Dr. Sanjeev Agarwal Profile

Dr. Sanjeev Agarwal

Distinguished Researcher at US Army CCDC C5ISR Ctr Night Vision & Electronic Sensors Directorate

SPIE Involvement:

Author

Publications (42)

Proceedings Article | 6 June 2022 Poster + Paper

Study of fidelity of synthetic imagery for artificial intelligence (AI) and machine learning (ML) problems

S. Susan Young, Sanjeev Agarwal, Joseph Reynolds, Suya You

Proceedings Volume 12113, 121131R (2022) https://doi.org/10.1117/12.2618397

KEYWORDS: Modulation transfer functions, Image resolution, Digital cameras, Cameras, Optical filters, Super resolution, Image acquisition, Geometrical optics, Image filtering, Artificial intelligence

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Proceedings Article | 21 May 2020 Presentation + Paper

Developing, integrating and validating a compressive hyperspectral video imager

Juefei (Jeff) Zhou, Yi Yang, Le Li, Sanjeev Agarwal, Son Nguyen, Anthony Giljum, Kevin Kelly

Proceedings Volume 11423, 114230V (2020) https://doi.org/10.1117/12.2560282

KEYWORDS: Sensors, Spectroscopy, Digital micromirror devices, Video, Data acquisition, Reconstruction algorithms, Cameras, Hyperspectral imaging, Detection and tracking algorithms, Video compression

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Proceedings Article | 11 June 2018 Paper

An overview of joint activities on computational imaging and compressive sensing systems by NATO SET-232

Todd Du Bosq, Sanjeev Agarwal, Judith Dijk, Alper Gungor, H. Emre Guven, Terence Haran, Martin Laurenzis, Kevin Leonard, Abhijit Mahalanobis, Gabriela Paunescu, Jonathan Piper, Endre Repasi, Yunlong Sheng

Proceedings Volume 10669, 106690H (2018) https://doi.org/10.1117/12.2307852

KEYWORDS: Imaging systems, Sensors, Computational imaging, Image resolution, Image compression, Compressed sensing, Cameras, Spatial resolution, Multiplexing, Computing systems

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Proceedings Article | 14 May 2015 Paper

Near real-time, on-the-move multisensor integration and computing framework

Chris Burnette, Matt Schneider, Sanjeev Agarwal, Diane Deterline, Chris Geyer, Chung Phan, Richard Lydic, Kevin Green, Bruce Swett

Proceedings Volume 9454, 94540N (2015) https://doi.org/10.1117/12.2177760

KEYWORDS: Sensors, Video, Global Positioning System, Video processing, Cameras, Telecommunications, Data acquisition, Receivers, Binary data, Data communications

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Proceedings Article | 14 May 2015 Paper

Near real-time, on-the-move software PED using VPEF

Kevin Green, Chris Geyer, Chris Burnette, Sanjeev Agarwal, Bruce Swett, Chung Phan, Diane Deterline

Proceedings Volume 9454, 94540O (2015) https://doi.org/10.1117/12.2177750

KEYWORDS: Video, Sensors, Algorithm development, Video processing, Computer vision technology, Machine vision, Detection and tracking algorithms, Databases, Data processing, Human-machine interfaces

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Proceedings Article | 14 May 2015 Paper

Real-time buried threat detection and cueing capability in VPEF environment

Bo Ling, Sanjeev Agarwal, Santiago Olivera, Zlatko Vasilkoski, Chung Phan, Chris Geyer

Proceedings Volume 9454, 94540P (2015) https://doi.org/10.1117/12.2177743

KEYWORDS: Target detection, Environmental sensing, Statistical analysis, Video, Data processing, Detection and tracking algorithms, Cameras, Infrared imaging, Algorithm development, Detector development

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Proceedings Article | 22 June 2012 Paper

Detection and localization of R/C electronic devices using Hurst parameter

Vivek Thotla, Mohammad Tayeb Ahmad Ghasr, Maciej Zawodniok, S. Jagannathan, Sanjeev Agarwal

Proceedings Volume 8359, 835915 (2012) https://doi.org/10.1117/12.919375

KEYWORDS: Interference (communication), Signal detection, Signal to noise ratio, Received signal strength, Receivers, Electronic components, Antennas, Signal processing, Oscillators, Sensors

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Proceedings Article | 25 May 2012 Paper

Neural-network-based navigation and control of unmanned aerial vehicles for detecting unintended emissions

H. Zargarzadeh, David Nodland, V. Thotla, S. Jagannathan, S. Agarwal

Proceedings Volume 8387, 83870H (2012) https://doi.org/10.1117/12.919355

KEYWORDS: Unmanned aerial vehicles, Signal detection, Control systems, Antennas, Kinematics, Receivers, Error analysis, Feedback control, Signal processing, Electronic components

