Soft Computing for Image Processing:
Any task that involves decision-making can benefit from soft computing techniques which allow premature decisions to be deferred. The processing and analysis of images is no exception to this rule. In the classical image analysis paradigm, the first step is nearly always some sort of segmentation pr...
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| Weitere Verfasser: | , , |
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| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Heidelberg
Physica-Verlag HD
2000
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| Schriftenreihe: | Studies in Fuzziness and Soft Computing
42 |
| Schlagworte: | |
| Online-Zugang: | FHI01 BTU01 Volltext |
| Zusammenfassung: | Any task that involves decision-making can benefit from soft computing techniques which allow premature decisions to be deferred. The processing and analysis of images is no exception to this rule. In the classical image analysis paradigm, the first step is nearly always some sort of segmentation process in which the image is divided into (hopefully, meaningful) parts. It was pointed out nearly 30 years ago by Prewitt (1] that the decisions involved in image segmentation could be postponed by regarding the image parts as fuzzy, rather than crisp, subsets of the image. It was also realized very early that many basic properties of and operations on image subsets could be extended to fuzzy subsets; for example, the classic paper on fuzzy sets by Zadeh [2] discussed the "set algebra" of fuzzy sets (using sup for union and inf for intersection), and extended the defmition of convexity to fuzzy sets. These and similar ideas allowed many of the methods of image analysis to be generalized to fuzzy image parts. For are cent review on geometric description of fuzzy sets see, e. g. , [3]. Fuzzy methods are also valuable in image processing and coding, where learning processes can be important in choosing the parameters of filters, quantizers, etc |
| Beschreibung: | 1 Online-Ressource (XVII, 591 p. 332 illus) |
| ISBN: | 9783790818581 |
| DOI: | 10.1007/978-3-7908-1858-1 |
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| 520 | |a Any task that involves decision-making can benefit from soft computing techniques which allow premature decisions to be deferred. The processing and analysis of images is no exception to this rule. In the classical image analysis paradigm, the first step is nearly always some sort of segmentation process in which the image is divided into (hopefully, meaningful) parts. It was pointed out nearly 30 years ago by Prewitt (1] that the decisions involved in image segmentation could be postponed by regarding the image parts as fuzzy, rather than crisp, subsets of the image. It was also realized very early that many basic properties of and operations on image subsets could be extended to fuzzy subsets; for example, the classic paper on fuzzy sets by Zadeh [2] discussed the "set algebra" of fuzzy sets (using sup for union and inf for intersection), and extended the defmition of convexity to fuzzy sets. These and similar ideas allowed many of the methods of image analysis to be generalized to fuzzy image parts. For are cent review on geometric description of fuzzy sets see, e. g. , [3]. Fuzzy methods are also valuable in image processing and coding, where learning processes can be important in choosing the parameters of filters, quantizers, etc | ||
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Datensatz im Suchindex
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| isbn | 9783790818581 |
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| spelling | Soft Computing for Image Processing edited by Sankar K. Pal, Ashish Ghosh, Malay K. Kundu Heidelberg Physica-Verlag HD 2000 1 Online-Ressource (XVII, 591 p. 332 illus) txt rdacontent c rdamedia cr rdacarrier Studies in Fuzziness and Soft Computing 42 Any task that involves decision-making can benefit from soft computing techniques which allow premature decisions to be deferred. The processing and analysis of images is no exception to this rule. In the classical image analysis paradigm, the first step is nearly always some sort of segmentation process in which the image is divided into (hopefully, meaningful) parts. It was pointed out nearly 30 years ago by Prewitt (1] that the decisions involved in image segmentation could be postponed by regarding the image parts as fuzzy, rather than crisp, subsets of the image. It was also realized very early that many basic properties of and operations on image subsets could be extended to fuzzy subsets; for example, the classic paper on fuzzy sets by Zadeh [2] discussed the "set algebra" of fuzzy sets (using sup for union and inf for intersection), and extended the defmition of convexity to fuzzy sets. These and similar ideas allowed many of the methods of image analysis to be generalized to fuzzy image parts. For are cent review on geometric description of fuzzy sets see, e. g. , [3]. Fuzzy methods are also valuable in image processing and coding, where learning processes can be important in choosing the parameters of filters, quantizers, etc Computer Science Image Processing and Computer Vision Artificial Intelligence (incl. Robotics) IT in Business Computer science Information technology Business / Data processing Artificial intelligence Image processing Bildverarbeitung (DE-588)4006684-8 gnd rswk-swf Soft Computing (DE-588)4455833-8 gnd rswk-swf 1\p (DE-588)4143413-4 Aufsatzsammlung gnd-content Bildverarbeitung (DE-588)4006684-8 s Soft Computing (DE-588)4455833-8 s 2\p DE-604 Pal, Sankar K. edt Ghosh, Ashish edt Kundu, Malay K. edt Erscheint auch als Druck-Ausgabe 9783790824681 https://doi.org/10.1007/978-3-7908-1858-1 Verlag URL des Erstveröffentlichers Volltext 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk 2\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Soft Computing for Image Processing Computer Science Image Processing and Computer Vision Artificial Intelligence (incl. Robotics) IT in Business Computer science Information technology Business / Data processing Artificial intelligence Image processing Bildverarbeitung (DE-588)4006684-8 gnd Soft Computing (DE-588)4455833-8 gnd |
| subject_GND | (DE-588)4006684-8 (DE-588)4455833-8 (DE-588)4143413-4 |
| title | Soft Computing for Image Processing |
| title_auth | Soft Computing for Image Processing |
| title_exact_search | Soft Computing for Image Processing |
| title_full | Soft Computing for Image Processing edited by Sankar K. Pal, Ashish Ghosh, Malay K. Kundu |
| title_fullStr | Soft Computing for Image Processing edited by Sankar K. Pal, Ashish Ghosh, Malay K. Kundu |
| title_full_unstemmed | Soft Computing for Image Processing edited by Sankar K. Pal, Ashish Ghosh, Malay K. Kundu |
| title_short | Soft Computing for Image Processing |
| title_sort | soft computing for image processing |
| topic | Computer Science Image Processing and Computer Vision Artificial Intelligence (incl. Robotics) IT in Business Computer science Information technology Business / Data processing Artificial intelligence Image processing Bildverarbeitung (DE-588)4006684-8 gnd Soft Computing (DE-588)4455833-8 gnd |
| topic_facet | Computer Science Image Processing and Computer Vision Artificial Intelligence (incl. Robotics) IT in Business Computer science Information technology Business / Data processing Artificial intelligence Image processing Bildverarbeitung Soft Computing Aufsatzsammlung |
| url | https://doi.org/10.1007/978-3-7908-1858-1 |
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