Dr. Peter A. Torrione Profile

Dr. Peter A. Torrione

CTO at Covar LLC

SPIE Involvement:

Author

Publications (65)

Proceedings Article | 12 June 2023 Presentation

Self-assessment for real-time performance estimation in AI/ML (Conference Presentation)

Peter Torrione

Proceedings Volume 12538, 1253808 (2023) https://doi.org/10.1117/12.2666001

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Proceedings Article | 22 May 2015 Paper

Recognizing subsurface target responses in ground penetrating radar data using convolutional neural networks

Rayn Sakaguchi, Kenneth Morton, Leslie Collins, Peter Torrione

Proceedings Volume 9454, 94541A (2015) https://doi.org/10.1117/12.2177747

KEYWORDS: General packet radio service, Neural networks, Sensors, Feature extraction, Visualization, Ground penetrating radar, Target recognition, Target detection, Error analysis, Convolutional neural networks

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Proceedings Article | 21 May 2015 Paper

Leveraging robust principal component analysis to detect buried explosive threats in handheld ground-penetrating radar data

Dmitry Kalika, Mary Knox, Leslie Collins, Peter Torrione, Kenneth Morton

Proceedings Volume 9454, 94541D (2015) https://doi.org/10.1117/12.2177944

KEYWORDS: Detection and tracking algorithms, Target detection, General packet radio service, Sensors, Explosives, Principal component analysis, Ground penetrating radar, Data modeling, Algorithm development, Expectation maximization algorithms

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Proceedings Article | 21 May 2015 Paper

Improving buried threat detection in ground-penetrating radar with transfer learning and metadata analysis

Kenneth Colwell, Peter Torrione, Kenneth Morton, Leslie Collins

Proceedings Volume 9454, 94541C (2015) https://doi.org/10.1117/12.2177940

KEYWORDS: General packet radio service, Ground penetrating radar, Signal processing, Target detection, Classification systems, Data modeling, Receivers, Image processing, Image classification, Binary data

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Proceedings Article | 21 May 2015 Paper

Target signature localization in GPR data by jointly estimating and matching templates

Daniël Reichman, Kenneth Morton, Jordan Malof, Leslie Collins, Peter Torrione

Proceedings Volume 9454, 945416 (2015) https://doi.org/10.1117/12.2176627

KEYWORDS: Land mines, General packet radio service, Data modeling, Detection and tracking algorithms, Target detection, Data analysis, Process modeling, Ground penetrating radar, Remote sensing, Sensors

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Proceedings Article | 15 May 2015 Paper

Clutter and target discrimination in forward-looking ground penetrating radar using sparse structured basis pursuits

Joseph Camilo, Jordan Malof, Peter Torrione, Leslie Collins, Kenneth Morton

Proceedings Volume 9454, 94540V (2015) https://doi.org/10.1117/12.2176491

KEYWORDS: Sensors, Antennas, General packet radio service, Radar, Polarization, Detection and tracking algorithms, Ground penetrating radar, Land mines, Image processing, Target detection

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Proceedings Article | 14 May 2015 Paper

Buried threat detection using a handheld ground penetrating radar system

Mary Knox, Peter Torrione, Leslie Collins, Kenneth Morton

Proceedings Volume 9454, 94540F (2015) https://doi.org/10.1117/12.2177812

KEYWORDS: Sensors, Detection and tracking algorithms, General packet radio service, Head-mounted displays, Mining, Expectation maximization algorithms, Feature extraction, Ground penetrating radar, Robotics, Principal component analysis

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Proceedings Article | 14 May 2015 Paper

A queuing model for designing multi-modality buried target detection systems: preliminary results

Jordan Malof, Kenneth Morton, Leslie Collins, Peter Torrione

Proceedings Volume 9454, 94540I (2015) https://doi.org/10.1117/12.2176610

KEYWORDS: Forward looking infrared, General packet radio service, Sensors, Systems modeling, Data modeling, Computing systems, Detection and tracking algorithms, Target detection, Cameras, Infrared sensors

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Proceedings Article | 13 June 2014 Paper

A novel algorithm for buried target detection evaluated on a collection of seismo-acoustic data

