6. Rough set and soft set models in image processing
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Abstract
Image processing is used to extract useful information from images. It is among the rapidly growing technologies today and forms a core research area within engineering and computer science disciplines. Uncertainty based models play major roles in image processing in general and image segmentation in particular, leading to their applications in medical image processing and satellite image processing. From among the uncertainty models; namely fuzzy set, rough set, intuitionistic fuzzy set, soft set and their hybridmodels,we shall deal with only two as far as their role in image processing is concerned. These are rough set and soft set introduced by Pawlak in 1982 and Molodtsov in 1999 respectively. We shall also deal with some hybrid models of these two models and the models mentioned above. Our special attention will be on the application of these models in image segmentation.
Abstract
Image processing is used to extract useful information from images. It is among the rapidly growing technologies today and forms a core research area within engineering and computer science disciplines. Uncertainty based models play major roles in image processing in general and image segmentation in particular, leading to their applications in medical image processing and satellite image processing. From among the uncertainty models; namely fuzzy set, rough set, intuitionistic fuzzy set, soft set and their hybridmodels,we shall deal with only two as far as their role in image processing is concerned. These are rough set and soft set introduced by Pawlak in 1982 and Molodtsov in 1999 respectively. We shall also deal with some hybrid models of these two models and the models mentioned above. Our special attention will be on the application of these models in image segmentation.
Kapitel in diesem Buch
- Frontmatter I
- Preface V
- Contents XI
- 1. Medical color image enhancement: Problems, challenges & recent techniques 1
- 2. Exploring the scope of intelligent algorithms for various community detection techniques 19
- 3. An unsupervised graph-based approach for the representation of coronary arteries in X-ray angiograms 43
- 4. A study of recent trends in content based image classification 65
- 5. Intelligent monitoring and evaluation of digital geometry figures drawn by students 95
- 6. Rough set and soft set models in image processing 123
- 7. Quantum inspired simulated annealing technique for automatic clustering 145
- 8. Intelligent greedy model for influence maximization in multimedia data networks 167
- Index 183
Kapitel in diesem Buch
- Frontmatter I
- Preface V
- Contents XI
- 1. Medical color image enhancement: Problems, challenges & recent techniques 1
- 2. Exploring the scope of intelligent algorithms for various community detection techniques 19
- 3. An unsupervised graph-based approach for the representation of coronary arteries in X-ray angiograms 43
- 4. A study of recent trends in content based image classification 65
- 5. Intelligent monitoring and evaluation of digital geometry figures drawn by students 95
- 6. Rough set and soft set models in image processing 123
- 7. Quantum inspired simulated annealing technique for automatic clustering 145
- 8. Intelligent greedy model for influence maximization in multimedia data networks 167
- Index 183
