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Clustering Techniques for Image Segmentation, Siddiqui Fasahat Ullah, Yahya Abid


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Автор: Siddiqui Fasahat Ullah, Yahya Abid
Название:  Clustering Techniques for Image Segmentation
ISBN: 9783030812294
Издательство: Springer
Классификация:



ISBN-10: 3030812294
Обложка/Формат: Hardcover
Вес: 0.36 кг.
Дата издания: 20.09.2021
Серия: Springer series in synergetics
Язык: English
Издание: 1st ed. 2022
Иллюстрации: 50 tables, color; 16 illustrations, color; 39 illustrations, black and white; xx, 108 p. 55 illus., 16 illus. in color.
Размер: 24.13 x 16.51 x 1.02 cm
Читательская аудитория: Professional & vocational
Подзаголовок: Third international conference on lean and agile software development, lasd 2019, and 7th conference on multimedia, interaction, design and innovation, midi 2019, leipzig, germany, september 1-4, 2019, revised selected papers
Ссылка на Издательство: Link
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Поставляется из: Германии
Описание: This book presents the workings of major clustering techniques along with their advantages and shortcomings. After introducing the topic, the authors illustrate their modified version that avoids those shortcomings. The book then introduces four modified clustering techniques, namely the Optimized K-Means (OKM), Enhanced Moving K-Means-1(EMKM-1), Enhanced Moving K-Means-2(EMKM-2), and Outlier Rejection Fuzzy C-Means (ORFCM). The authors show how the OKM technique can differentiate the empty and zero variance cluster, and the data assignment procedure of the K-mean clustering technique is redesigned. They then show how the EMKM-1 and EMKM-2 techniques reform the data-transferring concept of the Adaptive Moving K-Means (AMKM) to avoid the centroid trapping problem. And that the ORFCM technique uses the adaptable membership function to moderate the outlier effects on the Fuzzy C-meaning clustering technique. This book also covers the working steps and codings of quantitative analysis methods. The results highlight that the modified clustering techniques generate more homogenous regions in an image with better shape and sharp edge preservation. * Showcases major clustering techniques, detailing their advantages and shortcomings; * Includes several methods for evaluating the performance of segmentation techniques; * Presents several applications including medical diagnosis systems, satellite imaging systems, and biometric systems.
Дополнительное описание: Introduction.- Introduction to Image Segmentation and Clustering.- Hard and Soft Clustering Techniques.- New Enhanced Clustering Techniques.- Mathematical Model of clustering techniques and evaluation methods.- Conclusion.


Metaheuristics for Data Clustering and Image Segmentation

Автор: Meera Ramadas; Ajith Abraham
Название: Metaheuristics for Data Clustering and Image Segmentation
ISBN: 3030040968 ISBN-13(EAN): 9783030040963
Издательство: Springer
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Цена: 93160.00 T
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Описание: In this book, differential evolution and its modified variants are applied to the clustering of data and images. Metaheuristics have emerged as potential algorithms for dealing with complex optimization problems, which are otherwise difficult to solve using traditional methods. In this regard, differential evolution is considered to be a highly promising technique for optimization and is being used to solve various real-time problems. The book studies the algorithms in detail, tests them on a range of test images, and carefully analyzes their performance. Accordingly, it offers a valuable reference guide for all researchers, students and practitioners working in the fields of artificial intelligence, optimization and data analytics.

Data Clustering and Image Segmentation Through Genetic Algorithms: Emerging Research and Opportunities

Автор: S. Dash, B.K. Tripathy
Название: Data Clustering and Image Segmentation Through Genetic Algorithms: Emerging Research and Opportunities
ISBN: 1522563199 ISBN-13(EAN): 9781522563198
Издательство: Mare Nostrum (Eurospan)
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Цена: 189420.00 T
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Описание: As computers are being used more and more to solve complex problems, the application of biology or natural evolution principles to the study and design of human systems helps provide efficient optimization algorithms.

Data Clustering and Image Segmentation Through Genetic Algorithms: Emerging Research and Opportunities is an essential reference source that discusses applications of bio-inspired algorithms in data mining, computer vision, image processing, and pattern recognition, as well as methods of designing competent algorithms based on decomposition principles. Featuring research on topics such as cluster analysis, metaheuristic optimization, and image processing, this book is ideally designed for IT professionals, computer engineers, researchers, academicians, and upper-level students seeking coverage on how to develop efficient clustering algorithms.