Jordan Malof, Mary Knox, Peter Torrione, Leslie Collins, Kenneth Morton

Proceedings Volume 9072, 907208 (2014) https://doi.org/10.1117/12.2053842

KEYWORDS: Target detection, Detection and tracking algorithms, Acoustics, Laser Doppler velocimetry, Data processing, Ferroelectric LCDs, Fourier transforms, Sensors, Blob detection, Doppler effect

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Proceedings Article | 13 June 2014 Paper

Fusion of forward looking infrared and ground penetrating radar for improved stopping distances in landmine detection

Jordan Malof, Kenneth Morton, Leslie Collins, Peter Torrione

Proceedings Volume 9072, 90720W (2014) https://doi.org/10.1117/12.2051444

KEYWORDS: General packet radio service, Forward looking infrared, Sensors, Standoff detection, Feature extraction, Detection and tracking algorithms, Data processing, Cameras, Ground penetrating radar, Land mines

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Proceedings Article | 29 May 2014 Paper

Hyperbolic and PLSDA filter algorithms to detect buried threats in GPR data

Dmitry Kalika, Kenneth Morton, Leslie Collins, Peter Torrione

Proceedings Volume 9072, 90720U (2014) https://doi.org/10.1117/12.2050502

KEYWORDS: Detection and tracking algorithms, General packet radio service, Explosives, Target detection, Ground penetrating radar, Algorithm development, Gaussian pulse, Phase shifts, Fusion energy, Sensors

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Proceedings Article | 29 May 2014 Paper

Target localization and signature extraction in GPR data using expectation-maximization and principal component analysis

Daniel Reichman, Kenneth Morton, Leslie Collins, Peter Torrione

Proceedings Volume 9072, 90720Q (2014) https://doi.org/10.1117/12.2049874

KEYWORDS: Land mines, General packet radio service, Principal component analysis, Data modeling, Target detection, Sensors, Expectation maximization algorithms, Detection and tracking algorithms, Distortion, Dielectrics

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Proceedings Article | 29 May 2014 Paper

A robust Bayesian approach to target detection applied to explosive threat detection in handheld ground penetrating radar data

Kenneth Morton, Leslie Collins, Peter Torrione

Proceedings Volume 9072, 90720R (2014) https://doi.org/10.1117/12.2050514

KEYWORDS: Detection and tracking algorithms, General packet radio service, Target detection, Algorithm development, Statistical analysis, Data modeling, Ground penetrating radar, Radar, Sensors, Explosives

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Proceedings Article | 29 May 2014 Paper

Experiment design for measuring the probability of detection in remote sensing: how many objects and how many passes

Peter Torrione, Leslie Collins, Kenneth Morton

Proceedings Volume 9072, 90720M (2014) https://doi.org/10.1117/12.2049704

KEYWORDS: Target detection, Data modeling, Sensors, Detection and tracking algorithms, Remote sensing, General packet radio service, Systems modeling, Electromagnetic coupling, Sensing systems, Visualization

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Proceedings Article | 29 May 2014 Paper

Physics-based deformations of ground penetrating radar signals to improve the detection of buried explosives

Rayn Sakaguchi, Kennth Morton, Leslie Collins, Peter Torrione

Proceedings Volume 9072, 90720P (2014) https://doi.org/10.1117/12.2049316

KEYWORDS: General packet radio service, Land mines, Dielectrics, Sensors, Data modeling, Ground penetrating radar, Antennas, Signal detection, Explosives, Target detection

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Proceedings Article | 7 June 2013 Paper

A novel framework for processing forward looking infrared imagery with application to buried threat detection

Jordan Malof, Kenneth Morton, Leslie Collins, Peter Torrione

Proceedings Volume 8709, 87091D (2013) https://doi.org/10.1117/12.2016113

KEYWORDS: Forward looking infrared, Sensors, Cameras, Video, Detection and tracking algorithms, Image processing, Infrared cameras, Land mines, Sensing systems, Transform theory

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Proceedings Article | 7 June 2013 Paper

Application of image categorization methods for buried threat detection in GPR data

