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Image Co-segmentation, Hati


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Автор: Hati   (Хати)
Название:  Image Co-segmentation
Перевод названия: Хати: Совместная сегментация изображений
ISBN: 9789811985690
Издательство: Springer
Классификация:


ISBN-10: 9811985693
Обложка/Формат: Hardback
Страницы: 221
Вес: 0.57 кг.
Дата издания: 17.02.2023
Серия: Studies in Computational Intelligence
Язык: English
Издание: 1st ed. 2023
Иллюстрации: 110 illustrations, color; 8 illustrations, black and white; xiv, 221 p. 118 illus., 110 illus. in color.
Размер: 235 x 155
Читательская аудитория: Professional & vocational
Основная тема: Engineering
Ссылка на Издательство: Link
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Поставляется из: Германии
Описание: This book presents and analyzes methods to perform image co-segmentation. In this book, the authors describe efficient solutions to this problem ensuring robustness and accuracy, and provide theoretical analysis for the same. Six different methods for image co-segmentation are presented. These methods use concepts from statistical mode detection, subgraph matching, latent class graph, region growing, graph CNN, conditional encoder–decoder network, meta-learning, conditional variational encoder–decoder, and attention mechanisms. The authors have included several block diagrams and illustrative examples for the ease of readers. This book is a highly useful resource to researchers and academicians not only in the specific area of image co-segmentation but also in related areas of image processing, graph neural networks, statistical learning, and few-shot learning.
Дополнительное описание: Introduction.- Survey of Image Co-segmentation.- Mathematical Background.- Co-segmentation using a Classification Framework.- Use of Maximum Common Subgraph Matching.- Maximally Occurring Common Subgraph Matching.- Co-segmentation using Graph Convolutiona



Hybrid Soft Computing for Image Segmentation

Автор: Siddhartha Bhattacharyya; Paramartha Dutta; Sourav
Название: Hybrid Soft Computing for Image Segmentation
ISBN: 3319472224 ISBN-13(EAN): 9783319472225
Издательство: Springer
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Цена: 15372.00 р.
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Описание: This book proposes soft computing techniques for segmenting real-life images in applications such as image processing, image mining, video surveillance, and intelligent transportation systems. The book suggests hybrids deriving from three main approaches: fuzzy systems, primarily used for handling real-life problems that involve uncertainty; artificial neural networks, usually applied for machine cognition, learning, and recognition; and evolutionary computation, mainly used for search, exploration, efficient exploitation of contextual information, and optimization.The contributed chapters discuss both the strengths and the weaknesses of the approaches, and the book will be valuable for researchers and graduate students in the domains of image processing and computational intelligence.

Marginal Space Learning for Medical Image Analysis

Автор: Yefeng Zheng; Dorin Comaniciu
Название: Marginal Space Learning for Medical Image Analysis
ISBN: 1493955756 ISBN-13(EAN): 9781493955756
Издательство: Springer
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Цена: 11179.00 р.
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Описание: Automatic detection and segmentation of anatomical structures in medical images are prerequisites to subsequent image measurements and disease quantification, and therefore have multiple clinical applications.

Multi Modality State-of-the-Art Medical Image Segmentation and Registration Methodologies

Автор: Ayman S. El-Baz; Rajendra Acharya U; Majid Mirmehd
Название: Multi Modality State-of-the-Art Medical Image Segmentation and Registration Methodologies
ISBN: 1489978135 ISBN-13(EAN): 9781489978134
Издательство: Springer
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Цена: 28732.00 р.
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Описание: Advances in image-guided cancer surgery have enlarged the role of image segmentation and registration. This book reviews current methodologies, to help physicians delineate anatomical structures, enhance the accuracy of diagnosis and improve treatment planning.

Hybrid Soft Computing for Multilevel Image and Data Segmentation

Автор: Sourav De; Siddhartha Bhattacharyya; Susanta Chakr
Название: Hybrid Soft Computing for Multilevel Image and Data Segmentation
ISBN: 3319475231 ISBN-13(EAN): 9783319475233
Издательство: Springer
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Цена: 13275.00 р.
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Описание: This book explains efficient solutions for segmenting the intensity levels of different types of multilevel images. The authors present hybrid soft computing techniques, which have advantages over conventional soft computing solutions as they incorporate data heterogeneity into the clustering/segmentation procedures.This is a useful introduction and reference for researchers and graduate students of computer science and electronics engineering, particularly in the domains of image processing and computational intelligence.

