Prof. Panagiotis P. Markopoulos Profile

Prof. Panagiotis P. Markopoulos

Associate Professor at Univ of Texas at San Antonio

SPIE Involvement:

Conference Chair | Editor | Author

Area of Expertise:

Robust machine learning , Limited data learning , Multimodal learning and fusion , Subspace signal processing , Lp-norm methods , Tensor methods

Websites:

Personal Website

Dr. Markopoulos is an expert in the areas of machine learning, data science, and signal processing. His research mission is to advance efficient, explainable, and trustworthy artificial intelligence. Dr. Markopoulos focuses on fundamental machine learning (statistical, computational), but also on practical machine-learning solutions to a wide range of real-world problems.

Proceedings Article | 10 June 2024 Presentation + Paper

L1-PCA with quantum annealing

Ian Tomeo, Panagiotis (Panos P.) Markopoulos, Andreas Savakis

Proceedings Volume 13036, 1303605 (2024) https://doi.org/10.1117/12.3015944

KEYWORDS: Principal component analysis, Quantum annealing, Quantum communications, Quantum hardware, Quantum numbers, Matrices, Singular value decomposition, Binary data, Annealing, Quantum data

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Proceedings Article | 14 June 2023 Presentation + Paper

Multimodal aerial view object classification with disjoint unimodal feature extraction and fully connected-layer fusion

Saurav Singh, Manish Sharma, Jamison Heard, Jesse Lew, Eli Saber, Panos Markopoulos

Proceedings Volume 12522, 1252206 (2023) https://doi.org/10.1117/12.2664041

KEYWORDS: Feature extraction, Data fusion, Synthetic aperture radar, Feature fusion, Image classification, Airborne remote sensing, Machine learning, Scene classification, Sensors

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Proceedings Article | 12 June 2023 Poster + Paper

HDA: an iterative hyperplane-search method for discriminant analysis

Ian Tomeo, Panos Markopoulos

Proceedings Volume 12538, 125381U (2023) https://doi.org/10.1117/12.2664331

KEYWORDS: Education and training, Statistical analysis, Covariance matrices, Matrices, Performance modeling, Overfitting, Machine learning, Eigenvectors, Databases, Covariance, Classification systems, Dimension reduction

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Proceedings Article | 15 May 2020 Presentation + Paper

Vehicle detection from multi-modal aerial imagery using YOLOv3 with mid-level fusion

Mayur Dhanaraj, Manish Sharma, Tiyasa Sarkar, Srivallabha Karnam, Dimitris Chachlakis, Raymond Ptucha, Panos Markopoulos, Eli Saber

Proceedings Volume 11395, 1139506 (2020) https://doi.org/10.1117/12.2558115

KEYWORDS: RGB color model, Infrared imaging, Image fusion, Airborne remote sensing, Target detection, Feature extraction, Data fusion, Defense and security, Neural networks

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SPIE Journal Paper | 16 July 2019

Spatial–spectral segmentation of hyperspectral images for subpixel target detection

Yilong Liang, Panos Markopoulos, Eli Saber

JARS, Vol. 13, Issue 03, 036502, (July 2019) https://doi.org/10.1117/12.10.1117/1.JRS.13.036502

KEYWORDS: Image segmentation, Target detection, Sensors, Hyperspectral imaging, Detection and tracking algorithms, Statistical analysis, Image processing algorithms and systems, Expectation maximization algorithms, Hyperspectral target detection, Image filtering

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Proceedings Article | 13 May 2019 Paper

Options for multimodal classification based on L1-Tucker decomposition

Dimitris Chachlakis, Mayur Dhanaraj, Ashley Prater-Bennette, Panos Markopoulos

Proceedings Volume 10989, 109890O (2019) https://doi.org/10.1117/12.2520140

KEYWORDS: Feature extraction, Video, Binary data, Data processing, Image classification, Signal detection, Matrices, Resistance, Microelectronics, Electrical engineering, Data analysis

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Proceedings Article | 14 May 2018 Paper

A linear discriminative analysis based fall motion detector using radar

Sivan Zlotnikov, Patrick Somaru, Panos Markopoulos, Fauzia Ahmad

Proceedings Volume 10658, 106580D (2018) https://doi.org/10.1117/12.2311574

KEYWORDS: Doppler effect, Principal component analysis, Radar, Feature extraction, Motion analysis, Sensors, Continuous wave operation

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Proceedings Article | 14 May 2018 Paper

Robust decomposition of 3-way tensors based on L1-norm

Dimitris Chachlakis, Panos Markopoulos

Proceedings Volume 10658, 1065807 (2018) https://doi.org/10.1117/12.2307843

KEYWORDS: Principal component analysis, Matrices, Data processing, Resistance, Signal processing, Statistical analysis, Reconstruction algorithms, Microelectronics, Electrical engineering, Machine learning

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Proceedings Article | 5 May 2017 Presentation + Paper

L1-norm principal-component analysis in L2-norm-reduced-rank data subspaces

Panos Markopoulos, Dimitris Pados, George Karystinos, Michael Langberg

Proceedings Volume 10211, 1021104 (2017) https://doi.org/10.1117/12.2263733

KEYWORDS: Electrical engineering, Detection and tracking algorithms, Sensors, Algorithms, Ultraviolet radiation, Spectral data processing, Statistical analysis, Principal component analysis, Astatine, Resistance, Signal processing, Data processing, Analytics

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Proceedings Article | 5 May 2017 Presentation + Paper

Visual tracking with L1-Grassmann manifold modeling

Dimitris Chachlakis, Panos Markopoulos, Raj Muchhala, Andreas Savakis

Proceedings Volume 10211, 1021102 (2017) https://doi.org/10.1117/12.2263691

KEYWORDS: Optical tracking, Visual process modeling, Video, Object recognition, Principal component analysis, Optimization (mathematics), Optical imaging, Detection and tracking algorithms, Resistance, Ultraviolet radiation, Microelectronics, Electrical engineering, Computer engineering

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Proceedings Article | 5 May 2017 Presentation + Paper

Adaptive sparse-binary waveform design for all-spectrum channelization

George Sklivanitis, Panos Markopoulos, Stella Batalama, Dimitris Pados

Proceedings Volume 10211, 102110B (2017) https://doi.org/10.1117/12.2262311

KEYWORDS: Picosecond phenomena, High dynamic range imaging, Information technology, Phase shift keying, Transmitters, Matrices, Binary data, Data hiding, Modulation, Linear filtering, Receivers, Telecommunications

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Proceedings Article | 23 May 2014 Paper

Direction finding with L1-norm subspaces

P. Markopoulos, N. Tsagkarakis, D. Pados, G. Karystinos

Proceedings Volume 9109, 91090J (2014) https://doi.org/10.1117/12.2053049

KEYWORDS: Signal to noise ratio, Resistance, Sensors, Signal processing, Contamination, Receivers, Electrical engineering, Electronics engineering, Computer engineering, Data analysis

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