Grayscale enhancement techniques of x-ray images of carry-on luggage (original) (raw)
Related papers
Journal of Electronic Imaging, 2004
Very few image processing applications have dealt with x-ray luggage scenes in the past. Concealed threats in general, and low-density items in particular, pose a major challenge to airport screeners. A simple enhancement method for data decluttering is introduced. Initially, the method is applied using manually selected thresholds to progressively generate decluttered slices. Further automation of the algorithm, using a novel metric based on the Radon transform, is conducted to determine the optimum number and values of thresholds and to generate a single optimum slice for screener interpretation. A comparison of the newly developed metric to other known metrics demonstrates the merits of the new approach. On-site quantitative and qualitative evaluations of the various decluttered images by airport screeners further establishes that the single slice from the image hashing algorithm outperforms traditional enhancement techniques with a noted increase of 58% in lowdensity threat detection rates.
Automatic X-ray image segmentation for threat detection
Proceedings Fifth International Conference on Computational Intelligence and Multimedia Applications. ICCIMA 2003
Multithresholding and data clustering techniques are used to segment X-ray images for low intensity threat detection in carry-on luggage. The widely used statistical validity indexes methods do not generate a reasonable estimation of the optimal number of clusters and produce a biased evaluation of the segmented images acquired by different segmentation methods. We propose a method based on the Radon transform to determine the optimal number of clusters and to evaluate the segmented images. The method utilizes both statistical and spatial information from the image and is computationally efficient. Experimental results show that the proposed method produces results consistent with human visual assessment.
Effective Use of Color in X-ray Image Enhancement for Luggage Inspection
This paper discusses implementation of color-coding schemes for detecting a variety of potential threat objects in x-ray imagery of carry-on luggage by using color effectively. Using colors effectively refers to utilizing colors in a proper way to provide a more attractive and pleasing images to the screener's eye, which will help them to detect the threat more easily without much strain. This capability will increase the efficiency of luggage inspection by decreasing the time required to perform inspection and reduce the probability of errors due to fatigue.
2005
X-ray luggage inspection systems play an important role in ensuring air travelers security. However, the false alarm rate of commercial systems can be as high as 20% due to less than perfect image processing algorithms. In an effort to reduce the false alarm rate, this paper proposes a combinational scheme to fuse, de-noise and enhance dual-energy X-ray images for better object classification and threat detection. The fusion step is based on the wavelet transform. Fused images generally reveal more detail information; however, background noise often gets amplified during the fusion process. This paper applies a backgroundsubtraction- based noise reduction technique which is very efficient in removing background noise from fused X-ray images. The de-noised image is then processed using a new enhancement technique to reconstruct the final image. The final image not only contains complementary information from both source images, but is also background-noise-free and contrastenhanced, therefore easier to segment automatically or be interpreted by screeners, thus reducing the false alarm rate in X-ray luggage inspection.
Automated X-ray image analysis for cargo security: Critical review and future promise
Journal of X-Ray Science and Technology, 2017
We review the relatively immature field of automated image analysis for X-ray cargo imagery. There is increasing demand for automated analysis methods that can assist in the inspection and selection of containers, due to the ever-growing volumes of traded cargo and the increasing concerns that customs-and securityrelated threats are being smuggled across borders by organised crime and terrorist networks. We split the field into the classical pipeline of image preprocessing and image understanding. Preprocessing includes: image manipulation; quality improvement; Threat Image Projection (TIP); and material discrimination and segmentation. Image understanding includes: Automated Threat Detection (ATD); and Automated Contents Verification (ACV). We identify several gaps in the literature that need to be addressed and propose ideas for future research. Where the current literature is sparse we borrow from the single-view, multi-view, and CT X-ray baggage domains, which have some characteristics in common with X-ray cargo.
Detection of Regular Objects in Baggages Using Multiple X-ray Views
In order to reduce the security risk of a commercial aircraft, passengers are not allowed to take certain items in carry-on baggage. For this reason, human operators are trained to detect prohibited items using a manually controlled baggage screening process. In this paper, we propose the use of a method based on multiple X-ray views to detect some regular prohibited items with very defined shapes and sizes. The method consists of two steps: 'structure estimation', to obtain a geometric model of the multiple views from the object to be inspected (a baggage), and 'parts detection', to detect the parts of interest (prohibited items). The geometric model is estimated using a structure from motion algorithm. The detection of the parts of interest is performed by an adhoc segmentation algorithm (object dependent) followed by a general tracking algorithm based on geometric and appearance constraints. In order to illustrate the effectiveness of the proposed method, experimental results on detecting regular objects −razor blades and guns− are shown yielding promising results.
