for 3-D Threat Image Projection for 3-D Automated Explosive Detection Systems. The method presented here is a projection based approach where both the threat object and the bag remain in projection sinogram space. Novel approaches have been developed for projection based object segmentation, projection based streak reduction used for threat object isolation along with scan orientation independence and projection based streak generation for an overall realistic 3-D image. The algorithms are prototyped in MatLab and C++ and demonstrate non discernible 3-D threat image insertion into various luggage, and non discernable streak patterns for 3-D images when compared to actual scanned images.

Projection Image Enhancement for Explosive Detection Systems

Automated Explosive Detection Systems (EDS) utilizing Computed Tomography (CT) generate a series of 2-D projections from a series of X-ray scans OF luggage under inspection. 3-D volumetric images can also be generated from the collected data set. Extensive data manipulation of the 2-D and 3-D image sets for detecting the presence of explosives is done automatically by EDS. The results are then forwarded to human screeners for final review. The final determination as to whether the luggage contains an explosive and needs to be searched manually is performed by trained TSA (Transportation Security Administration) screeners following an approved TSA protocol. The TSA protocol has the screeners visually inspect the resulting images and the renderings from the EDS to determine if the luggage is suspicious and consequently should be searched manually. Enhancing those projection images delivers a higher quality screening, reduces screening time and also reduces the amount of luggage that needs to be manually searched otherwise. This paper presents a novel edge detection algorithm that is geared towards, though not exclusive to, automated explosive detection systems. The goal of these enhancements is to provide a higher quality screening process while reducing the overall screening time and luggage search rates. Accurately determining the location of edge pixels within 2-D signals, often the first step in segmentation and recognition systems indicates the boundary between overlapping objects in a luggage. Most of the edge detection algorithms such as Canny, Prewitt, Roberts, Sobel, and Laplacian methods are based on the first and second derivatives/difference operators. These operators detect the discontinuities in the differences of pixels. These approaches are sensitive to the presence of noise and could produce false edges in noisy images. Including large scale filters, may avoid errors generated by noise, but often simultaneously eliminating the finer edge details as well. This paper proposes a novel pixels ratio based edge detection algorithm which is immune to noise. The new method compares ratios of pixels in multiple directions to an adaptive threshold to determine edges in different directions.

Bag Separation Algorithm

Automated Explosive Detection Systems (EDS) utilizing Computed Tomography (CT) performs a series of X-ray scans of the luggage being checked, then various 2D projection images of the luggage are generated from the collected data set and sometimes 3D volumetric images of the luggage are generated in addition. Automatic explosives determination as to the presence of an explosive in the luggage is determined through extensive data manipulation of the 2D and 3D image sets, the results are then forwarded to a human interface for final review. The final determination as to whether the luggage contains an explosive and needs to be searched manually is performed by trained TSA (Transportation Security Administration) screeners following an approved TSA protocol. The TSA protocol has the screeners visually inspect the projection images and the renderings of the automated explosive results from detection to determine if the luggage needs to be suspected and consequently searched. Unlike conventional X-ray systems, the user interface for EDS systems are usually designed to display one bag at a time. However, in airport environments, there is usually more than one bag being processed. Therefore, segmentation is a crucial part of higher quality screening. If the screeners have to manually manipulate (zoom, pan, separate) the image, this increases overall screening time and decreases screener efficiency. This paper presents a novel image segmentation technique that is geared towards, though not exclusive to, automated explosive detection systems. The goal of this algorithm is to correctly separate each bag image to provide a higher quality screening process while reducing the overall screening time and luggage search rates.