Thermal imaging for law enforcement and security: post 9-11

Francisco, Glen; Billups, Ramsey B.; DeHorn, Tom; Anstett, F; O'Hara, Michael J.; Fisher, David

doi:10.1117/12.501084

Cited by 2 publications

(2 citation statements)

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“…Note that joint optimization is beyond the scope of this paper and our ultimate goal is to reduce MSEI value as minimum as possible in a cascaded manner. Besides important works reported and cited in the literature extensively (more than 100 times) [7][8][9][10][11], Reference [6] provides the interpretation of vital parameters in Tab. 1 and selection of optimum values in a clear and concise manner.…”

Section: Parameter Interpretation and Selection Of Optimum Values Formentioning

confidence: 99%

Priority and significance analysis of selecting threshold values in Adaptive Unsharp Masking for infrared images

Jane

İlk

2010

15th Conference on Microwave Techniques COMITE 2010

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Infrared image processing has been the focal point of considerable research activity in the last decade mainly because of its wide application areas in security and defense. With the aid of an existing image enhancement technique, Adaptive Unsharp Masking (AUM), proposed optimum parameters selection procedure delivers better performance in sharpness and contrast adjustment for the detection of targets in interest in objective quality metrics. In our study, we focus on the lower and upper limit threshold parameters which classify the contrast areas and therefore have significant effect on sharpening the edges for two different infrared (IR) images. The experimental results prove that changes of convergence rate of adaptive filter and positive convergence parameter versus mean square error (MSE) exhibit similar characteristics under certain lower and upper threshold values which satisfy "minimum" MSE; that is these threshold values are the most dominant criterion amongst the other parameters and they are significant. KeywordsAdaptive filtering, surveillance applications, image enhancement, Gauss-Newton algorithm, minimum mean square error.

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Section: Parameter Interpretation and Selection Of Optimum Values Formentioning

confidence: 99%

Priority and significance analysis of selecting threshold values in Adaptive Unsharp Masking for infrared images

Jane

İlk

2010

15th Conference on Microwave Techniques COMITE 2010

View full text Add to dashboard Cite

show abstract

“…Although the algorithm might be seen trivial at first glance, it is well reported and cited in the literature extensively (more than 100 times) [3,14]. This important work however, lacks the use of edge detection and discussion of adaptive filters on the output image.…”

Section: Introductionmentioning

confidence: 99%