GDXray: The Database of X-ray Images for Nondestructive Testing

Mery, Domingo; Riffo, Vladimir; Zscherpel, Uwe; Mondragón, Germán; Lillo, Iván; Zuccar, Irene; Löbel, Hans; Carrasco, Miguel

doi:10.1007/s10921-015-0315-7

Cited by 296 publications

(202 citation statements)

References 24 publications

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“…But unfortunately, very few X-ray datasets have been published for research purposes. A recently released benchmark, GDXray [25], contains three major categories of prohibited items including gun, shuriken and razor blade. However, images in GDXray were provided with few background clutters as well as overlaps, thus, it becomes considerably easy to recognizing these images and/or detecting the objects within.…”

Section: X-ray Images and Benchmarksmentioning

confidence: 99%

“…To provide a public benchmark for research in this field, in this paper, we present a dataset named Security Inspection X-ray (SIXray), which is 100 times larger than the existing largest image collection for prohibited item discovery, i.e., the baggage group in the GDXray dataset [25]. SIXray contains more than one million X-ray images in which only less than 1% images have positive labels (i.e., prohibited items are annotated).…”

Section: Introductionmentioning

confidence: 99%

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SIXray: A Large-Scale Security Inspection X-Ray Benchmark for Prohibited Item Discovery in Overlapping Images

Miao

Xie

Wan

et al. 2019

2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

195

246

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In this paper, we present a large-scale dataset and establish a baseline for prohibited item discovery in Security Inspection X-ray images. Our dataset, named SIXray, consists of 1,059,231 X-ray images, in which 6 classes of 8,929 prohibited items are manually annotated. It raises a brand new challenge of overlapping image data, meanwhile shares the same properties with existing datasets, including complex yet meaningless contexts and class imbalance.

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Section: X-ray Images and Benchmarksmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

SIXray: A Large-Scale Security Inspection X-Ray Benchmark for Prohibited Item Discovery in Overlapping Images

Miao

Xie

Wan

et al. 2019

2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

195

246

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“…3). The images are coming from a publicly available database, namely GDXray (Mery et al, 2015;GDXray, 2015). The object to be detected is a razor blade.…”

Section: Real Imagesmentioning

confidence: 99%

Region Homogeneity in the Logarithmic Image Processing Framework: Application to Region Growing Algorithms

Noyel

Jourlin

2019

Image Anal Stereol

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In order to create an image segmentation method robust to lighting changes, two novel homogeneity criteria of an image region were studied. Both were defined using the Logarithmic Image Processing (LIP) framework whose laws model lighting changes. The first criterion estimates the LIP-additive homogeneity and is based on the LIP-additive law. It is theoretically insensitive to lighting changes caused by variations of the camera exposure-time or source intensity. The second, the LIP-multiplicative homogeneity criterion, is based on the LIP-multiplicative law and is insensitive to changes due to variations of the object thickness or opacity. Each criterion is then applied in Revol and Jourlin's (1997) region growing method which is based on the homogeneity of an image region. The region growing method becomes therefore robust to the lighting changes specific to each criterion. Experiments on simulated and on real images presenting lighting variations prove the robustness of the criteria to those variations. Compared to a state-of the art method based on the image component-tree, ours is more robust. These results open the way to numerous applications where the lighting is uncontrolled or partially controlled.

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“…Thereafter, the manipulated X-ray images are input to the architecture proposed in Section 3. An example of the suggested technique on single-energy X-ray images from 18 is presented in Figure 7. Despite the weapon being texture-less our enhancement strategy still afforded disjoining the weapon from the clutter object.…”

Section: High Imentioning

confidence: 99%

Automatic X-ray image segmentation and clustering for threat detection

Kechagias‐Stamatis

Aouf

Nam

et al. 2017

Target and Background Signatures III

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Firearms currently pose a known risk at the borders. The enormous number of X-ray images from parcels, luggage and freight coming into each country via rail, aviation and maritime presents a continual challenge to screening officers. To further improve UK capability and aid officers in their search for firearms we suggest an automated object segmentation and clustering architecture to focus officers' attentions to high-risk threat objects. Our proposal utilizes dual-view single/ dual-energy 2D X-ray imagery and is a blend of radiology, image processing and computer vision concepts. It consists of a triple-layered processing scheme that supports segmenting the luggage contents based on the effective atomic number of each object, which is then followed by a dual-layered clustering procedure. The latter comprises of mild and a hard clustering phase. The former is based on a number of morphological operations obtained from the image-processing domain and aims at disjoining mild-connected objects and to filter noise. The hard clustering phase exploits local feature matching techniques obtained from the computer vision domain, aiming at sub-clustering the clusters obtained from the mild clustering stage. Evaluation on highly challenging single and dual-energy X-ray imagery reveals the architecture's promising performance.

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GDXray: The Database of X-ray Images for Nondestructive Testing

Cited by 296 publications

References 24 publications

SIXray: A Large-Scale Security Inspection X-Ray Benchmark for Prohibited Item Discovery in Overlapping Images

SIXray: A Large-Scale Security Inspection X-Ray Benchmark for Prohibited Item Discovery in Overlapping Images

Region Homogeneity in the Logarithmic Image Processing Framework: Application to Region Growing Algorithms

Automatic X-ray image segmentation and clustering for threat detection

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