Back
Return
Biography

Prof. Gerhard Ritter

University of Florida, USA


Email: ritter@cise.ufl.edu


Qualifications

1971  Ph.D., University of Wisconsin-Madison, USA


Publications (selected)


  1. G.X. Ritter and G. Urcid, Perfect Recovery from Noisy Input Patterns with a Dendritic Lattice Associative Memory,  Proceedings of the International Joint Conference on Neural Networks (IEEE/INNS), San Jose, CA, 2011, pp. 503-510.
  2. G. X. Ritter and  G. Urcid. “A Lattice  Matrix Method for Hyperspectral Image Unmixing,” Information Science, 181. Elsevier Science Publishers B.V.: Amsterdam, 2011, pp. 1787-1803.
  3. G. X. Ritter and G. Urcid. “Lattice Algebra Approach to Endmember Determination in Hyperspectral Imagery.” Advances in Imaging and Electron Physics, Vol. 160. Ed. P. Hawkes. Academic Press: Burlington, Massachusetts, 2010, pp. 113-169.
  4. G. Urcid, J.C. Valdiviezo, G.X. Ritter, Lattice Associative Memories for Segmenting Color Images in Different Color Spaces, IEEE Conference on Hybrid Artificial Intelligence Systems (HAIS), San Sebastian, Spain, 2010, pp.
  5. G.X. Ritter and G. Urcid, Learning in Lattice Neural Networks that Employ Dendritic Computing, Computational Intelligence Based on Lattice Theory Vol. 67 of Studies in Computational Intelligence. Eds. V.G. Kaburlasos and G.X. Ritter. Springer Science+Business Media: Berlin Heidelberg, 2007, pp 25-44.
  6. G. Urcid and G. X. Ritter. “Noise Masking for Pattern Recall Using a Single Lattice Matrix Associative Memory.” Computational Intelligence Based on Lattice Theory. Vol. 67 of Studies in Computational Intelligence. Edited by V.G. Kaburlasos and G.X. Ritter. Springer Science+Business Media: Berlin Heidelberg, 2007, pp. 81-100.
  7. Computational Intelligence Based on Lattice Theory Vol. 67 of Studies in Computational Intelligence. Edited by V.G. Kaburlasos and G.X. Ritter. Springer Science+Business Media: Berlin Heidelberg, 2007, pp. 45-58.
  8. G.X. Ritter and L. Iancu. “A Lattice Algebra Approach to Neural Computation.” Handbook of Computational Geometry for Pattern Recognition, Computer Vision, Neurocomputing and Robotics. Springer Science+Business Media: Berlin Heidelberg, 2005, pp. 97-129.
  9. G.X. Ritter and L. Iancu, Lattice Algebra Approach to Neural Networks and Pattern Classification,” Pattern Recognition and Image Analysis 14(2). Nauka/Interperiodica Publishing: Moscow, 2004, pp. 191-198.
  10. G.X. Ritter and L. Iancu. “Lattice Algebra Approach to Neural Networks and PPattern Recognition, Image Analysis and Applications”. Vol. 3287 of Lecture Notes in Computer Science. Springer Science+Business Media: Berlin Heidelberg, 2004, pp. 163-170.
  11. G.X. Ritter and G. Urcid, Lattice Algebra Approach to Single-Neuron Computation, IEEE Trans. Neural Networks, 14(2), 2003, pp.282-296.
  12. G.X. Ritter, P. Gader, A.K. Hocaoglu, and L. Iancu. Automatic Acoustic Mine Detection Using Morphological Perceptrons, Journal of the Acoustical Society of America, 112(5), 2002.
  13. W.C. Hu, K.H. Chang, G.X. Ritter, “Web Class: Web document classification using modified decision trees,” in Proceedings 38th Annual ACM Southeast Conference, Clemson, SC., April 2000, pp. 262-263.
  14. J.N. Wilson, G.X. Ritter, E.J. Riedy. An Image Algebra Based SIMD Image Processing Environment, in VISUAL COMMUNICATIONS and IMAGE PROCESSING PROCESSING, Chapter 17, Marcel Dekker Inc., New York, 1999.
  15. W.C. Hu, G.X. Ritter, and M.S. Schmalz, “Approximating the Longest Approximate Common Subsequence Problem,” in Proceedings of the 36th Annual Southeast ACM Conference, Marietta, GA., April, 1998, pp. 166-172.
  16. G.X. Ritter and J.N. Wilson. Handbook of Cpmputer Vision Algorithmd in Image Algebra, CRC Press, Boca Raton, FL., 1996.
  17. H. Shi, G.X. Ritter, and J.N. Wilson. “Parallel Image Processing with Image Algebra on SIMD Mesh-connected Computers.” Advances in Imaging and Electron Physics, Vol. 90. Edited by P. Hawkes. Academic Press: New York, New York, 1995.