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Johnson, Amos

Amos Johnson Jr., PH.D.

Morehouse College

Science, Technology, Engineering and Mathematics Division Faculty
  • Associate Professor, Computer Science
Linkedin
Education
  • Morehouse College
    Bachelor of Science, Electrical Engineering
  • Morehouse College
    Bachelor of Science, General Science
  • Georgia Institute of Technology
    Master of Science, Electrical and Computer Engineering
  • Georgia Institute of Technology
    Doctor of Philosophy, Electrical Engineering
  • Faith School of Ministry
Dr. Amos Johnson’s research background is in pattern recognition and pattern discovery. 

He has developed pattern recognition algorithms to identify people from their walking patterns and created algorithms to discover patterns in video surveillance data. As an associate professor, he has focused on providing students with the best teaching instruction possible and creating enhancements to their learning process in computer science.

Contact:

Email
Amos.Johnson@morehouse.edu

Office Location
TBD

Phone
TBD

Office Hours
TBD

RESEARCH INTERESTS

  • Interactive Visual Art Modules
    Using image recognition techniques to interact with a virtual reality world, we studied
    the effectiveness of using non-traditional input devices and simulation environments
    that include artistic elements to increase a student’s interest in science, technology,
    engineering, and math.
  • Learning Variations of a Single Predefined-Activity
    Created soft systems to learn the various ways a single predefined activity may occur
    from a limited amount of visual data of the activity. Using this information, the
    system will classify a new instance of the activity as either belonging to one of the
    many variations of the activity or as an abnormally.
  • Gait Recognition
    Our gait-recognition method recovers static body and stride parameters of subjects as
    they walk. Our technique does not directly analyze the dynamic gait patterns, but uses
    the action of walking to extract relative body parameters.
  • Parametric Metrics to Estimate Performance of Biometric Techniques
    Parametric metrics, expected confusion and transformed expected confusion, are
    developed as means to estimate verification performance of biometric techniques.
  • Method to Predict Cumulative Match Characteristic Performance
    A method to predict cumulative match characteristic (CMC) curve performance for
    large galleries of individuals using data from a significantly smaller gallery is derived.

GRANTS

  • National Science Foundation / $1 Million
    2006
    Encouraging students to pursue undergraduate degrees in STEM fields by exposing them to fundamental STEM paradigms via Interactive visual arts modulesa 
  • FACES / $30k
    2006
    Career Initiation Grant
  • P&G / $10k
    2011
    Mobile Computing Lab
  • The Leona M. and Harry B. Helmsley Charitable Trust / $5 Million
    2015
    Vertically-Integrated Projects (joint grant with Georgia Tech)
  • Defense Intelligence Agency / $1.5 Million
    2019
    Intelligence Community Center for Academic Excellence (joint grant with VA Tech

Publications & Presentations

E.J. Coyle, J.V. Krogmeier, R.T. Abler, A. Johnson, S. Marshall and B.E.
Gilchrist, “The Vertically-Integrated Projects (VIP) Program: Leveraging
Faculty Research Interests to Transform Undergraduate STEM Education,”
Chapter in Transforming Institutions: Undergraduate STEM Education for the
21st Century, edited by G.C. Weaver, W.D. Burgess, A.L. Childress, and L.
Slakey; Purdue University Press, West Lafayette, IN 2015; pp. 223-234.

E.J. Coyle, J. V. Krogmeier, Randal T. Abler, A. Johnson, S. Marshall, and B.E.
Gilchrist, "The Vertically Integrated Projects (VIP) Program -- Leveraging
Faculty Research Interests to Transform Undergraduate STEM Education,"
Presented at Transforming Institutions: 21st Century Undergraduate STEM
Education Conference, Indianapolis IN, Oct. 23-24, 2014.

J. Melkers, A. Kiopa, R. Abler, E. Coyle, J. Ernst, J. Krogmeier, A. Johnson,
“The Social Web of Engineering Education: Knowledge Exchange in
Integrated Project Teams,” submitted to the 2012 ASEE Annual Conference
and Exposition, San Antonio, TX, June 10-13, 2012.

Earl C., Johnson A., Yelpaala, K., Good T. Making project team
recommendations from online information sources, 5th International AAAI
Conference on Weblogs and Social Media (ICWSM 2011), July 2011.

Maribeth Gandy, Brian Jones, Scott Robertson, Tiffany O'Quinn, Amos Johnson.
"Rapidly Prototyping Marker Based Tangible User Interfaces.” HCI
International, July 2009.

R. Hamid, S. Maddi, A. Johnson, A. Bobick, I. Essa, C. Isbell. A Novel
Sequence Representation for Unsupervised Analysis of Human Activities. Artificial Intelligence Journal, August 2008.

Hamid, Maddi, Johnson, Bobick, Essa, Isbell,“Discovery and Characterization
of Activities from Event-Streams” In proceedings of the 21st Conference on
Uncertainty in Artifcal Intelligence, Edinburgh, Scotland, July 2005.

Hamid, Johnson, Batta, Bobick, Isbell, Coleman, “Detection and Explanation of
Anomalous Activities: Representing Activities as Bags of Event n-Grams” To
in IEEE Conference on Computer Vision and Pattern Recognition, San Diego,
CA, June 2005.

Johnson, Sun, Bobick, “Using similarity scores from a small gallery to
estimate recognition performance for larger galleries” In IEEE International
Workshop on Analysis and Modeling of Faces and Gestures held in conjunction
with the International Conference on Computer Vision, Nice, France, October
2003

 Johnson, Sun, Bobick, “Predicting large population data cumulative match
characteristic performance from small population data” In proceedings of the
4th International Conference on Audio- and Video Based Biometric Person
Authentication, University of Surrey, Guildford, UK June 9-11, 2003.

Johnson, Bobick, “Relationship between identification metrics: Expected
Confusion and Area Under a ROC curve” In Proceedings of IEEE
International Conference on Pattern Recognition, Quebec, Canada, August 2002.

Bobick, Johnson, “Gait recognition using static activity-specific parameters”
In Proceedings of IEEE Computer Vision and Pattern Recognition Conference,
Kauai, Hawaii, December 2001.

Kwatra, Bobick, Johnson, “Temporal integration of multiple silhouette-based
body-part hypotheses”, In Proceedings of IEEE Computer Vision and Pattern
Recognition Conference, Kauai, Hawaii, December 2001.

Johnson, Bobick, “A Multi-view Method for Gait Recognition Using Static
Body Parameters” In 3rd International Conference on Audio- and Video Based
Biometric Person Authentication, pages 301-311, Halmstad, Sweden, June 2001

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