Image Segmentation and Compression Using Hidden Markov Models:
In the current age of information technology, the issues of distributing and utilizing images efficiently and effectively are of substantial concern. Solutions to many of the problems arising from these issues are provided by techniques of image processing, among which segmentation and compression a...
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Boston, MA
Springer US
2000
|
| Schriftenreihe: | The Springer International Series in Engineering and Computer Science
571 |
| Schlagworte: | |
| Online-Zugang: | FHI01 BTU01 Volltext |
| Zusammenfassung: | In the current age of information technology, the issues of distributing and utilizing images efficiently and effectively are of substantial concern. Solutions to many of the problems arising from these issues are provided by techniques of image processing, among which segmentation and compression are topics of this book. Image segmentation is a process for dividing an image into its constituent parts. For block-based segmentation using statistical classification, an image is divided into blocks and a feature vector is formed for each block by grouping statistics of its pixel intensities. Conventional block-based segmentation algorithms classify each block separately, assuming independence of feature vectors. Image Segmentation and Compression Using Hidden Markov Models presents a new algorithm that models the statistical dependence among image blocks by two dimensional hidden Markov models (HMMs). Formulas for estimating the model according to the maximum likelihood criterion are derived from the EM algorithm. To segment an image, optimal classes are searched jointly for all the blocks by the maximum a posteriori (MAP) rule. The 2-D HMM is extended to multiresolution so that more context information is exploited in classification and fast progressive segmentation schemes can be formed naturally. The second issue addressed in the book is the design of joint compression and classification systems using the 2-D HMM and vector quantization. A classifier designed with the side goal of good compression often outperforms one aimed solely at classification because overfitting to training data is suppressed by vector quantization. Image Segmentation and Compression Using Hidden Markov Models is an essential reference source for researchers and engineers working in statistical signal processing or image processing, especially those who are interested in hidden Markov models. It is also of value to those working on statistical modeling |
| Beschreibung: | 1 Online-Ressource (XIII, 141 p) |
| ISBN: | 9781461544975 |
| DOI: | 10.1007/978-1-4615-4497-5 |
Internformat
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| 520 | |a In the current age of information technology, the issues of distributing and utilizing images efficiently and effectively are of substantial concern. Solutions to many of the problems arising from these issues are provided by techniques of image processing, among which segmentation and compression are topics of this book. Image segmentation is a process for dividing an image into its constituent parts. For block-based segmentation using statistical classification, an image is divided into blocks and a feature vector is formed for each block by grouping statistics of its pixel intensities. Conventional block-based segmentation algorithms classify each block separately, assuming independence of feature vectors. Image Segmentation and Compression Using Hidden Markov Models presents a new algorithm that models the statistical dependence among image blocks by two dimensional hidden Markov models (HMMs). Formulas for estimating the model according to the maximum likelihood criterion are derived from the EM algorithm. To segment an image, optimal classes are searched jointly for all the blocks by the maximum a posteriori (MAP) rule. The 2-D HMM is extended to multiresolution so that more context information is exploited in classification and fast progressive segmentation schemes can be formed naturally. The second issue addressed in the book is the design of joint compression and classification systems using the 2-D HMM and vector quantization. A classifier designed with the side goal of good compression often outperforms one aimed solely at classification because overfitting to training data is suppressed by vector quantization. Image Segmentation and Compression Using Hidden Markov Models is an essential reference source for researchers and engineers working in statistical signal processing or image processing, especially those who are interested in hidden Markov models. It is also of value to those working on statistical modeling | ||
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Datensatz im Suchindex
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| author | Li, Jia Gray, Robert M. |
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| discipline | Informatik |
| doi_str_mv | 10.1007/978-1-4615-4497-5 |
| format | Electronic eBook |
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| id | DE-604.BV045148976 |
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| indexdate | 2024-07-10T08:10:02Z |
| institution | BVB |
| isbn | 9781461544975 |
| language | English |