Customer Segmentation and Clustering Using SAS Enterprise Miner, Third Edition

Автор: Collica Randall S.
Название: Customer Segmentation and Clustering Using SAS Enterprise Miner, Third Edition
ISBN: 1629601063 ISBN-13(EAN): 9781629601069
Издательство: Неизвестно
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Цена: 80870.00 T
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Segmentation Analytics with SAS Viya: An Approach to Clustering and Visualization (Hardcover edition)

Автор: Collica Randall S.
Название: Segmentation Analytics with SAS Viya: An Approach to Clustering and Visualization (Hardcover edition)
ISBN: 1952363063 ISBN-13(EAN): 9781952363061
Издательство: Неизвестно
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Цена: 73510.00 T
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Описание:

Better understand your customers using segmentation analytics in SAS Viya!

Segmentation Analytics with SAS Viya: An Approach to Clustering and Visualization demonstrates the use of clustering and machine learning methods for the purpose of segmenting customer or client data into useful categories for marketing, market research, next best offers by segment, and more. This book highlights the latest and greatest methods available that show the power of SAS Viya while solving typical industry issues. Packed with real-world examples, this book provides readers with practical methods of using SAS Visual Data Mining and Machine Learning (VDMML), SAS Model Studio, SAS Visual Statistics, SAS Visual Analytics, and coding in SAS Studio for segmentation model development and analysis.

This book is designed for analysts, data miners, and data scientists who need to use the all in-memory platform of SAS Viya for the purposes of clustering and segmentation. Understanding how customers behave is a primary objective of most organizations, and segmentation is a key analytic method for achieving that objective.


Customer Segmentation and Clustering Using SAS Enterprise Miner, Third Edition

Автор: Collica Randall S.
Название: Customer Segmentation and Clustering Using SAS Enterprise Miner, Third Edition
ISBN: 1642953091 ISBN-13(EAN): 9781642953091
Издательство: Неизвестно
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Цена: 105390.00 T
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Описание: Understanding your customers is the key to your company's success

Segmentation is one of the first and most basic machine learning methods. It can be used by companies to understand their customers better, boost relevance of marketing messaging, and increase efficacy of predictive models. In Customer Segmentation and Clustering Using SAS Enterprise Miner, Third Edition, Randy Collica explains, in step-by-step fashion, the most commonly available techniques for segmentation using the powerful data mining software SAS Enterprise Miner.

A working guide that uses real-world data, this new edition will show you how to segment customers more intelligently and achieve the one-to-one customer relationship that your business needs. Step-by-step examples and exercises, using a number of machine learning and data mining techniques, clearly illustrate the concepts of segmentation and clustering in the context of customer relationship management.

The book includes four parts, each of which increases in complexity. Part 1 reviews the basics of segmentation and clustering at an introductory level, providing examples from a variety of industries. Part 2 offers an in-depth treatment of segmentation with practical topics, such as when and how to update your models. Part 3 goes beyond traditional segmentation practices to introduce recommended strategies for clustering product affinities, handling missing data, and incorporating textual records into your predictive model with SAS Text Miner. Finally, part 4 takes segmentation to a new level with advanced techniques, such as clustering of product associations, developing segmentation-scoring models from customer survey data, combining segmentations using ensemble segmentation, and segmentation of customer transactions.

New to the third edition is a chapter that focuses on predictive models within microsegments and combined segments, and a new parallel process technique is introduced using SAS Factory Miner. In addition, all examples have been updated to the latest version of SAS Enterprise Miner.

Brain tumor mri image segmentation using deep learning techniques

Название: Brain tumor mri image segmentation using deep learning techniques
ISBN: 0323911714 ISBN-13(EAN): 9780323911719
Издательство: Elsevier Science
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Цена: 154960.00 T
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Brain Tumor MRI Image Segmentation Using Deep Learning Techniques offers a description of deep learning approaches used for the segmentation of brain tumors. The book demonstrates core concepts of deep learning algorithms by using diagrams, data tables and examples to illustrate brain tumor segmentation. After introducing basic concepts of deep learning-based brain tumor segmentation, sections cover techniques for modeling, segmentation and properties. A focus is placed on the application of different types of convolutional neural networks, like single path, multi path, fully convolutional network, cascade convolutional neural networks, Long Short-Term Memory - Recurrent Neural Network and Gated Recurrent Units, and more.

