Enhancement and fusion of data for concealed weapon detection

Slamani, M.; Ramac, Liane C.; Uner, Mucahit K.; Varshney, Pramod K.; Weiner, D.D.; Alford, Mark; Ferris, David D.; Vannicola, Vincent C.

doi:10.1117/12.280804

Cited by 19 publications

(10 citation statements)

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“…Almost all image fusion algorithms, from the simplest weighted averaging to more advanced multiscale methods, belong to this level [2] . Currently, pixel-level image fusion systems are used extensively in the fields of military [3][4] , medical imaging [5][6] , remote sensing [7] , and security and surveillance [8] . The benefits of image fusion include improved spatial awareness, increased accuracy in target detection and recognition, reduced operator workload, and increased system reliability [9][10][11] .…”

Section: Introductionmentioning

confidence: 99%

Review of pixel-level image fusion

Yang

Jing

Zhao

2010

J. Shanghai Jiaotong Univ. (Sci.)

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Image fusion can be performed at different levels: signal, pixel, feature and symbol levels. Almost all image fusion algorithms developed to date fall into pixel level. This paper provides an overview of the most widely used pixel-level image fusion algorithms and some comments about their relative strengths and weaknesses. Particular emphasis is placed on multiscale-based methods. Some performance measures practicable for pixel-level image fusion are also discussed. At last, prospects of pixel-level image fusion are made.

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Section: Introductionmentioning

confidence: 99%

Review of pixel-level image fusion

Yang

Jing

Zhao

2010

J. Shanghai Jiaotong Univ. (Sci.)

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“…The obtained fused image is more useful for human v isual perception and further image processing operations [1]. Image fusion has been used in many d isparate fields such as, medical imaging [2,3], military [4], surveillance [5], robotics and remote sensing [6], etc.…”

Section: Introductionmentioning

confidence: 99%

Discrete Wavelet Transform and Cross Bilateral Filter based Image Fusion

Sonam¹,

Kumar²

2017

IJISA

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Abstract-The main object ive of image fusion is to obtain an enhanced image with more relevant informat ion by integrating complimentary information fro m two source images. In this paper, a novel image fusion algorith m based on discrete wavelet transform (DWT) and cross bilateral filter (CBF) is proposed. In the proposed framewo rk, source images are decomposed into low and high frequency subbands using DWT. The low frequency subbands of the transformed images are combined using pixel averag ing method. Meanwhile, the high frequency subbands of the transformed images are fused with weighted average fusion ru le where, the weights are co mputed using CBF on both the images . Finally, to reconstruct the fused image inverse DWT is performed over the fused coefficients. The proposed method has been extensively tested on several pairs of mu lti-focus and multisensor images. To compare the results of proposed method with d ifferent existing methods, a variety of image fusion quality metrics are emp loyed for the qualitative measurement. The analysis of co mparison results demonstrates that the proposed method exhibits better results than many other fusion methods, qualitatively as well as quantitatively.

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“…No single sensor will completely satisfy the concealed weapon detection mission. Sensor fusion is an enabling technology that may increase the sensitivity, and reduce the number of false alarms and clutter by combining the signals of two or more sensors of different and complementary modalities [3][4][5]8,11,18 . Here we propose a new image fusion scheme that produces fused color images that appear similar to standard color images.…”

Section: Introductionmentioning

confidence: 99%

Color image fusion for concealed weapon detection

Toet

2003

SPIE Proceedings

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Recent advances in passive and active imaging sensor technology offer the potential to detect weapons that are concealed underneath a person's clothing or carried along in bags. Although the concealed weapons can sometimes easily be detected, it can be difficult to perceive their context, due to the non-literal nature of these images. Especially for dynamic crowd surveillance purposes it may be impossible to rapidly asses with certainty which individual in the crowd is the one carrying the observed weapon. Sensor fusion is an enabling technology that may be used to solve this problem. Through fusion the signal of the sensor that depicts the weapon can be displayed in the context provided by a sensor of a different modality. We propose an image fusion scheme in which non-literal imagery can be fused with standard color images such that the result clearly displays the observed weapons in the context of the original color image. The procedure is such that the relevant contrast details from the non-literal image are transferred to the color image without altering the original color distribution of this image. The result is a natural looking color image that fluently combines all details from both input sources. When an observer who performs a dynamic crowd surveillance task, detects a weapon in the scene, he will also be able to quickly determine which person in the crowd is actually carrying the observed weapon (e.g. "the man with the red T-shirt and blue jeans"). The method is illustrated by the fusion of thermal 8-12 µm imagery with standard RGB color images.

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Enhancement and fusion of data for concealed weapon detection

Cited by 19 publications

References 0 publications

Review of pixel-level image fusion

Review of pixel-level image fusion

Discrete Wavelet Transform and Cross Bilateral Filter based Image Fusion

Color image fusion for concealed weapon detection

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