Mr. Mark Bernhardt Profile

Mark Bernhardt

at Waterfall Solutions Ltd

SPIE Involvement:

Author

Publications (30)

Proceedings Article | 4 May 2010 Paper

Multi-modal panoramic imaging for security and surveillance applications

J. Sadler, D. Hickman, J. Davis, M. Bernhardt, T. Riley, A. Thomas, C. Dent

Proceedings Volume 7661, 76610B (2010) https://doi.org/10.1117/12.850016

KEYWORDS: Image fusion, Panoramic photography, Sensors, Image processing, Cameras, Imaging systems, Infrared imaging, Target detection, Image sensors, Thermography

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Proceedings Article | 4 May 2009 Paper

Adaptive processing for enhanced target acquisition

Scott Page, Moira Smith, Duncan Hickman, Mark Bernhardt, William Oxford, Norman Watson, F. Beath

Proceedings Volume 7338, 733807 (2009) https://doi.org/10.1117/12.818459

KEYWORDS: Sensors, Target acquisition, Target detection, Detection and tracking algorithms, Image processing, Image segmentation, Roads, Modeling, Mid-IR, Data modeling

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Proceedings Article | 15 April 2008 Paper

Next generation enhanced vision system processing

M. Bernhardt, C. Cowell, T. Riley

Proceedings Volume 6957, 695705 (2008) https://doi.org/10.1117/12.777053

KEYWORDS: Image enhancement, Image processing, Vignetting, Image quality, Image sensors, Detection and tracking algorithms, Sensors, Algorithm development, Enhanced vision, Cameras

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Proceedings Article | 11 April 2008 Paper

Atmospheric invariants for hyperspectral image correction

M. Bernhardt, W. Oxford

Proceedings Volume 6966, 69661A (2008) https://doi.org/10.1117/12.777060

KEYWORDS: Reflectivity, Atmospheric corrections, Transform theory, Hyperspectral imaging, Tolerancing, Databases, Atmospheric physics, Calibration, Atmospheric propagation, Data corrections

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Proceedings Article | 11 April 2008 Paper

Optimised autonomous search pattern evaluation using the Cerberus framework

C. Angell, M. Bernhardt

Proceedings Volume 6965, 696509 (2008) https://doi.org/10.1117/12.777359

KEYWORDS: Sensors, Detection and tracking algorithms, Algorithm development, Target detection, Computer simulations, Data fusion, Monte Carlo methods, Modeling, Sensor performance, Defense technologies

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Proceedings Article | 17 March 2008 Paper

Advances in image registration and fusion

Christopher Steer, Jeremy Rogers, Moira Smith, Jamie Heather, Mark Bernhardt, Duncan Hickman

Proceedings Volume 6974, 697404 (2008) https://doi.org/10.1117/12.777070

KEYWORDS: Image registration, 3D modeling, Sensors, Image fusion, Image sensors, Image processing, Data modeling, 3D image processing, Cameras, Image quality

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Proceedings Article | 24 October 2007 Paper

Hyperspectral clutter, phenomenology, and detection algorithms

C. Steer, M. Bernhardt

Proceedings Volume 6748, 67480F (2007) https://doi.org/10.1117/12.738024

KEYWORDS: Sensors, Vegetation, Detection and tracking algorithms, Target detection, Data modeling, Algorithm development, Hyperspectral imaging, Hyperspectral target detection, Image processing, Mahalanobis distance

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Proceedings Article | 8 October 2007 Paper

Implementing advanced image processing technology in sensor systems for security and surveillance

Tom Riley, Mark Bernhardt, Catherine Cowell, Duncan Hickman, Moira Smith

Proceedings Volume 6741, 67410W (2007) https://doi.org/10.1117/12.741855

KEYWORDS: Sensors, Image processing, Image sensors, Imaging systems, Cameras, Image enhancement, Nonuniformity corrections, Surveillance, Light sources and illumination, Detection and tracking algorithms

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Proceedings Article | 5 October 2007 Paper

Algorithms and evaluation framework for uninhabited vehicles

C. Angell, M. Bernhardt

Proceedings Volume 6736, 673607 (2007) https://doi.org/10.1117/12.738031

KEYWORDS: Sensors, Algorithm development, Detection and tracking algorithms, Monte Carlo methods, Computer simulations, Data fusion, Target detection, Systems engineering, Unattended ground sensors, Systems modeling

