Mr. Ryan R. McCoppin Profile

Ryan R. McCoppin

Graduate Research Assistant at Wright State Univ

SPIE Involvement:

Author

Area of Expertise:

Machine Learning , Artificial Intelligence , Computer Vision , Deep learning

For the past few years I have been working on the gender classification problem in EO and LIDAR mediums. I am interested in theory of computer science and machine learning and its implications on future learning techniques and AI. I am especially fond of stochastic methods of learning and physics-inspired computation.

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

Read Abstract +

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

Read Abstract +

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

Read Abstract +

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.

Keywords/Phrases

Search In:

Publication Years