Rayn Sakaguchi, Kenneth Morton, Leslie Collins, Peter Torrione

Proceedings Volume 8709, 87091P (2013) https://doi.org/10.1117/12.2016709

KEYWORDS: Land mines, General packet radio service, Fusion energy, Detection and tracking algorithms, Image processing, Sensors, Visualization, Data modeling, Feature extraction, Data processing

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Proceedings Article | 7 June 2013 Paper

Multiple instance hidden Markov models for GPR-based landmine detection

Achut Manandhar, Kenneth Morton, Leslie Collins, Peter Torrione

Proceedings Volume 8709, 87091L (2013) https://doi.org/10.1117/12.2016401

KEYWORDS: Land mines, Data modeling, General packet radio service, Statistical modeling, Target detection, Feature extraction, Matrices, Image segmentation, Detection and tracking algorithms, Signal detection

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Proceedings Article | 7 June 2013 Paper

Sparse model inversion and processing of spatial frequency-domain electromagnetic induction sensor array data for improved landmine discrimination

Stacy Tantum, Kenneth Colwell, Waymond Scott, Peter Torrione, Leslie Collins, Kenneth Morton

Proceedings Volume 8709, 87091E (2013) https://doi.org/10.1117/12.2016063

KEYWORDS: Sensors, Data modeling, Land mines, Electromagnetic coupling, Mining, Model-based design, Surface plasmons, Electromagnetism, Target detection, Signal processing

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Proceedings Article | 25 May 2012 Paper

Rapid position estimation and tracking for autonomous driving

Patrick Wang, Peter Torrione, Leslie Collins, Kenneth Morton

Proceedings Volume 8387, 83871I (2012) https://doi.org/10.1117/12.919317

KEYWORDS: Roads, Cameras, Video, Image analysis, Data modeling, RGB color model, Statistical analysis, Televisions, Computer simulations, Particles

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Proceedings Article | 11 May 2012 Paper

Feature extraction and processing of spatial frequency-domain electromagnetic induction sensor data for improved landmine discrimination

Stacy Tantum, Kenneth Colwell, Kenneth Morton, Waymond Scott, Leslie Collins, Peter Torrione

Proceedings Volume 8357, 835708 (2012) https://doi.org/10.1117/12.919421

KEYWORDS: Sensors, Data modeling, Land mines, Electromagnetic coupling, Mining, Electromagnetism, Feature extraction, Target detection, Model-based design, Signal processing

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Proceedings Article | 11 May 2012 Paper

Investigation of the effects of operator technique on handheld sensor data for landmine detection

Stacy Tantum, Kenneth Morton, Leslie Collins, Peter Torrione

Proceedings Volume 8357, 83571B (2012) https://doi.org/10.1117/12.919427

KEYWORDS: Sensors, Land mines, General packet radio service, Target detection, Statistical analysis, Ground penetrating radar, Dielectrics, Electromagnetism, Detection and tracking algorithms, Head

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Proceedings Article | 11 May 2012 Paper

A Bayesian method for discriminative context-dependent fusion of GPR-based detection algorithms

Christopher Ratto, Kenneth Morton, Leslie Collins, Peter Torrione

Proceedings Volume 8357, 835723 (2012) https://doi.org/10.1117/12.919079

KEYWORDS: General packet radio service, Land mines, Detection and tracking algorithms, Data fusion, Feature extraction, Metals, Performance modeling, Ground penetrating radar, Sensors, Soil science

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Proceedings Article | 11 May 2012 Paper

Multiple instance learning for landmine detection using ground penetrating radar

Achut Manandhar, Kenneth Morton, Leslie Collins, Peter Torrione

Proceedings Volume 8357, 835721 (2012) https://doi.org/10.1117/12.917947

KEYWORDS: Data modeling, Land mines, Process modeling, General packet radio service, Feature extraction, Algorithm development, Machine learning, Computer simulations, Detection and tracking algorithms, Ground penetrating radar