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Proceedings Article | 7 May 2007 Paper

Sonification of hyperspectral image data

Mark Bernhardt, Catherine Cowell, William Oxford

Proceedings Volume 6565, 65650D (2007) https://doi.org/10.1117/12.719348

KEYWORDS: Associative arrays, Hyperspectral imaging, Image analysis, Detection and tracking algorithms, Acoustics, Algorithm development, Ear, Analytical research, Sensors, Matrices

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Proceedings Article | 7 May 2007 Paper

A hyperspectral model for target detection

Mark Bernhardt, Phil Clare, Catherine Cowell, Moira Smith

Proceedings Volume 6565, 65650F (2007) https://doi.org/10.1117/12.719363

KEYWORDS: Data modeling, Sensors, Atmospheric modeling, Systems modeling, Reflectivity, Detection and tracking algorithms, Electro optical modeling, Performance modeling, Statistical modeling, Target detection

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Proceedings Article | 5 October 2006 Paper

Sensor data association for the Seawolf Mid-Life Update Programme: an update

Christopher Angell, Mark Bernhardt, Dilip Patel, Colin Wardell

Proceedings Volume 6395, 63950S (2006) https://doi.org/10.1117/12.689826

KEYWORDS: Sensors, Missiles, Electro optical modeling, Cameras, Radar, Data modeling, Image processing, Signal detection, Electro optics, Control systems

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Proceedings Article | 4 May 2006 Paper

Hyperspectral clutter statistics, generative models, and anomaly detection

Mark Bernhardt, Jamie Heather, Oliver Watkins

Proceedings Volume 6233, 623321 (2006) https://doi.org/10.1117/12.665652

KEYWORDS: Sensors, Detection and tracking algorithms, Data modeling, Vegetation, Visualization, Statistical modeling, Hyperspectral imaging, Mathematical modeling, Mahalanobis distance, Algorithm development

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Proceedings Article | 1 June 2005 Paper

New models for hyperspectral anomaly detection and un-mixing

M. Bernhardt, J. Heather, M. Smith

Proceedings Volume 5806, (2005) https://doi.org/10.1117/12.601738

KEYWORDS: Detection and tracking algorithms, Vegetation, Evolutionary algorithms, Sensors, Algorithm development, Data modeling, Hyperspectral imaging, Process modeling, Statistical modeling, Statistical analysis

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Proceedings Article | 10 November 2004 Paper

Statistical detection algorithms in fat-tailed hyperspectral background clutter

Mark Bernhardt, William Oxford, Philip Clare, Vicky Wilkinson, Damien Clarke

Proceedings Volume 5573, (2004) https://doi.org/10.1117/12.565537

KEYWORDS: Data modeling, Detection and tracking algorithms, Statistical modeling, Target detection, Algorithm development, Sensors, Mahalanobis distance, Defense and security, Statistical analysis, Detector development

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Proceedings Article | 25 August 2004 Paper

Target tracking for missile warning applications

Moira Smith, Jamie Heather, Jason Ralph, Mark Bernhardt, Elias Griffith, Derek Bradley, Harwinder Padda

Proceedings Volume 5428, (2004) https://doi.org/10.1117/12.541482

KEYWORDS: Missiles, Detection and tracking algorithms, Image processing, Medium wave, Algorithm development, Infrared imaging, Image registration, Image filtering, Infrared radiation, Imaging systems

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Proceedings Article | 9 August 2004 Paper

Sensor data association for the Seawolf mid-life update (SWMLU) program

Christopher Dent, Duncan Hickman, Mark Bernhardt

Proceedings Volume 5429, (2004) https://doi.org/10.1117/12.541502

KEYWORDS: Missiles, Sensors, Radar, Target detection, Signal processing, Radar signal processing, Control systems, Signal detection, K band, Scintillation

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Proceedings Article | 5 August 2004 Paper

A statistical sea-surface clutter model in the long-wave infrared

Mark Bernhardt, Moira Smith, Philip Whitehead, L. Hunt, Duncan Hickman, Christopher Dent

Proceedings Volume 5431, (2004) https://doi.org/10.1117/12.541523

KEYWORDS: Electro optical modeling, Data modeling, Infrared radiation, Statistical analysis, Radar, Statistical modeling, Sensors, Long wavelength infrared, Sun, Thermal modeling

