&lt;title&gt;Morphological filters and wavelet-based image fusion for concealed weapons detection&lt;/title&gt;

Ramac, Liane C.; Uner, Mucahit K.; Varshney, Pramod K.; Alford, Mark; Ferris, David D.

doi:10.1117/12.303671

Cited by 32 publications

(17 citation statements)

References 5 publications

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“…Toet [17] has explained ratio of low pass pyramid .Anderson [18] has explained filtersubtract-decimate hierarchical pyramid. Ramac [19] has explained Morphological filters pyramid with the help of opening and closing operations. Image closing enhances the sharpness of the edges of the objects in the images but at the same time it degrades the overall quality of the image.…”

Section: Pyramid Based Fusionmentioning

confidence: 99%

“…Fusion using Gradient Pyramid [16] Fusion using Ratio of Low Pass Pyramid [17] Fusion using Filter Subtract Decimate(FSD) Pyramid [18] Fusion using Morphological Pyramid [19] Discrete Transform Based Fusion Discrete Cosine Transform (DCT) Method [21] It reduces the complexity and decomposed images into series of waveform. This algorithm can be used for real applications,…”

Section: International Journal Of Computer Applications (0975 -8887)mentioning

confidence: 99%

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Image Fusion Techniques: A Review

Mishra¹,

Palkar²

2015

IJCA

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Image Fusion is used to retrieve important data from a set of input images and put it into a single output image to make it more informative and useful than any of the input images. It improves quality and applicability of data. Quality of the fused image depends on the application. Image fusion is widely used in intelligent robots, stereo camera fusion, medical imaging, and manufacture process monitoring, electronic circuit design and inspection, complex machine/device diagnostics and in intelligent robots on assembly lines. This paper presents a literature review on various spatial and frequency domain image fusion techniques such as averaging, min-max, block replace, HIS,PCA, brovey, pyramid based and transform based techniques. Various quality measures have been discussed to perform quantitative comparison of these methods.

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Section: Pyramid Based Fusionmentioning

confidence: 99%

Section: International Journal Of Computer Applications (0975 -8887)mentioning

confidence: 99%

Image Fusion Techniques: A Review

Mishra¹,

Palkar²

2015

IJCA

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“…This is because input ''noise'' may be treated by the fusion system as valid information and transferred to the fused output image. Furthermore input image noise may affect the selection/fusion process of the MSL-IF system in a way that introduces additional unwanted artefacts and distortion into the fused image [1][2][3][4][5][6].…”

Section: Figure 2 Multisensor Image Fusion Systemsmentioning

confidence: 99%

Performance Degraded by the Sensor Noise at Pixel Level Image Fusion

Jayanth¹,

Koliwad²

2010

IJCA

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Remote sensing is defined as obtaining information about a Performance metrics for measuring absolute degradation and their gain in fused image quality are proposed when fusing noisy input modalities. This considers fusion of noise patterns, is also developed and used to evaluate the perceptual effect of noise corrupting homogenous image regions (i.e. areas with no salient features). These metrics are employed to compare the performance of different image fusion methodologies and feature selection/information fusion strategies operating under noisy input conditions. The aim of this paper is to define appropriate metrics which measure the effects of input sensor noise on the performance of image fusion systems.' noisy fusion'' metrics are developed and used, in the first two scenarios, to measure the effects of additive sensor noise on the performance of several signal-level image fusion algorithms operating across a range of input signal-to-noise ratio (SNR) values.

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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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<title>Morphological filters and wavelet-based image fusion for concealed weapons detection</title>

Cited by 32 publications

References 5 publications

Image Fusion Techniques: A Review

Image Fusion Techniques: A Review

Performance Degraded by the Sensor Noise at Pixel Level Image Fusion

Color image fusion for concealed weapon detection

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