4 edition of **Rough fuzzy image analysis** found in the catalog.

- 141 Want to read
- 7 Currently reading

Published
**2010**
by Taylor & Francis in Boca Raton
.

Written in English

- Image analysis,
- Fuzzy sets

**Edition Notes**

Statement | editors, Sankar K. Pal, James F. Peters. |

Contributions | Pal, Sankar K., Peters, James F. |

Classifications | |
---|---|

LC Classifications | TA1637 .R68 2010 |

The Physical Object | |

Pagination | p. cm. |

ID Numbers | |

Open Library | OL24022983M |

ISBN 10 | 9781439803295 |

LC Control Number | 2009053741 |

OCLC/WorldCa | 262430664 |

We adopt a rough-fuzzy set approach, which can properly process high dimensionality, for image segmentation considering both color and texture features. This approach firstly constructs a structure named fuzzy data cube, whose attributes are composed of the fuzzy sets associated with image by: 1. Learn how to apply rough-fuzzy computing techniques to solve problems in bioinformatics and medical image processing. Emphasizing applications in bioinformatics and medical image processing, this text offers a clear framework that enables readers to take advantage of the latest rough-fuzzy computing techniques to build working pattern recognition : Wiley.

Rough set-based data analysis methods have been successfully applied in bioinformatics, economics and finance, medicine, multimedia, web and text mining, signal and image processing, software engineering, robotics, and engineering (e.g. power systems and control engineering). Recently the three regions of rough sets are interpreted as regions. This book constitutes the refereed proceedings of the 11th International Conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing, RSFDGrC , held in Toronto, Canada in May in conjunction with the Second International Conference on Rough Sets and Knowledge Technology, RSKT , both as part of the Joint Rough Set Author: Aijun An.

Rough fuzzy set based scale space transforms and their use in image analysis. Fuzzy logic is a form of many-valued logic in which the truth values of variables may be any real number between 0 and 1 both inclusive. It is employed to handle the concept of partial truth, where the truth value may range between completely true and completely false. By contrast, in Boolean logic, the truth values of variables may only be the integer values 0 or 1.

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Emphasizing the utility of fuzzy, near, and rough sets in image analysis, Rough Fuzzy Image Analysis: Foundations and Methodologies introduces the fundamentals and applications in the state of the art of rough fuzzy image analysis.

In the first chapter, the distinguished editors explain how fuzzy, near, and rough sets provide the basis for the stages of pictorial pattern recognition: image Format: Paperback.

Emphasizing the utility of fuzzy, near, and rough sets in image analysis, Rough Fuzzy Image Analysis: Foundations and Methodologies introduces the fundamentals and applications in the state of the art of rough fuzzy image analysis. In the first chapter, the distinguished editors explain how fuzzy, near, and rough sets provide the basis for the stages of pictorial pattern recognition: image.

Fuzzy sets, near sets, and rough sets are useful and important stepping stones in a variety of approaches to image analysis. These three types of sets and their various hybridizations provide powerful frameworks for image analysis. Emphasizing the utility of fuzzy, near, and rough sets in image analysis, Rough Fuzzy Image Analysis: Foundations andCited by: Summary: Fuzzy sets, near sets, and rough sets are useful and important stepping stones in a variety of approaches to image analysis.

Emphasizing the utility of fuzzy, near, and rough sets in image analysis, this title introduces the fundamentals and applications in rough fuzzy image analysis. Emphasizing the utility of fuzzy, near, and rough sets in image analysis, ""Rough Fuzzy Image Analysis: Foundations and Methodologies"" introduces the fundamentals and Rough fuzzy image analysis book in the In the first chapter, the distinguished editors explain how fuzzy, near, and rough sets provide the basis for the stages of pictoria.

Emphasizing the utility of fuzzy, near, and rough sets in image analysis, Rough Fuzzy Image Analysis: Foundations and Methodologies introduces the fundamentals and applications in.

Papers related to the foundations and applications of fuzzy sets, rough sets and near sets approaches in image analysis can be found in S.K. Pal and J.F.

Pe- ters. In solving the image. The M-band wavelet packet analysis and rough-fuzzy-possibilistic c -means are used for text-graphics segmentation problem.

