Prof. Mateen M. Rizki Profile

Prof. Mateen M. Rizki

SPIE Involvement:

Author

Proceedings Article | 20 May 2015 Paper

Exploring EO vehicle recognition performance using manifolds as a function of lighting condition variability

Olga Mendoza-Schrock, Mateen Rizki, Edmund Zelnio, Vincent Velten, Frederick Garber, Michael Raymer, John Gallagher

Proceedings Volume 9464, 94640I (2015) https://doi.org/10.1117/12.2178408

KEYWORDS: Light sources and illumination, Diffusion, Electro optics, Target recognition, Intelligence systems, Cameras, Analytical research, Surveillance, Reconnaissance, Image classification

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Proceedings Article | 10 June 2014 Paper

Gender classification under extended operating conditions

Howard Rude, Mateen Rizki

Proceedings Volume 9079, 90790R (2014) https://doi.org/10.1117/12.2052867

KEYWORDS: Image classification, Video, Image resolution, Classification systems, Feature extraction, Cameras, Human subjects, Binary data, Computer simulations, Video processing

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Proceedings Article | 10 June 2014 Paper

Deep learning for image classification

Ryan McCoppin, Mateen Rizki

Proceedings Volume 9079, 90790T (2014) https://doi.org/10.1117/12.2054045

KEYWORDS: Image classification, Standards development, Data modeling, Classification systems, Databases, Feature extraction, Binary data, Machine learning, Convolutional neural networks, Electro optics

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Proceedings Article | 28 May 2013 Paper

Electro-optical seasonal weather and gender data collection

Ryan McCoppin, Nathan Koester, Howard Rude, Mateen Rizki, Louis Tamburino, Andrew Freeman, Olga Mendoza-Schrock

Proceedings Volume 8751, 87510H (2013) https://doi.org/10.1117/12.2018903

KEYWORDS: Cameras, Classification systems, Image registration, Algorithm development, Electro optics, Machine learning, Databases, Human subjects, Data modeling, Image classification

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Proceedings Article | 9 May 2012 Paper

Exploring point-cloud features from partial body views for gender classification

Aaron Fouts, Ryan McCoppin, Mateen Rizki, Louis Tamburino, Olga Mendoza-Schrock

Proceedings Volume 8402, 84020L (2012) https://doi.org/10.1117/12.921880

KEYWORDS: Clouds, Feature extraction, Databases, Cameras, Genetic algorithms, Analytical research, Human subjects, LIDAR, Algorithm development, Image classification

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In this paper we extend a previous exploration of histogram features extracted from 3D point cloud images of human subjects for gender discrimination. Feature extraction used a collection of concentric cylinders to define volumes for counting 3D points. The histogram features are characterized by a rotational axis and a selected set of volumes derived from the concentric cylinders. The point cloud images are drawn from the CAESAR anthropometric database provided by the Air Force Research Laboratory (AFRL) Human Effectiveness Directorate and SAE International. This database contains approximately 4400 high resolution LIDAR whole body scans of carefully posed human subjects. Success from our previous investigation was based on extracting features from full body coverage which required integration of multiple camera images. With the full body coverage, the central vertical body axis and orientation are readily obtainable; however, this is not the case with a one camera view providing less than one half body coverage. Assuming that the subjects are upright, we need to determine or estimate the position of the vertical axis and the orientation of the body about this axis relative to the camera. In past experiments the vertical axis was located through the center of mass of torso points projected on the ground plane and the body orientation derived using principle component analysis. In a natural extension of our previous work to partial body views, the absence of rotational invariance about the cylindrical axis greatly increases the difficulty for gender classification. Even the problem of estimating the axis is no longer simple. We describe some simple feasibility experiments that use partial image histograms. Here, the cylindrical axis is assumed to be known. We also discuss experiments with full body images that explore the sensitivity of classification accuracy relative to displacements of the cylindrical axis. Our initial results provide the basis for further investigation of more complex partial body viewing problems and new methods for estimating the two position coordinates for the axis location and the unknown body orientation angle.