Clustering Techniques for Image Segmentation

Автор: Siddiqui
Название: Clustering Techniques for Image Segmentation
ISBN: 3030812324 ISBN-13(EAN): 9783030812324
Издательство: Springer
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Цена: 12157.00 р.
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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.

Statistical Atlases and Computational Models of the Heart. Atrial Segmentation and LV Quantification Challenges

Автор: Mihaela Pop; Maxime Sermesant; Jichao Zhao; Shuo L
Название: Statistical Atlases and Computational Models of the Heart. Atrial Segmentation and LV Quantification Challenges
ISBN: 3030120287 ISBN-13(EAN): 9783030120283
Издательство: Springer
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Цена: 6986.00 р.
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Описание: This book constitutes the thoroughly refereed post-workshop proceedings of the 9th International Workshop on Statistical Atlases and Computational Models of the Heart: Atrial Segmentation and LV Quantification Challenges, STACOM 2018, held in conjunction with MICCAI 2018, in Granada, Spain, in September 2018. The 52 revised full workshop papers were carefully reviewed and selected from 60 submissions. The topics of the workshop included: cardiac imaging and image processing, machine learning applied to cardiac imaging and image analysis, atlas construction, statistical modelling of cardiac function across different patient populations, cardiac computational physiology, model customization, atlas based functional analysis, ontological schemata for data and results, integrated functional and structural analyses, as well as the pre-clinical and clinical applicability of these methods.

Text Segmentation and Recognition for Enhanced Image Spam Detection: An Integrated Approach

Автор: Rajalingam Mallikka
Название: Text Segmentation and Recognition for Enhanced Image Spam Detection: An Integrated Approach
ISBN: 3030530469 ISBN-13(EAN): 9783030530464
Издательство: Springer
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Цена: 15372.00 р.
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Описание: This book discusses email spam detection and its challenges such as text classification and categorization.

Marginal Space Learning for Medical Image Analysis

Автор: Zheng
Название: Marginal Space Learning for Medical Image Analysis
ISBN: 1493905996 ISBN-13(EAN): 9781493905997
Издательство: Springer
Рейтинг:
Цена: 11179.00 р.
Наличие на складе: Есть у поставщика Поставка под заказ.

Описание: Automatic detection and segmentation of anatomical structures in medical images are prerequisites to subsequent image measurements and disease quantification, and therefore have multiple clinical applications.

Hybrid Soft Computing for Image Segmentation

Автор: Bhattacharyya Siddhartha, Dutta Paramartha, de Sourav
Название: Hybrid Soft Computing for Image Segmentation
ISBN: 3319836846 ISBN-13(EAN): 9783319836843
Издательство: Springer
Рейтинг:
Цена: 13974.00 р.
Наличие на складе: Есть у поставщика Поставка под заказ.

Описание: This book proposes soft computing techniques for segmenting real-life images in applications such as image processing, image mining, video surveillance, and intelligent transportation systems.

Hybrid Soft Computing for Multilevel Image and Data Segmentation

Автор: de Sourav, Bhattacharyya Siddhartha, Chakraborty Susanta
Название: Hybrid Soft Computing for Multilevel Image and Data Segmentation
ISBN: 3319837583 ISBN-13(EAN): 9783319837581
Издательство: Springer
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Цена: 13974.00 р.
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Metaheuristic Algorithms for Image Segmentation: Theory and Applications

Автор: Oliva Diego, Abd Elaziz Mohamed, Hinojosa Salvador
Название: Metaheuristic Algorithms for Image Segmentation: Theory and Applications
ISBN: 3030129330 ISBN-13(EAN): 9783030129330
Издательство: Springer
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Цена: 13974.00 р.
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Описание: The images have to be accurately analyzed, and crucial pre-processing steps, like image segmentation, and artificial intelligence, including metaheuristics, are applied in the automatic analysis of digital images.

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: 13974.00 р.
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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.


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