Small craft identification discrimination criteria N 50 and V 50 for visible and infrared sensors in maritime security

Krapels, Keith A.; Deaver, Dawne M.; Driggers, Ronald G.

doi:10.1117/12.689140

Cited by 5 publications

(7 citation statements)

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“…In this technique the 8-bit greyscale imagery was contrast stretched to 12-bit greyscale imagery. 7 The correlation results for the 12-bit greyscale imagery is given in Figure 11, showing a significant increase in correlation from the 8-bit imagery for the RSS and the line RSS metrics. However, a significant decrease in the correlation was found for the PC, line PC and PPC metrics using 12-bit greyscale imagery.…”

Section: Discussionmentioning

confidence: 95%

“…For use in the assessment of visible imagery the RSS metric assumes that the greyscale imagery has been contrast stretched to permit 5% saturation at the lowest and highest levels. 7 This contrast stretching is performed for the visible imagery to obtain the same order of magnitude in the visible contrast calculation as the temperature difference calculations for IR sensors, provided the value of the RSS metric is normalised by twice the scene mean (Section 3.1.6). To allow direct comparison to previous studies, the 8-bit greyscale imagery was contrast stretched to 12-bit greyscale imagery.…”

Section: Contrast Stretchingmentioning

confidence: 99%

“…This normalisation is accomplished by dividing the metric value by twice the scene mean (µ scene ). 7,8 The source contrast (C src ) is defined as:…”

Section: Contrast Normalisationmentioning

confidence: 99%

“…7 The final method was to approximate a V 50 value for water craft recognition. The V 50 value for recognition has been reported to be approximately half the V 50 value for identification for a land vehicle.…”

Section: Task Discrimination Criteriamentioning

confidence: 99%

“…These developments have focused mainly on the performance of visible imaging systems on small targets such as swimmers 6 and small water craft. 7 The TTP model, as presented up until now in the literature, does not model the performance of the human eye in field observations. This paper describes the adaptation of the TTP model to assess the performance of the unaided human eye in field observations.…”

Section: Introductionmentioning

confidence: 99%

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A target detection model predicting field observer performance in maritime scenes

Culpepper

Wheaton

2014

SPIE Proceedings

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The U.S. Army's target acquisition models, the ACQUIRE and Target Task Performance (TTP) models, have been employed for many years to assess the performance of thermal infrared sensors. In recent years, ACQUIRE and the TTP models have been adapted to assess the performance of visible sensors. These adaptations have been primarily focused on the performance of an observer viewing a display device. This paper describes an implementation of the TTP model to predict field observer performance in maritime scenes.Predictions of the TTP model implementation were compared to observations of a small watercraft taken in a field trial. In this field trial 11 Australian Navy observers viewed a small watercraft in an open ocean scene. Comparisons of the observed probability of detection to predictions of the TTP model implementation showed the normalised RSS metric overestimated the probability of detection. The normalised Pixel Contrast using a literature value for V 50 yielded a correlation of 0.58 between the predicted and observed probability of detection. With a measured value of N 50 or V 50 for the small watercraft used in this investigation, this implementation of the TTP model may yield stronger correlation with observed probability of detection.

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Section: Discussionmentioning

confidence: 95%

Section: Contrast Stretchingmentioning

confidence: 99%

“…This normalisation is accomplished by dividing the metric value by twice the scene mean (µ scene ). 7,8 The source contrast (C src ) is defined as:…”

Section: Contrast Normalisationmentioning

confidence: 99%

Section: Task Discrimination Criteriamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A target detection model predicting field observer performance in maritime scenes

Culpepper

Wheaton

2014

SPIE Proceedings

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Texture metric that predicts target detection performance

Culpepper

2015

Opt. Eng

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A search of the research on soldier-in-the-loop target acquisition performance modeling of infrared imaging system

Zhang

Li³

et al. 2009

SPIE Proceedings

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Predicting the performance of target acquisition of human-vision-based infrared imaging system is one research domain spanning multi-subject. From the point view of building model, the information processing mechanism of human eyes and the clutter metric are the critical factors for the development of the target acquisition model. The issues involved in the development of the model and the means of settlement are also introduced, while the improvement of the target acquisition model is also discussed. Finally, the concept of 'performance model' is explained, which lays a foundation for building targeting task performance model according to our own applying circumstances.

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Small craft identification discrimination criteria N 50 and V 50 for visible and infrared sensors in maritime security

Cited by 5 publications

References 0 publications

A target detection model predicting field observer performance in maritime scenes

A target detection model predicting field observer performance in maritime scenes

Texture metric that predicts target detection performance

A search of the research on soldier-in-the-loop target acquisition performance modeling of infrared imaging system

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