Dr. Stefan A. Robila Profile

Dr. Stefan A. Robila

Assistant Professor at Montclair State Univ

SPIE Involvement:

Author

Publications (18)

Proceedings Article | 19 July 2023 Paper

An extended reality environment for urban area environmental data analysis

Iman Cumberbatch, Stefan Robila

Proceedings Volume 12525, 125250L (2023) https://doi.org/10.1117/12.2668407

KEYWORDS: Virtual reality, Visualization, Data modeling, Data visualization, Computing systems, Scanning transmission electron microscopy, Vegetation, Data analysis, Visual process modeling, 3D modeling

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Proceedings Article | 18 May 2013 Paper

Impact of spatial complexity preprocessing on hyperspectral data unmixing

Stefan Robila, Kimberly Pirate, Terrance Hall

Proceedings Volume 8743, 874323 (2013) https://doi.org/10.1117/12.2015585

KEYWORDS: Independent component analysis, Hyperspectral imaging, Principal component analysis, Feature extraction, System on a chip, Image classification, Computer science, Multidimensional signal processing, Mining, Sensors

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Proceedings Article | 15 October 2012 Paper

Linear unmixing-based feature extraction for hyperspectral data in a high performance computing environment

Stefan Robila

Proceedings Volume 8515, 85150M (2012) https://doi.org/10.1117/12.928666

KEYWORDS: Data communications, System on a chip, Hyperspectral imaging, Feature extraction, Algorithm development, Distributed computing, Remote sensing, Data storage, Data processing, Solids

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Proceedings Article | 24 September 2011 Paper

Face recognition using spectral and spatial information

Stefan Robila, Marco Chang, Nisha D'Amico

Proceedings Volume 8135, 81351Q (2011) https://doi.org/10.1117/12.892743

KEYWORDS: Facial recognition systems, Image fusion, Hyperspectral imaging, Vegetation, Detection and tracking algorithms, Infrared imaging, Near infrared, Principal component analysis, Databases, Biometrics

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Proceedings Article | 3 June 2010 Paper

A feature-based approach for processing nanoscale images

Gregory Roughton, Aparna Varde, Stefan Robila, Jianyu Liang

Proceedings Volume 7729, 772911 (2010) https://doi.org/10.1117/12.853412

KEYWORDS: Image processing, Nanotechnology, Image analysis, Particles, Databases, Analytical research, Microscopes, Error analysis, Scanning electron microscopy, Electron microscopes

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Proceedings Article | 13 May 2010 Paper

Spectral face recognition using orthogonal subspace bases

Andrew Wimberly, Stefan Robila, Tansy Peplau

Proceedings Volume 7695, 76952E (2010) https://doi.org/10.1117/12.849892

KEYWORDS: Facial recognition systems, Principal component analysis, Databases, Image fusion, Hyperspectral imaging, Visible radiation, Infrared radiation, Sensors, Infrared imaging, Image sensors

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Proceedings Article | 27 January 2010 Paper

Band reduction for hyperspectral imagery processing

Stefan Robila

Proceedings Volume 7533, 75330W (2010) https://doi.org/10.1117/12.837953

KEYWORDS: Detection and tracking algorithms, Image processing, Feature selection, Hyperspectral imaging, Baryon acoustic oscillations, Feature extraction, Transform theory, Neodymium, Vegetation, Sensors

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Proceedings Article | 27 April 2009 Paper

Visualization of hyperspectral images

Mindy Schockling, Roberto Bonce, Angel Gutierrez, Stefan Robila

Proceedings Volume 7334, 733423 (2009) https://doi.org/10.1117/12.818566

KEYWORDS: Wavelets, Visualization, Hyperspectral imaging, Information visualization, Visible radiation, Visual analytics, Image visualization, Reflectivity, RGB color model, Image processing

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Proceedings Article | 27 April 2009 Paper

Investigating face recognition from hyperspectral data: impact of band extraction

Stefan Robila, Andrew LaChance, Shawna Ruff

Proceedings Volume 7334, 73341Y (2009) https://doi.org/10.1117/12.817025

KEYWORDS: Facial recognition systems, Independent component analysis, Databases, Principal component analysis, Infrared imaging, Thermography, Feature extraction, Infrared radiation, Sensors, Hyperspectral imaging

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Proceedings Article | 11 April 2008 Paper

Hyperspectral image processing: a direct image simplification method

Christopher Neylan, Tyler Rush, Angel Gutierrez, Stefan Robila

Proceedings Volume 6966, 69661Y (2008) https://doi.org/10.1117/12.780080

KEYWORDS: Hyperspectral imaging, Composites, Visualization, RGB color model, Principal component analysis, Visible radiation, Infrared radiation, Image processing, Sensors, Image sensors

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Proceedings Article | 3 March 2008 Paper

Toward hyperspectral face recognition

Stefan Robila

Proceedings Volume 6812, 68120X (2008) https://doi.org/10.1117/12.765268

KEYWORDS: Facial recognition systems, Visible radiation, Hyperspectral imaging, Infrared imaging, Sensors, Image fusion, Near infrared, Infrared radiation, Feature extraction, Thermography

