Neural learning algorithm for halftoning
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 of...
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| Published in | Optics communications Vol. 113; no. 4; pp. 360 - 364 |
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| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
Amsterdam
Elsevier B.V
1995
Elsevier Science |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0030-4018 1873-0310 |
| DOI | 10.1016/0030-4018(94)00570-K |
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| 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. |
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| AbstractList | 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. |
| Author | Tuttaß, T. Bryngdahl, O. |
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| EndPage | 364 |
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| Keywords | Fourier transformation Function block diagram Grey level image Image processing Iterative process Algorithmics Theoretical study Neural network Learning algorithm Half tone image Adaptation Mean square error |
| Language | English |
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| References | Weissbach, Wyrowski (bib1) 1992; 31 Minsky, Papert (bib4) 1969 Broja, Wyrowski, Bryngdahl (bib2) 1989; 69 Tuttaß, Bryngdahl (bib6) 1993; 99 Beale, Jackson (bib3) 1990 Rumelhart, Hinton, Williams (bib8) 1986; Vol. 1 Hopfield, Tank (bib5) 1985; 52 Tuttaß, Broja, Bryngdahl (bib7) 1993 Beale (10.1016/0030-4018(94)00570-K_bib3) 1990 Minsky (10.1016/0030-4018(94)00570-K_bib4) 1969 Tuttaß (10.1016/0030-4018(94)00570-K_bib7) 1993 Weissbach (10.1016/0030-4018(94)00570-K_bib1) 1992; 31 Broja (10.1016/0030-4018(94)00570-K_bib2) 1989; 69 Hopfield (10.1016/0030-4018(94)00570-K_bib5) 1985; 52 Rumelhart (10.1016/0030-4018(94)00570-K_bib8) 1986; Vol. 1 Tuttaß (10.1016/0030-4018(94)00570-K_bib6) 1993; 99 |
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| Snippet | Most processes used for halftoning consist of linear and nonlinear elements. Neural networks offer the possibility of combining these elements in a general and... |
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| SubjectTerms | Applied sciences Artificial intelligence Computer science; control theory; systems Electric, optical and optoelectronic circuits Electronics Exact sciences and technology Image processing Information, signal and communications theory Neural networks Pattern recognition. Digital image processing. Computational geometry Signal processing Telecommunications and information theory |
| Title | Neural learning algorithm for halftoning |
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