The book also highlights how the use of deep neural networks can address new questions and protocols, as well as improve upon existing challenges in brain tumor segmentation.


Topographical tools for filtering and segmentation 1

Автор: Meyer, Fernand
Название: Topographical tools for filtering and segmentation 1
ISBN: 1786301571 ISBN-13(EAN): 9781786301574
Издательство: Wiley
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Цена: 146730.00 T
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Описание:

Mathematical morphology has developed a powerful methodology for segmenting images, based on connected filters and watersheds. We have chosen the abstract framework of node- or edge-weighted graphs for an extensive mathematical and algorithmic description of these tools.

Volume 1 is devoted to watersheds. The topography of a graph appears by observing the evolution of a drop of water moving from node to node on a weighted graph, along flowing paths, until it reaches regional minima. The upstream nodes of a regional minimum constitute its catchment zone.

The catchment zones may be constructed independently of each other and locally, in contrast with the traditional approach where the catchment basins have to be constructed all at the same time. Catchment zones may overlap, and thus, a new segmentation paradigm is proposed in which catchment zones cover each other according to a priority order. The resulting partition may then be corrected, by local and parallel treatments, in order to achieve the desired precision.


Level Set Method in Medical Imaging Segmentation

Автор: Ayman El-Baz, Jasjit S. Suri
Название: Level Set Method in Medical Imaging Segmentation
ISBN: 113855345X ISBN-13(EAN): 9781138553453
Издательство: Taylor&Francis
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Цена: 224570.00 T
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Описание: Level set methods are numerical techniques which offer remarkably powerful tools for understanding, analyzing, and computing interface motion in a host of settings. When used for medical imaging analysis and segmentation, the function assigns a label to each pixel or voxel and optimality is defined based on desired imaging properties. This often includes a detection step to extract specific objects via segmentation. This allows for the segmentation and analysis problem to be formulated and solved in a principled way based on well-established mathematical theories. Level set method is a great tool for modeling time varying medical images and enhancement of numerical computations.

Segmentation, Revenue Management and Pricing Analytics

Автор: Bodea
Название: Segmentation, Revenue Management and Pricing Analytics
ISBN: 0415898331 ISBN-13(EAN): 9780415898331
Издательство: Taylor&Francis
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Цена: 69410.00 T
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Описание: This book guides students and professionals in identifying and exploiting revenue management and pricing opportunities to improve profit in different business contexts, using relevant concepts and quantitative methods.

Topographical Tools for Filtering and Segmentation 2

Автор: Meyer, Fernand
Название: Topographical Tools for Filtering and Segmentation 2
ISBN: 1786304074 ISBN-13(EAN): 9781786304070
Издательство: Wiley
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Цена: 146730.00 T
Наличие на складе: Поставка под заказ.
Описание:

Mathematical morphology has developed a powerful methodology for segmenting images, based on connected filters and watersheds. We have chosen the abstract framework of node- or edge-weighted graphs for an extensive mathematical and algorithmic description of these tools.

Volume 2 proposes two physical models for describing valid flooding on a node- or edge-weighted graph, and establishes how to pass from one to another. Many new flooding algorithms are derived, allowing parallel and local flooding of graphs.

Watersheds and flooding are then combined for solving real problems. Their ability to model a real hydrographic basin represented by its digital elevation model constitutes a good validity check of the underlying physical models.

The last part of Volume 2 explains why so many different watershed partitions exist for the same graph. Marker-based segmentation is the method of choice for curbing this proliferation. This book proposes new algorithms combining the advantages of the previous methods which treated node- and edge-weighted graphs differently.


Interactive Segmentation Techniques

Автор: Jia He; Chang-Su Kim; C.-C. Jay Kuo
Название: Interactive Segmentation Techniques
ISBN: 9814451592 ISBN-13(EAN): 9789814451598
Издательство: Springer
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Цена: 60940.00 T
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Описание: This book will first introduce classic graph-cut segmentation algorithms and then discuss state-of-the-art techniques, including graph matching methods, region merging and label propagation, clustering methods, and segmentation methods based on edge detection.


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