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| spelling | Li, Jia Verfasser aut Image Segmentation and Compression Using Hidden Markov Models by Jia Li, Robert M. Gray Boston, MA Springer US 2000 1 Online-Ressource (XIII, 141 p) txt rdacontent c rdamedia cr rdacarrier The Springer International Series in Engineering and Computer Science 571 In the current age of information technology, the issues of distributing and utilizing images efficiently and effectively are of substantial concern. Solutions to many of the problems arising from these issues are provided by techniques of image processing, among which segmentation and compression are topics of this book. Image segmentation is a process for dividing an image into its constituent parts. For block-based segmentation using statistical classification, an image is divided into blocks and a feature vector is formed for each block by grouping statistics of its pixel intensities. Conventional block-based segmentation algorithms classify each block separately, assuming independence of feature vectors. Image Segmentation and Compression Using Hidden Markov Models presents a new algorithm that models the statistical dependence among image blocks by two dimensional hidden Markov models (HMMs). Formulas for estimating the model according to the maximum likelihood criterion are derived from the EM algorithm. To segment an image, optimal classes are searched jointly for all the blocks by the maximum a posteriori (MAP) rule. The 2-D HMM is extended to multiresolution so that more context information is exploited in classification and fast progressive segmentation schemes can be formed naturally. The second issue addressed in the book is the design of joint compression and classification systems using the 2-D HMM and vector quantization. A classifier designed with the side goal of good compression often outperforms one aimed solely at classification because overfitting to training data is suppressed by vector quantization. Image Segmentation and Compression Using Hidden Markov Models is an essential reference source for researchers and engineers working in statistical signal processing or image processing, especially those who are interested in hidden Markov models. It is also of value to those working on statistical modeling Computer Science Image Processing and Computer Vision Signal, Image and Speech Processing Electrical Engineering Computer Graphics Management of Computing and Information Systems Computer science Computer graphics Image processing Management information systems Electrical engineering Bildsegmentierung (DE-588)4145448-0 gnd rswk-swf Datenkompression (DE-588)4121121-2 gnd rswk-swf Bildverarbeitung (DE-588)4006684-8 gnd rswk-swf Hidden-Markov-Modell (DE-588)4352479-5 gnd rswk-swf Bildsignal (DE-588)4122924-1 gnd rswk-swf Markov-Prozess (DE-588)4134948-9 gnd rswk-swf Bildsignal (DE-588)4122924-1 s Datenkompression (DE-588)4121121-2 s Hidden-Markov-Modell (DE-588)4352479-5 s 1\p DE-604 Bildsegmentierung (DE-588)4145448-0 s 2\p DE-604 Markov-Prozess (DE-588)4134948-9 s 3\p DE-604 Bildverarbeitung (DE-588)4006684-8 s 4\p DE-604 Gray, Robert M. aut Erscheint auch als Druck-Ausgabe 9781461370277 https://doi.org/10.1007/978-1-4615-4497-5 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 3\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk 4\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Li, Jia Gray, Robert M. Image Segmentation and Compression Using Hidden Markov Models Computer Science Image Processing and Computer Vision Signal, Image and Speech Processing Electrical Engineering Computer Graphics Management of Computing and Information Systems Computer science Computer graphics Image processing Management information systems Electrical engineering Bildsegmentierung (DE-588)4145448-0 gnd Datenkompression (DE-588)4121121-2 gnd Bildverarbeitung (DE-588)4006684-8 gnd Hidden-Markov-Modell (DE-588)4352479-5 gnd Bildsignal (DE-588)4122924-1 gnd Markov-Prozess (DE-588)4134948-9 gnd |
| subject_GND | (DE-588)4145448-0 (DE-588)4121121-2 (DE-588)4006684-8 (DE-588)4352479-5 (DE-588)4122924-1 (DE-588)4134948-9 |
| title | Image Segmentation and Compression Using Hidden Markov Models |
| title_auth | Image Segmentation and Compression Using Hidden Markov Models |
| title_exact_search | Image Segmentation and Compression Using Hidden Markov Models |
| title_full | Image Segmentation and Compression Using Hidden Markov Models by Jia Li, Robert M. Gray |
| title_fullStr | Image Segmentation and Compression Using Hidden Markov Models by Jia Li, Robert M. Gray |
| title_full_unstemmed | Image Segmentation and Compression Using Hidden Markov Models by Jia Li, Robert M. Gray |
| title_short | Image Segmentation and Compression Using Hidden Markov Models |
| title_sort | image segmentation and compression using hidden markov models |
| topic | Computer Science Image Processing and Computer Vision Signal, Image and Speech Processing Electrical Engineering Computer Graphics Management of Computing and Information Systems Computer science Computer graphics Image processing Management information systems Electrical engineering Bildsegmentierung (DE-588)4145448-0 gnd Datenkompression (DE-588)4121121-2 gnd Bildverarbeitung (DE-588)4006684-8 gnd Hidden-Markov-Modell (DE-588)4352479-5 gnd Bildsignal (DE-588)4122924-1 gnd Markov-Prozess (DE-588)4134948-9 gnd |
| topic_facet | Computer Science Image Processing and Computer Vision Signal, Image and Speech Processing Electrical Engineering Computer Graphics Management of Computing and Information Systems Computer science Computer graphics Image processing Management information systems Electrical engineering Bildsegmentierung Datenkompression Bildverarbeitung Hidden-Markov-Modell Bildsignal Markov-Prozess |
| url | https://doi.org/10.1007/978-1-4615-4497-5 |
| work_keys_str_mv | AT lijia imagesegmentationandcompressionusinghiddenmarkovmodels AT grayrobertm imagesegmentationandcompressionusinghiddenmarkovmodels |