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Proceedings Article | 23 May 2011 Paper

Validating spectral spatial detection based on MMPP formulation

Anh Trang, Sanjeev Agarwal, Thomas Broach, Thomas Smith

Proceedings Volume 8017, 801723 (2011) https://doi.org/10.1117/12.886804

KEYWORDS: Land mines, Target detection, Detection and tracking algorithms, Algorithm development, Mining, Statistical analysis, Device simulation, Expectation maximization algorithms, Detector development, Computer simulations

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Spectral, shape or texture features of the detected targets are used to model the likelihood of the targets to be potential mines in a minefield. However, some potential mines can be false alarms due to the similarity of the mine signatures with natural and other manmade clutter signatures. Therefore, in addition to the target features, spatial distribution of the detected targets can be used to improve the minefield detection performance. In our recently published SPIE paper, we evaluated minefield detection performance for both patterned and unpatterned minefields in highly cluttered environments, simultaneously using both target features and target spatial distributions that define Markov Marked Point Process (MMPP). The results have suggested that proper exploitation of spectral/shape features and spatial distributions can indeed contribute improved performance of patterned and unpatterned minefield detection. Also, the ability of the algorithm to detect the minefields in highly cluttered environments shows the robustness of the developed minefield detection algorithm based on MMPP formulation. Moreover, the results show that the MMPP minefield detection algorithm performs significantly better than the baseline algorithm employing spatial point process with false alarm mitigation. Since these results were based on the simulated data, it is not clear that the MMPP detection algorithm has fully achieved its best performance. To validate its performance, an analytical solution for the minefield detection problem will be developed, and its performance will be compared with the performance of the simulated solution. The analytical solution for the complete minefield detection problem is intractable due to a large number of detections and the variation of the number of detected mines in the minefield process. Therefore, an analytical solution for a simplified detection problem will be derived, and its minefield performance will be compared with the minefield performance obtained from the simulation in the same MMPP framework for different clutter rates.

Proceedings Article | 29 April 2010 Paper

GPU-based processing for airborne detection

Dheeraj Singiresu, Sanjeev Agarwal, Shivakar Vulli, Harish Ramakrishnan

Proceedings Volume 7664, 766427 (2010) https://doi.org/10.1117/12.851492

KEYWORDS: Image registration, Sensors, MATLAB, Image processing, Image segmentation, Data processing, Convolution, Target detection, Detection and tracking algorithms, Image sensors

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Proceedings Article | 29 April 2010 Paper

Exploiting spatial distributions for minefield detection in cluttered environment

Anh Trang, Sanjeev Agarwal, Thomas Broach, Thomas Smith

Proceedings Volume 7664, 766426 (2010) https://doi.org/10.1117/12.851488

KEYWORDS: Land mines, Detection and tracking algorithms, Target detection, Algorithm development, Mining, Environmental sensing, Evolutionary algorithms, Sensors, Detector development, Computer simulations

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Proceedings Article | 5 May 2009 Paper

Simultaneously exploiting spectral similarity and spatial distribution for patterned minefield detection

Anh Trang, Sanjeev Agarwal, Thomas Broach, Thomas Smith

Proceedings Volume 7303, 73032B (2009) https://doi.org/10.1117/12.819336

KEYWORDS: Land mines, Target detection, Expectation maximization algorithms, Detection and tracking algorithms, Mining, Sensors, Algorithm development, Fermium, Frequency modulation, Computer simulations

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Proceedings Article | 5 May 2009 Paper

Spectral and spatial analysis of false alarms in background data

Abhishek Sanaka, Shivakar Vulli, Sanjeev Agarwal, Richard Ess, Anh Trang

Proceedings Volume 7303, 730316 (2009) https://doi.org/10.1117/12.819501

KEYWORDS: Vegetation, Near infrared, Land mines, Multispectral imaging, Mid-IR, Sensors, Spatial analysis, Algorithm development, Mining, Target detection

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Proceedings Article | 5 May 2009 Paper

Sensor data fusion for spectroscopy-based detection of explosives

Pratik Shah, Abhijeet Singh, Sanjeev Agarwal, Sahra Sedigh, Alan Ford, Robert Waterbury

Proceedings Volume 7303, 730329 (2009) https://doi.org/10.1117/12.819902

KEYWORDS: Laser induced breakdown spectroscopy, Raman spectroscopy, Explosives, Data fusion, Sensors, Explosives detection, Spectroscopy, Improvised explosive devices, Statistical analysis, Fusion energy

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Proceedings Article | 5 May 2009 Paper

Collection and evaluation of false alarm signatures in background data

Sanjeev Agarwal, Shivakar Vulli, Neil Malloy, Elizabeth Lord, Josh Fairley, Bruce Sabol, Wesley Johnson, Richard Ess, Anh Trang