Detection of regular objects in baggage using multiple X-ray views
Insight - Non-Destructive Testing and Condition Monitoring, 2013
In order to reduce the security risk of a commercial aircraft, passengers are not allowed to take certain items in carry-on baggage. For this reason, human operators are trained to detect prohibited items using a manually controlled baggage screening process. In this paper, we propose the use of a method based on multiple X-ray views to detect some regular prohibited items with very defined shapes and sizes. The method consists of two steps: 'structure estimation', to obtain a geometric model of the multiple views from the object to be inspected (a baggage), and 'parts detection', to detect the parts of interest (prohibited items). The geometric model is estimated using a structure from motion algorithm. The detection of the parts of interest is performed by an adhoc segmentation algorithm (object dependent) followed by a general tracking algorithm based on geometric and appearance constraints. In order to illustrate the effectiveness of the proposed method, experimental results on detecting regular objects −razor blades and guns− are shown yielding promising results.
Identifying explosives in baggage at airports relies on being able to characterize the materials that make up an X-ray image. If a suspicion is generated during the imaging process (step 1), the image data could be enhanced by adapting the scanning parameters (step 2). This paper addresses the first part of this problem and uses textural signatures to recognize and characterize materials and hence enabling system control. Directional Gabor-type filtering was applied to a series of different X-ray images. Images were processed in such a way as to simulate a line scanning geometry. Based on our experiments with images of industrial standards and our own samples it was found that different materials could be characterized in terms of the frequency range and orientation of the filters. It was also found that the signal strength generated by the filters could be used as an indicator of visibility and optimum imaging conditions predicted.
Assessing Image Difficulty in X-Ray Screening Using Image Processing Algorithms
2006
The relevance of aviation security has increased dramatically in the last years. One of the most important tasks is the visual inspection of passenger bags using x-ray machines. In this study we investigated the role of the three image-based factors view difficulty, superposition and bag complexity on human detection of familiar prohibited items (knives) in x-ray images. In Experiment 1 we replicated earlier findings in order to provide converging evidence for the validity of these factors. In Experiment 2 we assessed the subjective perception of the same image-based factors. Twelve participants rated the x-ray images used in Experiment 1. Threat images were rated for view difficulty, superposition, clutter, transparency and general difficulty. Except for clutter ratings obtained in Experiment 2 were significantly correlated with detection performance in Experiment 1. We then developed statistical and imageprocessing algorithms to calculate the image-based factors automatically from x-ray images. In Experiment 3 it was revealed that most of our computer-generated estimates were well correlated with human ratings of image-based effects obtained in Experiment 2. This shows that our computer-based estimates of view difficulty, superposition, clutter and transparency are perceptually plausible. Using multiple regression analysis we could show in Experiment 4 that our computer estimates were able to predict human performance in Experiment 1 as well as the human ratings obtained in Experiment 2. Applications of such a computational model are discussed for threat image projection systems and for adaptive computer-based training.
Enhanced images for checked and carry-on baggage and cargo screening
Sensors, and Command, Control, Communications, and Intelligence (C3I) Technologies for Homeland Security and Homeland Defense III, 2004
The current X-ray systems used by airport security personnel for the detection of contraband, and objects such as knives and guns that can impact the security of a flight, have limited effect because of the limited display quality of the X-ray images. Since the displayed images do not possess optimal contrast and sharpness, it is possible for the security personnel to miss potentially hazardous objects. This problem is also common to other disciplines such as medical Xrays, and can be mitigated, to a large extent, by the use of state-of-the-art image processing techniques to enhance the contrast and sharpness of the displayed image. The NASA Langley Research Center's Visual Information Processing Group has developed an image enhancement technology that has direct applications to this problem of inadequate display quality. Airport security X-ray imaging systems would benefit considerably by using this novel technology, making the task of the personnel who have to interpret the X-ray images considerably easier, faster, and more reliable. This improvement would translate into more accurate screening as well as minimizing the screening time delays to airline passengers. This technology, Retinex, has been optimized for consumer applications but has been applied to medical X-rays on a very preliminary basis. The resultant technology could be incorporated into a new breed of commercial x-ray imaging systems which would be transparent to the screener yet allow them to see subtle detail much more easily, reducing the amount of time needed for screening while greatly increasing the effectiveness of contraband detection and thus public safety.