Proceedings Article | 4 May 2012 Paper

Differential profiling of volatile organic compound biomarker signatures utilizing a logical statistical filter-set and novel hybrid evolutionary classifiers

Claude Grigsby, Michael Zmuda, Derek Boone, Tyler Highlander, Ryan Kramer, Mateen Rizki

Proceedings Volume 8402, 84020K (2012) https://doi.org/10.1117/12.920021

KEYWORDS: Profiling, Prototyping, Biological research, Statistical analysis, Visualization, Classification systems, Spectroscopy, Machine learning, Molecules, Ions

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Proceedings Article | 20 May 2011 Paper

Evolving point-cloud features for gender classification

Brittany Keen, Aaron Fouts, Mateen Rizki, Louis Tamburino, Olga Mendoza-Schrock

Proceedings Volume 8059, 80590N (2011) https://doi.org/10.1117/12.884764

KEYWORDS: Clouds, Feature extraction, Databases, Human subjects, Sensors, Evolutionary algorithms, Head, Genetic algorithms, Analytical research, LIDAR

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Proceedings Article | 9 May 2006 Paper

Evolving illumination parameters for improved target recognition

D. Courte, L. Tamburino, M. Rizki

Proceedings Volume 6229, 62290C (2006) https://doi.org/10.1117/12.665290

KEYWORDS: RGB color model, Target recognition, Computer science, Automatic target recognition, Algorithm development, Digital photography, Photography, Computer engineering, Object recognition, Computer security

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Proceedings Article | 2 September 2004 Paper

Target classification using morphological features

Michael Zmuda, Mateen Rizki, Louis Tamburino

Proceedings Volume 5427, (2004) https://doi.org/10.1117/12.548119

KEYWORDS: Sensors, Prototyping, Clouds, Image processing, Mathematical morphology, Pattern recognition, 3D image processing, Classification systems, Analytical research, 3D acquisition

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Proceedings Article | 9 July 1992 Paper

Feature extraction using morphological analysis of multiresolution gray-scale images

Louis Tamburino, Mateen Rizki, Michael Zmuda

Proceedings Volume 1699, (1992) https://doi.org/10.1117/12.138246

KEYWORDS: Sensors, Neural networks, Feature extraction, Binary data, Detection and tracking algorithms, Image resolution, Evolutionary algorithms, Image filtering, Morphological analysis, Fourier transforms

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Proceedings Article | 9 July 1992 Paper

Computational complexity of synthesizing target recognition programs

Michael Zmuda, Mateen Rizki, Louis Tamburino

Proceedings Volume 1699, (1992) https://doi.org/10.1117/12.138229

KEYWORDS: Target recognition, Silicon, Automatic target recognition, Information operations, Computer science, Computing systems, Computability theory, Detection and tracking algorithms, Computer engineering, Legal

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Proceedings Article | 1 June 1992 Paper

Automatic generation of morphological sequences

Michael Zmuda, Louis Tamburino, Mateen Rizki

Proceedings Volume 1769, (1992) https://doi.org/10.1117/12.60636

KEYWORDS: Silicon, Image processing, Detection and tracking algorithms, Information operations, Feature extraction, Chemical elements, Composites, Evolutionary algorithms, Aluminum, Binary data

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Proceedings Article | 1 August 1991 Paper

Efficient software techniques for morphological image processing with desktop computers

Michael Zmuda, Louis Tamburino, Mateen Rizki

Proceedings Volume 1470, (1991) https://doi.org/10.1117/12.44849

KEYWORDS: Image processing, Selenium, Binary data, Data storage, Computing systems, Mathematical morphology, Image storage, Image segmentation, Raster graphics, Logic

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Proceedings Article | 1 November 1990 Paper

Adaptive search for morphological feature detectors

Mateen Rizki, Louis Tamburino, Michael Zmuda

Proceedings Volume 1350, (1990) https://doi.org/10.1117/12.23583

KEYWORDS: Sensors, Image processing, Genetics, Prototyping, Pattern recognition, Genetic algorithms, Matrices, Image classification, Environmental sensing, Neural networks

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Showing 5 of 14 publications

SPIE Conference Volume | 3 May 2012

Evolutionary and Bio-Inspired Computation: Theory and Applications VI

Conference Committee Involvement (2)

Machine Intelligence and Bio-inspired Computation: Theory and Applications VII

2 May 2013 | Baltimore, Maryland, United States

Evolutionary and Bio-inspired Computation: Theory and Applications VI

25 April 2012 | Baltimore, Maryland, United States

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