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Ground Penetrating Radar (GPR) has been extensively employed as a technology for the detection of subsurface buried threats. Although vehicular mounted GPRs generate data in three dimensions, alarm declarations are usually only available in the form of 2-D spatial coordinates. The uncertainty in the depth of the target in the three dimensional volume of data, and the difficulties associated with automatically localizing objects in depth, can adversely impact feature extraction and training in some detection algorithms. In order to mitigate the negative impact of uncertainty in target depth, several algorithms have been developed that extract features from multiple depth regions and utilize these feature vectors in classification algorithms to perform final mine/nonmine decisions. However, the uncertainty in object depth significantly complicates learning since features at the correct target depth are often significantly different from features at other depths but in the same volume. Multiple Instance Learning (MIL) is a type of supervised learning approach in which labels are available for a collection of feature vectors but not for individual samples, or in this application, depths. The goal of MIL is to classify new collections of vectors as they become available. This set-based learning method is applicable in the landmine detection problem because features that are extracted independently from several depth bins can be viewed as a set of unlabeled feature vectors, where the entire set either corresponds to a buried threat or a false alarm. In this work, a novel generative Dirichlet Process Gaussian mixture model for MIL is developed that automatically infers the number of mixture components required to model the underlying distributions of mine/non-mine signatures and performs classification using a likelihood ratio test. In this work, we show that the performance of the proposed approach for discriminating targets from non-targets in GPR data is promising.

Proceedings Article | 11 May 2012 Paper

Keypoint-based image processing for landmine detection in GPR data

Rayn Sakaguchi, Kenneth Morton, Leslie Collins, Peter Torrione

Proceedings Volume 8357, 83571Z (2012) https://doi.org/10.1117/12.918361

KEYWORDS: Sensors, General packet radio service, Image processing, Land mines, Detection and tracking algorithms, Machine vision, Computer vision technology, Sensor performance, Object recognition, Corner detection

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Proceedings Article | 11 May 2012 Paper

Integration of lidar with the NIITEK GPR for improved performance on rough terrain

Christopher Ratto, Kenneth Morton, Ian McMichael, Brian Burns, William Clark, Leslie Collins, Peter Torrione

Proceedings Volume 8357, 83571U (2012) https://doi.org/10.1117/12.919119

KEYWORDS: General packet radio service, LIDAR, Land mines, Detection and tracking algorithms, Antennas, Metals, Target detection, Sensors, Prototyping, Global Positioning System

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Proceedings Article | 11 May 2012 Paper

Change-based threat detection in urban environments with a forward-looking camera

Kenneth Morton, Christopher Ratto, Jordan Malof, Michael Gunter, Leslie Collins, Peter Torrione

Proceedings Volume 8357, 83571M (2012) https://doi.org/10.1117/12.917721

KEYWORDS: Video, Cameras, Explosives, Distance measurement, Video processing, Detection and tracking algorithms, Environmental sensing, Land mines, Roads, Light sources and illumination

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Proceedings Article | 11 May 2012 Paper

Processing forward-looking data for anomaly detection: single-look, multi-look, and spatial classification

Jordan Malof, Kenneth Morton, Leslie Collins, Peter Torrione

Proceedings Volume 8357, 83571O (2012) https://doi.org/10.1117/12.918447

KEYWORDS: Detection and tracking algorithms, Sensors, Forward looking infrared, Target detection, Algorithm development, Image filtering, Electronic filtering, Cameras, Data modeling, Explosives

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Proceedings Article | 4 June 2011 Paper

Signal processing for the detection of explosive residues on varying substrates using laser-induced breakdown spectroscopy

Kenneth Morton, Peter Torrione, Leslie Collins

Proceedings Volume 8018, 801817 (2011) https://doi.org/10.1117/12.885111

KEYWORDS: Laser induced breakdown spectroscopy, Chemometrics, Data modeling, Principal component analysis, Explosives, Detection and tracking algorithms, Explosives detection, Binary data, Aluminum, Signal processing

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Proceedings Article | 3 June 2011 Paper

Classification of acoustic gunshot signatures using a nonparametric Bayesian signal model

Kenneth Morton, Peter Torrione, Leslie Collins

Proceedings Volume 8019, 80190T (2011) https://doi.org/10.1117/12.885114

KEYWORDS: Autoregressive models, Acoustics, Data modeling, Weapons, Statistical analysis, Firearms, Signal detection, Statistical modeling, Performance modeling, Time-frequency analysis

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