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Proceedings Article | 4 August 2004 Paper

Validation and acceptance of synthetic infrared imagery

Moira Smith, Mark Bernhardt, Christopher Angell, Duncan Hickman, Philip Whitehead, Dilip Patel

Proceedings Volume 5408, (2004) https://doi.org/10.1117/12.542323

KEYWORDS: Image processing, Databases, Image analysis, Infrared imaging, Cameras, Infrared radiation, Statistical analysis, Data modeling, Signal to noise ratio, Visualization

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Proceedings Article | 23 September 2003 Paper

A new approach to anomaly detection in hyperspectral images

Philip Clare, Mark Bernhardt, William Oxford, Sean Murphy, Peter Godfree, Vicky Wilkinson

Proceedings Volume 5093, (2003) https://doi.org/10.1117/12.487030

KEYWORDS: Detection and tracking algorithms, Statistical analysis, Target detection, Hyperspectral imaging, Statistical modeling, Data modeling, Data analysis, Image processing, Monte Carlo methods, Sensors

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Proceedings Article | 5 September 2003 Paper

Development of an EO sensor validation case for a missile tracker system

Duncan Hickman, Philip Whitehead, Christopher Dent, Moira Smith, Mark Bernhardt

Proceedings Volume 5075, (2003) https://doi.org/10.1117/12.487080

KEYWORDS: Image processing, Data modeling, Signal processing, Sensors, Modeling, Image analysis, Mathematical modeling, Databases, Atmospheric modeling, Image sensors

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Proceedings Article | 25 August 2003 Paper

Range and brightness fusion: using radar and electro-optical data association for tracking small objects

Philip Whitehead, Mark Bernhardt, Duncan Hickman, Christopher Dent

Proceedings Volume 5096, (2003) https://doi.org/10.1117/12.487002

KEYWORDS: Sensors, Missiles, Radar, Detection and tracking algorithms, Data fusion, Electro optics, Electro optical sensors, Monte Carlo methods, Computer simulations, Algorithm development

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Proceedings Article | 6 March 2002 Paper

Distributed air-to-ground targeting

Jason Ralph, Moira Smith, Mark Bernhardt, Catherine West, Christopher Angell, Scott Sims

Proceedings Volume 4731, (2002) https://doi.org/10.1117/12.458387

KEYWORDS: Data fusion, Filtering (signal processing), Error analysis, Sensors, Target recognition, Matrices, Target detection, Detection and tracking algorithms, Global Positioning System, Monte Carlo methods

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Proceedings Article | 16 August 2001 Paper

Multisensor fusion for improved air-to-ground target localization

Moira Smith, Eleanor Januarius, Mark Bernhardt, David Nicholson, Gareth Rees, Jason Ralph

Proceedings Volume 4380, (2001) https://doi.org/10.1117/12.436971

KEYWORDS: Sensors, Data fusion, Filtering (signal processing), Detection and tracking algorithms, Target detection, Synthetic aperture radar, Algorithm development, Data modeling, Radar, Electronic filtering

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Proceedings Article | 22 March 2001 Paper

Peformance limits for multiplatform scene-referenced navigation systems

Jason Ralph, Eleanor Januarius, Moira Smith, Kenneth Edwards, Mark Bernhardt

Proceedings Volume 4385, (2001) https://doi.org/10.1117/12.421117

KEYWORDS: Navigation systems, Error analysis, Imaging systems, Databases, Sensors, Global Positioning System, Data fusion, Forward looking infrared, Solids, Image fusion

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Proceedings Article | 5 September 2000 Paper

Signal tracking technique for laser radar sensors

Harvey Lewis, Brendan Ruck, Mark Bernhardt, Paul Evans, Peter Randall, Meryl Welch

Proceedings Volume 4035, (2000) https://doi.org/10.1117/12.397812

KEYWORDS: Sensors, Doppler effect, LIDAR, Signal processing, Filtering (signal processing), Demodulation, Signal detection, Detection and tracking algorithms, Modulation, Interference (communication)

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Proceedings Article | 14 December 1999 Paper

Beyond Gaussian statistical analysis for man-made object detection in hyperspectral images

