Dr. Gregory P. Spell Profile

Dr. Gregory P. Spell

at CoVar LLC

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Publications (5)

Proceedings Article | 7 June 2024 Presentation + Paper

Integration of synthetic data into real world computer vision pipelines

Gregory Spell, Michael Tran, Peter Torrione, Mark Jeiran, Bassam Bahhur, Kimberly Manser

Proceedings Volume 13035, 130350X (2024) https://doi.org/10.1117/12.3012808

KEYWORDS: Deep learning, Image classification, Object detection, Infrared imaging, Image segmentation, Computer vision technology, RGB color model, Detection and tracking algorithms, Sensors

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Proceedings Article | 14 June 2023 Presentation + Paper

Real-data performance evaluation of Unreal Engine synthetic IR data

Christian Nadell, Gregory Spell, Mark Jeiran, Kimberly Manser

Proceedings Volume 12529, 1252904 (2023) https://doi.org/10.1117/12.2665012

KEYWORDS: Education and training, Data modeling, Performance modeling, Infrared imaging, Mid-IR, Long wavelength infrared, Object detection, Device simulation, Cross validation, Modulation transfer functions

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Proceedings Article | 14 June 2023 Presentation + Paper

Real-data performance evaluation of composite synthetic IR data

Gregory Spell, Christian Nadell, Bassam Bahhur, Kimberly Manser

Proceedings Volume 12529, 1252907 (2023) https://doi.org/10.1117/12.2665017

KEYWORDS: Data modeling, Performance modeling, Infrared imaging, Object detection, Image classification, Deep learning, Visual process modeling, Overfitting

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Proceedings Article | 26 May 2020 Paper

Background adaptive faster R-CNN for semi-supervised convolutional object detection of threats in x-ray images

John Sigman, Gregory Spell, Kevin Liang, Lawrence Carin

Proceedings Volume 11404, 1140404 (2020) https://doi.org/10.1117/12.2558542

KEYWORDS: X-rays, Data modeling, X-ray imaging, Feature extraction, Transportation security, Convolutional neural networks, Machine learning, Threat warning systems

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Recently, progress has been made in the supervised training of Convolutional Object Detectors (e.g. Faster RCNN) for threat recognition in carry-on luggage using X-ray images. This is part of the Transportation Security Administration's (TSA's) mission to ensure safety for air travelers in the United States. Collecting more data reliably improves performance for this class of deep algorithm, but requires time and money to produce training data with threats staged in realistic contexts. In contrast to these hand-collected data containing threats, data from the real-world, known as the Stream-of-Commerce (SOC), can be collected quickly with minimal cost; while technically unlabeled, in this work we make a practical assumption that these are without threat objects. Because of these data constraints, we will use both labeled and unlabeled sources of data for the automatic threat recognition problem. In this paper, we present a semi-supervised approach for this problem which we call Background Adaptive Faster R-CNN. This approach is a training method for two-stage object detectors which uses Domain Adaptation methods from the field of deep learning. The data sources described earlier are considered two “domains": one a hand-collected data domain of images with threats, and the other a real-world domain of images assumed without threats. Two domain discriminators, one for discriminating object proposals and one for image features, are adversarially trained to prevent encoding domain-specific information. Penalizing this encoding is important because otherwise the Convolutional Neural Network (CNN) can learn to distinguish images from the two sources based on superficial characteristics, and minimize a purely supervised loss function without improving its ability to recognize objects. For the hand-collected data, only object proposals and image features completely outside of areas corresponding to ground truth object bounding boxes (background) are used. The losses for these domain-adaptive discriminators are added to the Faster R-CNN losses of images from both domains. This technique enables threat recognition based on examples from the labeled data, and can reduce false alarm rates by matching the statistics of extracted features on the hand-collected backgrounds to that of the real world data. Performance improvements are demonstrated on two independently-collected datasets of labeled threats.

Proceedings Article | 27 April 2018 Presentation + Paper

Automatic threat recognition of prohibited items at aviation checkpoint with x-ray imaging: a deep learning approach

Kevin Liang, Geert Heilmann, Christopher Gregory, Souleymane Diallo, David Carlson, Gregory Spell, John Sigman, Kris Roe, Lawrence Carin

Proceedings Volume 10632, 1063203 (2018) https://doi.org/10.1117/12.2309484

KEYWORDS: Firearms, Data modeling, X-ray imaging, Performance modeling, Detection and tracking algorithms, Prototyping, Transportation security

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