Ms. Joanne B. Culpepper Profile

Joanne B. Culpepper

at Defence Science and Technology Group

SPIE Involvement:

Author

Proceedings Article | 23 October 2023 Presentation + Paper

Machine learning approach for extracting radiometric data from RGB images: a preliminary study

Joanne Culpepper, Tauseef Gulrez

Proceedings Volume 12736, 127360G (2023) https://doi.org/10.1117/12.2685352

KEYWORDS: RGB color model, Cameras, Reflectivity, Data modeling, Color, Machine learning, Light sources and illumination, Spectral response

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Proceedings Article | 2 November 2022 Presentation + Paper

Validating colour representation in synthetic scenes using a virtual colour checker chart

Q. T. Shao, Noel Richards, Ryan Messina, Neal Winter, Joanne Culpepper

Proceedings Volume 12270, 1227007 (2022) https://doi.org/10.1117/12.2638442

KEYWORDS: Image processing, Cameras, Reflectivity, Calibration, Photography, Digital photography

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Proceedings Article | 12 September 2021 Presentation + Paper

The relation between visual search and visual conspicuity for moving targets

Erik Van der Burg, Jay Yu, Maarten Hogervorst, Bin Lee, Joanne Culpepper, Alexander Toet

Proceedings Volume 11865, 118650C (2021) https://doi.org/10.1117/12.2600307

KEYWORDS: Target detection, Camouflage, Target recognition

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Proceedings Article | 12 September 2021 Presentation + Paper

Field observation, photosimulation and videosimulation of target detection in maritime environments: update

Ryan Messina, Joanne Culpepper

Proceedings Volume 11865, 118650D (2021) https://doi.org/10.1117/12.2602096

KEYWORDS: Target detection, Video, Eye, Environmental sensing, Visual process modeling, Target recognition, Digital photography, Cameras, Target acquisition, Light sources and illumination

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Proceedings Article | 20 September 2020 Presentation + Paper

Comparison of maritime target detection in field observation, photosimulation and videosimulation

Joanne Culpepper, Ryan Messina, Vivienne Wheaton

Proceedings Volume 11536, 1153605 (2020) https://doi.org/10.1117/12.2574358

KEYWORDS: Target detection, Video, Environmental sensing, Defense and security, Camouflage, Time metrology, Eye, Time series analysis

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Proceedings Article | 17 October 2019 Paper

Comparison of land vehicle target detection performance in field observation, photo simulation and video simulation

Ryan Messina, Vivienne Wheaton, Alaster Meehan, Joanne Culpepper

Proceedings Volume 11158, 111580M (2019) https://doi.org/10.1117/12.2538343

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Photo-simulation is a widely used method for target detection experimentation. In the defence context, such experiments are often developed in order to derive measures for the effectiveness of camouflage techniques in the field. This assumes that there is a strong link between photo-simulation performance and field performance. In this paper, we report on a three-stage experiment exploring that link. First, a field experiment was conducted where observers performed a search and detection task seeking vehicles in a natural environment, simultaneously with image and video capture of the scene. Next, the still images were used in a photo-simulation experiment, followed by a video-simulation experiment using the captured video. Analysis of the photo-simulation results has shown that there is moderate linear correlation between field and photo-simulation detection results (Pearson Correlation Coefficient, PCC = 0.64), but photo-simulation results only moderately fit the field observation results, with a reduced χ2 statistic of 1.996. Detectability of targets in the field was, mostly, slightly higher than in photo-simulation. Analysis of the video-simulation results using videos of stationary and moving targets has also shown moderate correlation with field observation results (PCC = 0.62), but these are a better fit with the field observation results, with a reduced x2 statistic of 1.45. However, when considering videos of moving targets and videos of stationary targets separately, there appear to be two distinct trends, with video-simulation detection results being routinely higher for moving targets than the field observation results, while for stationary targets, the video-simulation detection results are mostly lower than the field observation, similar to the trend noted in the photo-simulation results. There were too few moving target videos to confidently perform a fit, but the fit statistics for the stationary target videos becomes similar to that of the photo-simulation, having a reduced χ2 = 1.897.

Proceedings Article | 9 October 2018 Presentation + Paper

Methods for measuring time to detect in human observer trials

Joanne Culpepper, Vivienne Wheaton, Christopher Madden, Luke Simmonds

Proceedings Volume 10794, 107940C (2018) https://doi.org/10.1117/12.2325057

KEYWORDS: Video, Target detection, Sensors, Camouflage, Digital photography

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Evaluating the signature of operational platforms has long been a focus of military research. Human observations of targets in the field are perceived to be the most accurate way to assess a target's visible signature, although the results are limited to observers present in the field. Field observations do not introduce image capture or display artefacts, nor are they completely static, like the photographs used in screen based human observation experiments. A number of papers provide advances in the use of photographs and imagery to estimate the detectability of military platforms; however few describe advances in conducting human observer field trials.