Mark Bernhardt, Joanne Roberts

Proceedings Volume 3871, (1999) https://doi.org/10.1117/12.373247

KEYWORDS: Roads, Hyperspectral imaging, Sensors, Associative arrays, Statistical analysis, Mahalanobis distance, Data processing, Neural networks, Image classification, Hyperspectral systems

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Emerging Hyper-Spectral imaging technology allows the acquisition of data 'cubes' which simultaneously have high- resolution spatial and spectral components. There is a wealth of information in this data and effective techniques for extracting and processing this information are vital. Previous work by ERIM on man-made object detection has demonstrated that there is a huge amount of discriminatory information in hyperspectral images. This work used the hypothesis that the spectral characteristics of natural backgrounds can be described by a multivariate Gaussian model. The Mahalanobis distance (derived from the covariance matrix) between the background and other objects in the spectral data is the key discriminant. Other work (by DERA and Pilkington Optronics Ltd) has confirmed these findings, but indicates that in order to obtain the lowest possible false alarm probability, a way of including higher order statistics is necessary. There are many ways in which this could be done ranging from neural networks to classical density estimation approaches. In this paper we report on a new method for extending the Gaussian approach to more complex spectral signatures. By using ideas from the theory of Support Vector Machines we are able to map the spectral data into a higher dimensional space. The co- ordinates of this space are derived from all possible multiplicative combinations of the original spectral line intensities, up to a given order d -- which is the main parameter of the method. The data in this higher dimensional space are then analyzed using a multivariate Gaussian approach. Thus when d equals 1 we recover the ERIM model -- in this case the mapping is the identity. In order for such an approach to be at all tractable we must solve the 'combinatorial explosion' problem implicit in this mapping for large numbers of spectral lines in the signature data. In order to do this we note that in the final analysis of this approach it is only the inner (dot) products between vectors in the higher dimensional space that need to be computed. This can be done by efficient computations in the original data space. Thus the computational complexity of the problem is determined by the amount of data -- rather than the dimensionality of the mapping. The novel combination of non- linear mapping and high dimensional multivariate Gaussian analysis, only possible by using techniques from SVM theory, allows the practical application to hyperspectral imagery. We note that this approach also generates the non-linear Principal Components of the data, which have applications in their own right. In this paper we give a mathematical derivation of the method from first principles. The method is illustrated on a synthetic data set where complete control over the true statistics is possible. Results on this data show that the method is very powerful. It naturally extends the Gaussian approach to a variety of more complex probability distributions, including multi-modal and other manifestly non- Gaussian examples. Having shown the potential of this approach it is then applied to real hyperspectral trials data. The relative improvement in performance over the Gaussian approach is demonstrated for the real data.

Proceedings Article | 10 December 1999 Paper

Measurements of scintillation over a 17.55-km horizontal path

Mark Bernhardt, John Buckle, Christopher Dainty, Fred Reavell, Victor Ruiz-Cortes, Meryl Welch, Nicholas Wooder

Proceedings Volume 3866, (1999) https://doi.org/10.1117/12.371316

KEYWORDS: Scintillation, Sensors, Photon counting, Telescopes, Data acquisition, Signal detection, Turbulence, Carbon dioxide lasers, Data modeling, Modulation

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Proceedings Article | 4 October 1999 Paper

SPIRIT: a hyperspectral infrared imager

Stephen Evans, John Hargreaves, Paul Evans, Peter Randall, Mark Bernhardt

Proceedings Volume 3809, (1999) https://doi.org/10.1117/12.364018

KEYWORDS: Imaging systems, Sensors, Optical filters, Image registration, Image filtering, Spectroscopy, Hyperspectral imaging, Infrared imaging, Interference filters, Infrared radiation

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Proceedings Article | 8 September 1998 Paper

Demodulation schemes for vibrometers and associated effects on target classification

Brendan Ruck, Harvey Lewis, Andrew Rogoyski, Mark Bernhardt, Maxwell Hadley, Meryl Welch, Andrew Seedhouse

Proceedings Volume 3380, (1998) https://doi.org/10.1117/12.327190

KEYWORDS: Demodulation, Signal to noise ratio, Sensors, Frequency modulation, Signal processing, Doppler effect, Fermium, Modulation, Target recognition, Feature extraction

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Showing 5 of 30 publications

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