Passive Millimeter Wave Imaging

Yujiri, L.

doi:10.1109/mwsym.2006.249938

Cited by 127 publications

(109 citation statements)

References 5 publications

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“…Since radiation at these frequencies is nonionizing, it is considered to be safe for human exposure. Imaging using mmW has gained the interest of the security community [3], [12], [10], mainly due to its low intrusiveness and the ability to pass through clothing and other atmospheric obscurants such as cloud cover, fog, smoke, rain and dust storms. The predominant application of mmW images in the literature has been concealed weapon detection (CWD) or contraband.…”

Section: Introductionmentioning

confidence: 99%

Millimetre wave person recognition: Hand-crafted vs learned features

Gonzalez-Sosa

Vera-Rodríguez

Fiérrez

et al. 2017

2017 IEEE International Conference on Identity, Security and Behavior Analysis (ISBA)

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Esta es la versión de autor de la comunicación de congreso publicada en: This is an author produced version of a paper published in: AbstractImaging using millimeter waves (mmWs) has many advantages including ability to penetrate obscurants such as clothes and polymers. Although conceal weapon detection has been the predominant mmW imaging application, in this paper, we aim to gain some insight about the potential of using mmW images for person recognition. We report experimental results using the mmW TNO database consisting of 50 individuals based on both hand-crafted and learned features from Alexnet and VGG-face pretrained CNN models. Results suggest that: i) mmW torso region is more discriminative than mmW face and the entire body, ii) CNN features produce better results compared to hand-crafted features on mmW faces and the entire body, and iii) hand-crafted features slightly outperform CNN features on mmW torso.

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Section: Introductionmentioning

confidence: 99%

Millimetre wave person recognition: Hand-crafted vs learned features

Gonzalez-Sosa

Vera-Rodríguez

Fiérrez

et al. 2017

2017 IEEE International Conference on Identity, Security and Behavior Analysis (ISBA)

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“…A novel technique of endoscopy called wireless capsule endoscopy (WCE) was introduced in 2000 to diagnose GI tract cancers [7][8][9]. It consists of a camera with a short focal length, light source, and radio transmitter.…”

Section: Introductionmentioning

confidence: 99%

A New Method of Stomach Tumor Recognition Based on Ultra Wideband Capsule Endoscopy

Chen¹,

Zhang²

2017

PIER C

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Abstract-In this paper, a new kind of capsule endoscopy with through-body radar is utilized for the first time. Finite difference time domain (FDTD) method is used to establish an electromagnetic simulation model of stomach. A technique based on the combination of improved back-projection (BP) algorithm and support vector machine (SVM) is proposed to solve the problems of rapidly recognizing tumor shapes in the stomach. In this technique, imaging data can be obtained using the improved BP algorithm and are classified by the SVM. The algorithm must consider the influence of various tissues in the human body: the attenuation of the signal strength of electromagnetic waves, the decrease in speed and the refraction due to the different permittivity between the different organs of the body. These factors will eventually lead to image offset, and even generate a virtual image. It is effective to refrain the displacement of image with modifying the time element of the imaging algorithm by iteration. Simulation results based on data from the model verify its feasibility and validity. Results further demonstrate that the resolution is extremely high. Tumor shapes, which have different sizes, positions, and quantities, can be reconstructed using this approach. When the data are contaminated by noises, the tumor shape in the stomach can still be suitably predicted, which demonstrates the robustness of the method. Finally, classification accuracy analysis for different sampling distances and sampling intervals shows that the effects of changing the distance and intervals on shape recognition are limited. The classification accuracy can also be improved by decreasing the sampling intervals or increasing the sampling distance.

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“…Millimeter wave (MMW) imaging has been widely adopted in security and military applications [1][2][3][4][5][6]. Passive MMW imaging can generate interpretable images under low visibility conditions such as fog, rain, dust, and smoke.…”

Section: Introductionmentioning

confidence: 99%

“…The imaging of the concealed object is accomplished by radiometric temperature differences. The effective radiometric temperature depends on the emissivity and reflectivity as well as physical temperatures [1]. Unfortunately, the image quality in such applications is often degraded by diffraction limit and low signal-to-noise ratio (SNR).…”

Section: Introductionmentioning

confidence: 99%

Shape Feature Analysis of Concealed Objects With Passive Millimeter Wave Imaging

Yeom¹,

Lee²,

Son³

2015

PIER Letters

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Abstract-Millimeter wave (MMW) imaging has found rapid adoption in security applications such as concealed object detection under clothing. However, the imaging quality is often degraded due to resolution limit and low signal level. This study addresses shape feature analysis following concealed object detection. The object region is extracted by multi-level segmentation. Shape features are composed of several descriptors which are object area, perimeter, major and minor axes of the basic rectangle, rectangularity, compactness, and eccentricity. In the experiments, three objects (gun, hand ax, and plastic bottle containing liquid skin aid) concealed under clothing are captured by the passive MMW imaging system. The extracted shape features are compared with the true features from the object model showing good accuracy.

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Passive Millimeter Wave Imaging

Cited by 127 publications

References 5 publications

Millimetre wave person recognition: Hand-crafted vs learned features

Millimetre wave person recognition: Hand-crafted vs learned features

A New Method of Stomach Tumor Recognition Based on Ultra Wideband Capsule Endoscopy

Shape Feature Analysis of Concealed Objects With Passive Millimeter Wave Imaging

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