Proceedings Volume 7303, 730318 (2009) https://doi.org/10.1117/12.819498

KEYWORDS: Multispectral imaging, Mining, Databases, Mid-IR, Vegetation, Sensors, Land mines, Infrared signatures, Infrared imaging, Target detection

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Proceedings Article | 29 April 2008 Paper

Comparative performance between compressed and uncompressed airborne imagery

Chung Phan, Ronald Rupp, Sanjeev Agarwal, Anh Trang, Sumesh Nair

Proceedings Volume 6953, 69530U (2008) https://doi.org/10.1117/12.782360

KEYWORDS: Image compression, Land mines, Detection and tracking algorithms, Algorithms, Target detection, Mining, Data compression, Sensors, Image quality, Unmanned aerial vehicles

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Proceedings Article | 29 April 2008 Paper

FastKRX: a fast approximation for kernel RX anomaly detection

Spandan Tiwari, Sanjeev Agarwal, Anh Trang

Proceedings Volume 6953, 695316 (2008) https://doi.org/10.1117/12.779586

KEYWORDS: Sensors, Land mines, Explosives, Target detection, Lawrencium, Detection and tracking algorithms

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Proceedings Article | 29 April 2008 Paper

Exploiting "mineness" for scatterable minefield detection

Anh Trang, Sanjeev Agarwal, Thomas Broach, Thomas Smith

Proceedings Volume 6953, 695317 (2008) https://doi.org/10.1117/12.779585

KEYWORDS: Land mines, Target detection, Mining, Fermium, Frequency modulation, Sensors, Expectation maximization algorithms, Detection and tracking algorithms, Algorithm development, Computer simulations

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Proceedings Article | 7 May 2007 Paper

A patterned and un-patterned minefield detection in cluttered environments using Markov marked point process

Anh Trang, Sanjeev Agarwal, Phillip Regalia, Thomas Broach, Thomas Smith

Proceedings Volume 6553, 655313 (2007) https://doi.org/10.1117/12.721368

KEYWORDS: Land mines, Target detection, Mining, Environmental sensing, Sensors, Metals, Detection and tracking algorithms, Data modeling, Data analysis, Image processing

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Proceedings Article | 27 April 2007 Paper

Buried mine detection in airborne imagery using co-occurrence texture features

Spandan Tiwari, Sanjeev Agarwal, Anh Trang

Proceedings Volume 6553, 65530Z (2007) https://doi.org/10.1117/12.721509

KEYWORDS: Mining, Land mines, Sensors, Matrices, Feature extraction, Multispectral imaging, Statistical analysis, Infrared signatures, Feature selection, Target detection

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Proceedings Article | 18 May 2006 Paper

Design tradeoffs for an airborne minefield detection system

Saurabh Agarwal, Sanjeev Agarwal, Ron Rupp, Anh Trang

Proceedings Volume 6217, 62173E (2006) https://doi.org/10.1117/12.673806

KEYWORDS: Land mines, Sensors, Target detection, Detection and tracking algorithms, Unmanned aerial vehicles, Statistical analysis, Image segmentation, Device simulation, Data modeling, Mining

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Proceedings Article | 10 June 2005 Paper

Evaluating operator performance in aided airborne mine detection

Sanjeev Agarwal, Madhu Reddy, Richard Hall, Thomas Woodard, John Brown, Anh Trang

Proceedings Volume 5794, (2005) https://doi.org/10.1117/12.604675

KEYWORDS: Land mines, Mining, Sensors, Target detection, Mid-IR, Human subjects, Automatic target recognition, Visual process modeling, Human-machine interfaces, Interfaces

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In this paper we evaluate mine level detection performance of the human operator using high resolution mid-wave infrared (MWIR) imagery and compare it with the performance of automatic target recognition (ATR) like RX detector. Previous studies have shown that the anomaly detectors like the RX detector and even more sophisticated ATR techniques fall short of the performance achieved by human analyst for mine and minefield detection. There are three main objectives of the paper. First, we seek to establish performance bounds for mine detection using a single MWIR sensor under different conditions. Second, we evaluate the conditions under which the human visual system contributes significantly over and above RX anomaly detector. Third, we seek to qualitatively study the visual processes and mental models employed by the human operators to detect mines. A graphical user interface (HILgui) was developed using MATLAB to evaluate mine level detection performance for the operator. This interface is used to conduct a series of experiments examining performance for twenty subjects. The mine images varied systematically based on the time of day the images were collected, the type of terrain and type of mines. All the experiments were video-recorded and post-experiment interviews were conducted for qualitative analysis. Both qualitative and quantitative research techniques were used to gather and analyze the data. Results from different quantitative analysis including the accuracy of mine detection, propensity of false alarms and the time taken by the operator to mark individual targets are discussed. The mental models developed by the subjects for detection of mine targets are also discussed. Limitations of the current experiments and plans for future work are discussed. It is hoped that this systematic evaluation of a human operator in airborne mine detection will help in developing new and better ATR techniques and help identify critical features required in the operator interface for the warfighter-in-the-loop (WIL) minefield detection.