This paper describes the conduct of a set of human field observation trials for detecting small maritime crafts in a littoral setting. This trial was conducted from the East Arm Port in Darwin in February 2018 with up to 6 observers at a time and was used to investigate incremental improvements to the observation process compared to small craft trials conducted in 2013. This location features a high number of potential distractors, which make it more difficult to find the small target crafts. The experimental changes aimed to test ways to measure time to detect, a result not measured at the previous small craft detection experiment, through the use of video monitoring of the observation line to compare with the use of observer-operated stop watches. This experiment also included the occasional addition of multiple targets of interest in the field of regard. Initial analysis of time-to-detect data indicates the video process may accurately assess the time to detect targets by the observers, but only if observers are effectively trained. Ideas on how to further automate the process for the human observer task are also described; however this system has yet to be implemented. This improved human observer trial process will assist the development of signature assessment models by obtaining more accurate data from field trials, including targets moving through a dynamic scene.

Proceedings Article | 5 October 2017 Paper

Comparing synthetic imagery with real imagery for visible signature analysis: human observer results

Joanne Culpepper, Noel Richards, Christopher Madden, Neal Winter, Vivienne Wheaton

Proceedings Volume 10432, 104320H (2017) https://doi.org/10.1117/12.2277580

KEYWORDS: Target detection, 3D acquisition, 3D image processing, 3D modeling, Visualization, Digital photography, Vegetation, Visual analytics, Photography, Airborne remote sensing

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Proceedings Article | 5 October 2017 Paper

Mirage: a visible signature evaluation tool

Joanne Culpepper, Alaster Meehan, Q. Shao, Noel Richards

Proceedings Volume 10432, 104320G (2017) https://doi.org/10.1117/12.2277555

KEYWORDS: Target detection, Modeling, Machine learning, Camouflage, Photography, Environmental sensing, Probability theory, Visualization, Computer simulations, Image analysis

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SPIE Journal Paper | 29 November 2016

Clutter estimation and perception

Alaster Meehan, Joanne Culpepper

OE, Vol. 55, Issue 11, 113106, (November 2016) https://doi.org/10.1117/12.10.1117/1.OE.55.11.113106

KEYWORDS: Edge detection, Image segmentation, Visualization, Wavelets, Target detection, Optical engineering, Image processing algorithms and systems, Error analysis, Image processing, Photography

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Proceedings Article | 24 October 2016 Presentation + Paper

Utilising E-on Vue and Unity 3D scenes to generate synthetic images and videos for visible signature analysis

Christopher Madden, Noel Richards, Joanne Culpepper

Proceedings Volume 9997, 99970P (2016) https://doi.org/10.1117/12.2240811

KEYWORDS: Target acquisition, Target detection, 3D image processing, Received signal strength, 3D acquisition, Video, Photography, Light sources and illumination, 3D modeling, Clouds

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SPIE Journal Paper | 8 December 2015

Texture metric that predicts target detection performance

Joanne Culpepper

OE, Vol. 54, Issue 12, 123101, (December 2015) https://doi.org/10.1117/12.10.1117/1.OE.54.12.123101

KEYWORDS: MATLAB, Target detection, Received signal strength, Visualization, Error analysis, Optical engineering, Image segmentation, Image resolution, Matrices, Image analysis

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Proceedings Article | 21 October 2015 Paper

Performance of target distinctness metrics evaluated against colour and monochromatic photosimulation results

Vivienne Wheaton, Joanne Culpepper

Proceedings Volume 9653, 96530U (2015) https://doi.org/10.1117/12.2194229

KEYWORDS: Solids, Target detection, Data modeling, Image processing, Image analysis, Environmental sensing, Projection systems, Image resolution, Visualization, Error analysis

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Proceedings Article | 21 October 2015 Paper

A comparison between maritime field observations and photosimulation for developing and validating visible signature evaluation tools

Joanne Culpepper, Vivienne Wheaton, Q. Shao, Alistair Furnell

Proceedings Volume 9653, 96530V (2015) https://doi.org/10.1117/12.2193558

KEYWORDS: Visualization, Visual process modeling, Data modeling, Target detection, Plasma display panels, Targeting Task Performance metric, Head, Palladium, Performance modeling, Video

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Proceedings Article | 7 October 2014 Paper

A target detection model predicting field observer performance in maritime scenes

Joanne Culpepper, Vivienne Wheaton

Proceedings Volume 9249, 92490C (2014) https://doi.org/10.1117/12.2066264

KEYWORDS: Targeting Task Performance metric, Received signal strength, Performance modeling, Palladium, Target detection, Eye, Systems modeling, Eye models, Contrast sensitivity, Visual process modeling

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Showing 5 of 15 publications

Conference Committee Involvement (8)

Target and Background Signatures IX

3 September 2023 | Amsterdam, Netherlands

Target and Background Signatures VIII

5 September 2022 | Berlin, Germany

Target and Background Signatures VII

13 September 2021 | Online Only, Spain

Target and Background Signatures VI

21 September 2020 | Online Only, United Kingdom

Target and Background Signatures V

9 September 2019 | Strasbourg, France

Showing 5 of 8 Conference Committees

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