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Proceedings Article | 2 October 2007 Paper

Grid computing for hyperspectral data processing

Stefan Robila, Nicholas Senedzuk

Proceedings Volume 6765, 67650A (2007) https://doi.org/10.1117/12.734746

KEYWORDS: Computing systems, Sun, Data processing, Hyperspectral imaging, Computer architecture, Systems modeling, Spectral data processing, Hyperspectral target detection, Target detection, Parallel computing

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Proceedings Article | 2 October 2006 Paper

A parallel unmixing algorithm for hyperspectral images

Stefan Robila, Lukasz Maciak

Proceedings Volume 6384, 63840F (2006) https://doi.org/10.1117/12.685655

KEYWORDS: Hyperspectral imaging, Principal component analysis, Independent component analysis, Vegetation, Reflectivity, System on a chip, Parallel processing, Image processing, Ceramics, Feature extraction

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Proceedings Article | 11 March 2005 Paper

Subpixel target detection in hyperspectral data using higher order statistics source separation algorithms

Stefan Robila

Proceedings Volume 5674, (2005) https://doi.org/10.1117/12.586971

KEYWORDS: Target detection, Detection and tracking algorithms, Principal component analysis, Hyperspectral target detection, Image filtering, Independent component analysis, Feature extraction, Hyperspectral imaging, Composites, Data modeling

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Proceedings Article | 12 August 2004 Paper

Distributed source separation algorithms for hyperspectral image processing

Stefan Robila

Proceedings Volume 5425, (2004) https://doi.org/10.1117/12.541892

KEYWORDS: Independent component analysis, Hyperspectral imaging, Principal component analysis, Feature extraction, Algorithm development, Detection and tracking algorithms, Image processing, Target detection, Data modeling, Spectral resolution

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Proceedings Article | 23 September 2003 Paper

Investigation of spectral screening techniques for independent-component-analysis-based hyperspectral image processing

Stefan Robila

Proceedings Volume 5093, (2003) https://doi.org/10.1117/12.487091

KEYWORDS: Independent component analysis, Hyperspectral imaging, Principal component analysis, Distance measurement, Image processing, Roads, Feature extraction, Data processing, Multispectral imaging, Algorithm development

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This paper investigates the effect of spectral screening on processing hyperspectral data through Independent Component Analysis (ICA). ICA is a multivariate data analysis method producing components that are statistically independent. In the context of the linear mixture model, the endmember abundances can be viewed as independent components, the endmembers forming the columns of the mixing matrix. In essence, the ICA processing can be seen as an alternative solution to endmember unmixing. In the context of feature extraction, each feature will be represented by an independent component, thus leading to maximum separability among features. Spectral screening is defined as the reduction of the image cube to a subset of representative pixel vectors with the goal of achieving a considerable speedup in further processing. At the base of spectral screening are the measure used to assess the similarity between two pixel vectors and a threshold value. Two pixel vectors are similar if the value yielded by the similarity measure is smaller than the threshold and dissimilar otherwise. The spectral screened subset has to be formed such that any two vectors in the subset are dissimilar and for any vector in the original image cube there is a similar vector in the subset. The method we present uses spectral angle as distance measure. A necessary condition for the success of spectral screening is that the result of processing the reduced subset can be extended to the entire data. Intuitively, a larger subset would lead to increased accuracy. However, the overhead introduced by the spectral screening is directly proportional to the subset size. Our investigation has focused on finding the “ideal” threshold value that maximizes both the accuracy and the speedup. The practical effectiveness of the methods was tested on HYDICE data. The results indicate that considerable speedup is obtained without a considerable loss of accuracy. The presented method leads to a significant increase in computational efficiency allowing faster processing of hyperspectral images.

Proceedings Article | 16 September 2003 Paper

Further results in the use of independent component analysis for target detection in hyperspectral images

Stefan Robila, Pramod Varshney

Proceedings Volume 5094, (2003) https://doi.org/10.1117/12.487452

KEYWORDS: Independent component analysis, Hyperspectral imaging, Target detection, Detection and tracking algorithms, Principal component analysis, Hyperspectral target detection, Algorithm development, Distance measurement, Data processing, Image processing

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Proceedings Article | 25 July 2002 Paper

Target detection in hyperspectral images based on independent component analysis

Stefan Robila, Pramod Varshney

Proceedings Volume 4726, (2002) https://doi.org/10.1117/12.477024

KEYWORDS: Independent component analysis, Target detection, Hyperspectral imaging, Detection and tracking algorithms, Hyperspectral target detection, Principal component analysis, Image processing, Feature extraction, Data modeling, Roads

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Showing 5 of 18 publications

Conference Committee Involvement (4)

High-Performance Computing in Remote Sensing

22 September 2014 | Amsterdam, Netherlands

High-Performance Computing in Remote Sensing III

25 September 2013 | Dresden, Germany

High-Performance Computing in Remote Sensing

26 September 2012 | Edinburgh, United Kingdom

High-Performance Computing in Remote Sensing

19 September 2011 | Prague, Czech Republic

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