1. Neural learning algorithm for halftoning
- Author
-
T. Tuttaß and O. Bryngdahl
- Subjects
Structure (mathematical logic) ,Artificial neural network ,Computer science ,ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION ,Image processing ,Atomic and Molecular Physics, and Optics ,Electronic, Optical and Magnetic Materials ,Image (mathematics) ,symbols.namesake ,Nonlinear system ,Fourier transform ,Simple (abstract algebra) ,Algorithmics ,symbols ,Electrical and Electronic Engineering ,Physical and Theoretical Chemistry ,Algorithm - Abstract
Most processes used for halftoning consist of linear and nonlinear elements. Neural networks offer the possibility of combining these elements in a general and flexible structure. Image binarization methods can be analysed and transfered to neural structures and typical neural learning algorithms offer new ways to treat the halftoning problem. We examine a simple learning algorithm and demonstrate the difficulties and possibilities concerning the halftoning problem.
- Published
- 1995
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