Gerhard Ritter
· Ph.D. ProfessorUniversity of Florida · Computer & Information Science & Engineering
Active 1952–2021
About
Gerhard Ritter is a professor at the University of Florida, serving in the Department of Computer and Information Science and Engineering as well as the Department of Mathematics. His primary research interest is in the field of computer vision. He has contributed to this area through the development of UF's image algebra, an algebraic notation designed for specifying computer vision and image processing transformations. His work in this domain has led to the publication of a book titled 'Handbook of Computer Vision Algorithms in Image Algebra' with Joe Wilson, which provides specifications of over 80 computer vision and image processing algorithms using image algebra. In addition to his research, Professor Ritter has been involved in teaching various courses, including Neural Networks for Computing, Numerical Analysis, and Digital Image Processing, demonstrating his active engagement in education within his fields of expertise.
Research topics
- Computer Science
- Artificial Intelligence
- Mathematics
- Physics
- Machine Learning
- Pure mathematics
Selected publications
2021-08-06
book-chapter1st authorCorrespondingIntroduction to Lattice Algebra
2021 · 14 citations
1st authorCorresponding- Computer Science
- Computer Science
- Pure mathematics
Lattice theory extends into virtually every branch of mathematics, ranging from measure theory and convex geometry to probability theory and topology. A more recent development has been the rapid escalation of employing lattice theory for various applications outside the domain of pure mathematics. These applications range from electronic communication theory and gate array devices that implement Boolean logic to artificial intelligence and computer science in general. Introduction to Lattice Algebra: With Applications in AI, Pattern Recognition, Image Analysis, and Biomimetic Neural Networks lays emphasis on two subjects, the first being lattice algebra and the second the practical applications of that algebra. This textbook is intended to be used for a special topics course in artificial intelligence with a focus on pattern recognition, multispectral image analysis, and biomimetic artificial neural networks. The book is self-contained and – depending on the student’s major – can be used for a senior undergraduate level or first-year graduate level course. The book is also an ideal self-study guide for researchers and professionals in the above-mentioned disciplines. Features Filled with instructive examples and exercises to help build understanding Suitable for researchers, professionals and students, both in mathematics and computer science Contains numerous exercises.
Learning in Biomimetic Neural Networks
2021-08-06
book-chapter1st authorCorresponding2021-08-06
book-chapter1st authorCorresponding2021-08-06
book-chapter1st authorCorrespondingLattice-Based Biomimetic Neural Networks
2021-08-06
book-chapter1st authorCorrespondingImage Unmixing and Segmentation
2021-08-06
book-chapter1st authorCorrespondingMatrix-Based Lattice Associative Memories
2021-08-06
book-chapter1st authorCorresponding2021-08-06
book-chapter1st authorCorresponding2021-08-06
book-chapter1st authorCorresponding
Frequent coauthors
- 49 shared
Gonzalo Urcid
National Institute of Astrophysics, Optics and Electronics
- 48 shared
Mark S. Schmalz
- 22 shared
Joseph N. Wilson
University of Florida
- 17 shared
Peter Sussner
Universidade Estadual de Campinas (UNICAMP)
- 12 shared
Frank M. Caimi
- 9 shared
Wen‐Chen Hu
University of North Dakota
- 9 shared
Hongchi Shi
Texas State University
- 8 shared
Jennifer Newman
Iowa State University
Awards & honors
- Member of the European Academy of Science
- General Ronald W. Yates Award for Excellence in Technology T…
- Silver Core Award, International Federation for Information…
- Fellow of the International Society of Optical Engineering –…
- Founding member and first Chair of the Special Interest Grou…
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