Detection probability using relative clutter in infrared images

Meitzler, Thomas; Gerhart, Grant R.; Sohn, Euijung; Singh, Harpreet

doi:10.1109/7.705901

Cited by 21 publications

(10 citation statements)

References 11 publications

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“…[1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20] In order to most effectively apply the MTTV metric, it is important to understand how the metric responds under a variety of conditions. [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20] In order to most effectively apply the MTTV metric, it is important to understand how the metric responds under a variety of conditions.…”

Section: Resultsmentioning

confidence: 99%

“…[3][4][5][6][7][8][9][10][11][12] Recently, the masked target transform volume (MTTV) metric has provided promising results that overcome some limitations of earlier metrics. [3][4][5][6][7][8][9][10][11][12] Recently, the masked target transform volume (MTTV) metric has provided promising results that overcome some limitations of earlier metrics.…”

Section: Introductionmentioning

confidence: 99%

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Performance characteristics of the masked target transform volume clutter metric

Camp¹,

Moyer

Moore

2012

Opt. Eng

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Prior work has shown that the masked target transform volume (MTTV) clutter metric provides a measure of scene clutter that better correlates to measured probability of detection for human observers than several previously published clutter metrics. Several factors involved in using the MTTV to assess clutter in imagery are discussed here. A previously published modification to the MTTV metric to provide a normalized output value comparable across different image sets regardless of scene size is reviewed. Initial MTTV development required knowledge of a scene's target signature and produced an unbounded metric value. Metric behavior is discussed for the case in which an average of several target signatures is used in place of a specific target signature. This allows the MTTV to be calculated for images that do not contain a target. It is shown that the user may trade computational efficiency with metric accuracy to suit a particular application. The sensitivity of the metric to variations in image noise level, target segmentation error, and viewing distance are also presented.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Performance characteristics of the masked target transform volume clutter metric

Camp¹,

Moyer

Moore

2012

Opt. Eng

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“…Wavelet derived edge points were used by the authors in the model discussed in this paper as such a metric, but, there are others that could be used. Additionally, if a relative metric is used as one of the input vectors, a determination could be made as to which of the metrics is contributing the most weight to the final detection probability value [17].…”

Section: Resultsmentioning

confidence: 99%

Predicting search time in visually cluttered scenes using the fuzzy logic approach

Meitzler¹,

Sohn²,

Singh

et al. 2001

Opt. Eng

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The mean search time of observers searching for targets in visual scenes with clutter is computed using the Fuzzy Logic Approach (FLA). The FLA is presented as a robust method for the computation of search times and or probabilities of detection for signature management decisions. The Mamdani/Assilian and Sugeno models have been investigated and are compared. The Search_2 data set from TNO is used to build and validate the fuzzy logic model for detection. The input parameters are the: local luminance, range, aspect, width, wavelet edge points and the single output is search time. The Mamdani/Assilian model gave predicted mean search times for data not used in the training set that had a 0.957 correlation to the field search times. The data set is reduced using a clustering method then modeled using the FLA and results are compared to experiment. Report Documentation Page Form Approved OMB No. 0704-0188Public reporting burden for the collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing this burden, to Washington Headquarters Services, Directorate for Information Operations and Reports, 1215 Jefferson Davis Highway, Suite 1204, Arlington VA 22202-4302. Respondents should be aware that notwithstanding any other provision of law, no person shall be subject to a penalty for failing to comply with a collection of information if it does not display a currently valid OMB control number.

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“…There are so many researches on its improved algorithms in recent years [2][3][4] . In this paper, genetic algorithm is applied to target detection field and a new fitness measurement method based on target characteristic value is proposed according to concrete requirements.…”

Section: Improvement Of Genetic Algorithmmentioning

confidence: 99%

Research on adaptive target detection based on improved genetic algorithm from infrared images

Shao

Zhu

Liu

2010

2010 Seventh International Conference on Fuzzy Systems and Knowledge Discovery

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In this paper, a novel optimized genetic algorithm based on morphology for target detection from infrared images is proposed. In our improved algorithm, a new fitness measurement method based on target characteristics value is introduced to meet specific target detection needs. Male and female parent dynamic clustering methods are put forward to make crossover operator more reasonable. Besides, multi-population parallel evolution and optimum individual transplant strategy are adopted to merge optimum individual keeping and gene keeping effectively. Crossover probability and mutation probability are adjusted adaptively according to population diversity and more reasonable target characteristics variable is designed according to the features of infrared images. Morphology filter based on genetic optimization for infrared target detection is given to recognize structural information and target background information. Experimental results demonstrate that the convergence speed can be controlled and local search ability is increased as well by using trained structural elements based on improved genetic optimum. In addition, the efficiency and accuracy is boosted evidently and noise can be restrained to a great extent.

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Detection probability using relative clutter in infrared images

Cited by 21 publications

References 11 publications

Performance characteristics of the masked target transform volume clutter metric

Performance characteristics of the masked target transform volume clutter metric

Predicting search time in visually cluttered scenes using the fuzzy logic approach

Research on adaptive target detection based on improved genetic algorithm from infrared images

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