The M-band wavelet packet is used to extract the scale-space features, which offers a huge range of possibilities of scale-space features for document image and is able to zoom it onto narrow band high frequency by: with paper, explores Rough-Fuzzy K-means (RFKM) algorithm, a new intelligent technique used to discover data dependencies, data reduction, approximate set classification, and rule induction from image databases.

Rough sets offer an effective approach of managing uncertainties and also used for image segmentation, featureFile Size: 1MB.

Learn how to apply rough-fuzzy computing techniques to solve problems in bioinformatics and medical image processing. Emphasizing applications in bioinformatics and medical image processing, this text offers a clear framework that enables readers to take advantage of the latest rough-fuzzy computing techniques to build working pattern recognition models.

Fuzzy sets, near sets, and Rough sets are useful and important stepping stones in a variety of approaches to image analysis. These three types of sets and their various hybridizations provide powerful frameworks for image analysis. Emphasizing the utility of fuzzy, near, and Rough sets in image analysis, Rough Fuzzy Image Analysis: Foundations and Methodologies.

Rough-Fuzzy Pattern Recognition examines the important underlying theory as well as algorithms and applications, helping readers see the connections between theory and practice. The first chapter. Rough-Fuzzy Pattern Recognitionexamines the important underlying theory as well as algorithms and applications, helping readers see the connections between theory and practice.

The first chapter provides an introduction to pattern recognition and data mining, including the key challenges of working with high-dimensional, real-life data sets. Table of Contents 1 Cantor, Fuzzy, Near, and RoughSets in ImageAnalysis James F.

Peters and SankarK. Pal 2 Rough-Fuzzy ClusteringAlgorithm for Segmentation of Brain MRImages Pradipta Maji and SankarK. Pal 3 Image Thresholding using Generalized RoughSets Debashis SenandSankarK. Pal 4 Mathematical Morphologyand RoughSets. Rough-Fuzzy Pattern Recognition examines the important underlying theory as well as algorithms and applications, helping readers see the connections between theory and practice.

The first chapter provides an introduction to pattern recognition and data mining, including the key challenges of working with high-dimensional, real-life data cturer: Wiley-IEEE Computer Society Pr. This paper presents a segmentation method, integrating judiciously the merits of rough-fuzzy computing and multiresolution image analysis technique, for documents having both text and graphics regions.

It assumes that the text and non-text or graphics regions of a given document are considered to have different textural by: Rough-Fuzzy Pattern Recognition examines the important underlying theory as well as algorithms and applications, helping readers see the connections between theory and practice.

The first chapter provides an introduction to pattern recognition and data mining, including the key challenges of working with high-dimensional, real-life data sets.

Learn how to apply rough-fuzzy computing techniques to solve problems in bioinformatics and medical image processing Emphasizing applications in bioinformatics and medical image processing, this text offers a clear framework that enables readers to take advantage of the latest rough-fuzzy computing techniques to build working pattern recognition models.

About this book. Emphasizing applications in bioinformatics and medical image processing, this text offers a clear framework that enables readers to take advantage of the latest rough-fuzzy computing techniques to build working pattern recognition models. Abstract.

This paper presents a segmentation method, integrating judiciously the merits of rough-fuzzy computing and multiresolution image analysis technique, for documents having both text and graphics : Pradipta Maji, Shaswati Roy, Malay K. Kundu. Fuzzy sets, near sets, and rough sets are useful and important stepping stones in a variety of approaches to image analysis.

These three types of sets and their various hybridizations provide powerful frameworks for image analysis.

Emphasizing the utility of fuzzy, near, and rough sets in image analysis, Rough Fuzzy Image Analysis: Foundations and.A color image segmentation technique which exploits a novel definition of rough fuzzy sets and the rough–fuzzy product operation is presented.

The segmentation is performed by partitioning each block in multiple rough fuzzy sets that are used to build a lower and a upper histogram in the HSV color by: 2.-books-nosleep-science-DIY-food-UpliftingNews-space-Documentaries-photoshopbattles-sports-history-Futurology-EarthPorn-GetMotivated-WritingPrompts-creepy Rough Fuzzy Image Analysis Foundations and Methodologies (at4) submitted 1 minute ago by PureSeat4.