Proceedings Article | 10 June 2005 Paper

Feature correspondence and semi-automatic ground truthing for airborne data collection

Spandan Tiwari, Sanjeev Agarwal, Chung Phan, Todd Acinelli

Proceedings Volume 5794, (2005) https://doi.org/10.1117/12.604792

KEYWORDS: Land mines, Sensors, Mining, Binary data, Image registration, Tolerancing, Target detection, Detection and tracking algorithms, Global Positioning System, Cameras

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A significant amount of airborne data has been collected in the past and more is expected to be collected in the future to support airborne landmine detection research and evaluation under various programs. In order to evaluate mine and minefield detection performance for sensor and detection algorithms, it is essential to generate reliable and accurate ground truth for the location of the mine targets and fiducials present in raw imagery. The current ground truthing operation is primarily manual, which makes the ground truthing a time consuming and expensive exercise in the overall data collection effort. In this paper, a semi-automatic ground-truthing technique is presented which reduces the role of the operator to a few high-level input and validation actions. A correspondence is established between the high-contrast targets in the airborne imagery called the image features, and the known GPS locations of the targets on the ground called the map features by imposing various position and geometric constraints. These image and map features may include individual fiducial targets, rows of fiducial targets and triplets of non-collinear fiducials. The targets in the imagery are established using the RX anomaly detector. An affine or linear conformal transformation from map features to image features is calculated based on feature correspondence. This map-to-image transformation is used to generate ground-truth for mine targets. Since accurate and reliable flight-log data is currently not available, one-time specification of a few parameters like flight speed, flight direction, camera resolution and specification of the location of the initial frame on the map is required from the operator. These parameters are updated and corrected for subsequent frames based on the processing of previous frames. Image registration is used to ground-truth images which do not have enough high-contrast fiducials for reliable correspondence. A GUI called SemiAutoGT developed in MATLAB for the ground truthing process is briefly discussed. Results are presented for ground-truthing of the data collected under the Lightweight Airborne Multispectral Minefield Detection (LAMD) program.

Proceedings Article | 10 June 2005 Paper

Semi-automated based ground-truthing GUI for airborne imagery

Chung Phan, Rich Lydic, Tim Moore, Anh Trang, Sanjeev Agarwal, Spandan Tiwari

Proceedings Volume 5794, (2005) https://doi.org/10.1117/12.605208

KEYWORDS: Image processing, Target detection, Airborne remote sensing, Algorithm development, Detection and tracking algorithms, Sensors, Land mines, Mining, MATLAB, Human-machine interfaces

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Proceedings Article | 10 June 2005 Paper

Warfighter-in-the-loop: mental models in airborne minefield detection

Madhu Reddy, Sanjeev Agarwal, Richard Hall, John Brown, Thomas Woodard, Anh Trang

Proceedings Volume 5794, (2005) https://doi.org/10.1117/12.604789

KEYWORDS: Land mines, Sensors, Zoom lenses, Interfaces, Mining, Performance modeling, Cognitive modeling, Automatic target recognition, Human-machine interfaces, Target detection

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Proceedings Article | 21 September 2004 Paper

False-alarm mitigation and feature-based discrimination for airborne mine detection

Deepak Menon, Sanjeev Agarwal, Ritesh Ganju, C. Swonger

Proceedings Volume 5415, (2004) https://doi.org/10.1117/12.544634

KEYWORDS: Mining, Target detection, Land mines, Sensors, Metals, Detection and tracking algorithms, Image segmentation, Binary data, Remote sensing, Mid-IR

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Proceedings Article | 21 September 2004 Paper

Image-based synthesis of airborne minefield MWIR data

Sanjeev Agarwal, Thandava Edara, C. Swonger, Anh Trang

Proceedings Volume 5415, (2004) https://doi.org/10.1117/12.546045

KEYWORDS: Land mines, Mid-IR, Algorithm development, Databases, Detection and tracking algorithms, Image quality, Mathematical modeling, Sensors, Data modeling, Magnetorheological finishing

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Proceedings Article | 21 September 2004 Paper

Knowledge-based architecture for airborne mine and minefield detection

Sanjeev Agarwal, Deepak Menon, C. Swonger

Proceedings Volume 5415, (2004) https://doi.org/10.1117/12.544719

KEYWORDS: Land mines, Detection and tracking algorithms, Sensors, Target detection, Algorithm development, Environmental sensing, Computer architecture, Data acquisition, Databases, Vegetation

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