Dr. Patrick J. Rauss Profile

Dr. Patrick J. Rauss

Physicist

SPIE Involvement:

Author

Publications (8)

Proceedings Article | 10 May 2019 Paper

Understanding of multi-domain battle challenges: AI/ML and the day/night thermal variability of targets

Patrick Rauss, Dalton Rosario

Proceedings Volume 11006, 110061W (2019) https://doi.org/10.1117/12.2517510

KEYWORDS: Long wavelength infrared, Detection and tracking algorithms, Network architectures, Infrared radiation, Sensors, Sun, Machine learning, Vegetation, Hyperspectral target detection

Read Abstract +

SPIE Journal Paper | 19 June 2017

Deep greedy learning under thermal variability in full diurnal cycles

Patrick Rauss, Dalton Rosario

OE, Vol. 56, Issue 08, 081809, (June 2017) https://doi.org/10.1117/12.10.1117/1.OE.56.8.081809

KEYWORDS: Data modeling, Long wavelength infrared, Infrared radiation, Content addressable memory, Temperature metrology

Read Abstract +

Proceedings Article | 5 May 2017 Presentation + Paper

Deep learning over diurnal and other environmental effects

Dalton Rosario, Patrick Rauss

Proceedings Volume 10198, 101980E (2017) https://doi.org/10.1117/12.2262866

KEYWORDS: Long wavelength infrared, Infrared imaging, Hyperspectral imaging, Infrared radiation, Environmental sensing, Analytical research, Infrared signatures, Machine learning, Data modeling, Network architectures, Roads, Sensors, Temperature metrology

Read Abstract +

Proceedings Article | 13 May 2010 Paper

Use of ensemble learning technique for detection/identification of chemical plumes

Patrick Rauss, Heesung Kwon

Proceedings Volume 7695, 76951T (2010) https://doi.org/10.1117/12.850039

KEYWORDS: Sensors, Algorithm development, Palladium, Chemical analysis, Image processing, Detection and tracking algorithms, Visualization, Hyperspectral imaging, Neural networks, Absorption

Read Abstract +

Proceedings Article | 18 October 2001 Paper

Multisensor neural network approach to mine detection

Amber Iler, Jay Marble, Patrick Rauss

Proceedings Volume 4394, (2001) https://doi.org/10.1117/12.445429

KEYWORDS: Sensors, Neural networks, General packet radio service, Land mines, Metals, Target detection, Mining, Antennas, Infrared sensors, Forward looking infrared

Read Abstract +

Proceedings Article | 26 February 1997 Paper

FERET (Face Recognition Technology) program

Patrick Rauss, Jonathan Phillips, Mark Hamilton, A. Trent DePersia

Proceedings Volume 2962, (1997) https://doi.org/10.1117/12.267831

KEYWORDS: Databases, Detection and tracking algorithms, Algorithm development, Facial recognition systems, Video, Head, Standards development, Photography, Defense technologies, Image processing

Read Abstract +

Proceedings Article | 14 February 1997 Paper

FERET (Face Recognition Technology) program

Patrick Rauss, Jonathan Phillips, Hyeonjoon Moon, Syed Rizvi, Mark Hamilton, A. Trent DePersia

Proceedings Volume 2935, (1997) https://doi.org/10.1117/12.266788

KEYWORDS: Algorithm development, Detection and tracking algorithms, Databases, Facial recognition systems, Video, Image processing, Defense technologies, Prototyping, Eye, Video surveillance

Read Abstract +

Proceedings Article | 14 June 1996 Paper

Model-based automatic target recognition using hierarchical foveal machine vision

Douglas McKee, Cesar Bandera, Sugata Ghosal, Patrick Rauss

Proceedings Volume 2755, (1996) https://doi.org/10.1117/12.243150

KEYWORDS: Target detection, Fractal analysis, Automatic target recognition, Sensors, Image filtering, Visual process modeling, Machine vision, Image resolution, Model-based design, Target recognition

Read Abstract +

This paper presents a target detection and interrogation techniques for a foveal automatic target recognition (ATR) system based on the hierarchical scale-space processing of imagery from a rectilinear tessellated multiacuity retinotopology. Conventional machine vision captures imagery and applies early vision techniques with uniform resolution throughout the field-of-view (FOV). In contrast, foveal active vision features graded acuity imagers and processing coupled with context sensitive gaze control, analogous to that prevalent throughout vertebrate vision. Foveal vision can operate more efficiently in dynamic scenarios with localized relevance than uniform acuity vision because resolution is treated as a dynamically allocable resource. Foveal ATR exploits the difference between detection and recognition resolution requirements and sacrifices peripheral acuity to achieve a wider FOV (e.g. faster search), greater localized resolution where needed (e.g., more confident recognition at the fovea), and faster frame rates (e.g., more reliable tracking and navigation) without increasing processing requirements. The rectilinearity of the retinotopology supports a data structure that is a subset of the image pyramid. This structure lends itself to multiresolution and conventional 2-D algorithms, and features a shift invariance of perceived target shape that tolerates sensor pointing errors and supports multiresolution model-based techniques. The detection technique described in this paper searches for regions-of- interest (ROIs) using the foveal sensor's wide FOV peripheral vision. ROIs are initially detected using anisotropic diffusion filtering and expansion template matching to a multiscale Zernike polynomial-based target model. Each ROI is then interrogated to filter out false target ROIs by sequentially pointing a higher acuity region of the sensor at each ROI centroid and conducting a fractal dimension test that distinguishes targets from structured clutter.

Showing 5 of 8 publications

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Keywords/Phrases

Search In:

Publication Years