Cellular Image Classification, Xiang Xu; Xingkun Wu; Feng Lin
Автор: Xiang Xu; Xingkun Wu; Feng Lin Название: Cellular Image Classification ISBN: 3319476289 ISBN-13(EAN): 9783319476285 Издательство: Springer Рейтинг: Цена: 121110.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание:
This book introduces new techniques for cellular image feature extraction, pattern recognition and classification. The authors use the antinuclear antibodies (ANAs) in patient serum as the subjects and the Indirect Immunofluorescence (IIF) technique as the imaging protocol to illustrate the applications of the described methods. Throughout the book, the authors provide evaluations for the proposed methods on two publicly available human epithelial (HEp-2) cell datasets: ICPR2012 dataset from the ICPR'12 HEp-2 cell classification contest and ICIP2013 training dataset from the ICIP'13 Competition on cells classification by fluorescent image analysis.
First, the reading of imaging results is significantly influenced by one’s qualification and reading systems, causing high intra- and inter-laboratory variance. The authors present a low-order LP21 fiber mode for optical single cell manipulation and imaging staining patterns of HEp-2 cells. A focused four-lobed mode distribution is stable and effective in optical tweezer applications, including selective cell pick-up, pairing, grouping or separation, as well as rotation of cell dimers and clusters. Both translational dragging force and rotational torque in the experiments are in good accordance with the theoretical model. With a simple all-fiber configuration, and low peak irradiation to targeted cells, instrumentation of this optical chuck technology will provide a powerful tool in the ANA-IIF laboratories. Chapters focus on the optical, mechanical and computing systems for the clinical trials. Computer programs for GUI and control of the optical tweezers are also discussed.
to more discriminative local distance vector by searching for local neighbors of the local feature in the class-specific manifolds. Encoding and pooling the local distance vectors leads to salient image representation. Combined with the traditional coding methods, this method achieves higher classification accuracy.
Then, a rotation invariant textural feature of Pairwise Local Ternary Patterns with Spatial Rotation Invariant (PLTP-SRI) is examined. It is invariant to image rotations, meanwhile it is robust to noise and weak illumination. By adding spatial pyramid structure, this method captures spatial layout information. While the proposed PLTP-SRI feature extracts local feature, the BoW framework builds a global image representation. It is reasonable to combine them together to achieve impressive classification performance, as the combined feature takes the advantages of the two kinds of features in different aspects.
Finally, the authors design a Co-occurrence Differential Texton (CoDT) feature to represent the local image patches of HEp-2 cells. The CoDT feature reduces the information loss by ignoring the quantization while it utilizes the spatial relations among the differential micro-texton feature. Thus it can increase the discriminative power. A generative model adaptively characterizes the CoDT feature space of the training data. Furthermore, exploiting a discriminant representation allows for HEp-2 cell images based on the adaptive partitioned feature space. Therefore, the resulting representation is adapted to the classification task. By cooperating with linear Support Vector Machine (SVM) classifier, this framework can exploit the advantages of both generative and discriminative approaches for cellular image classification.
The book is written for those researchers who would like to develop their own programs, and the working MatLab codes are included for all the important algorithms presented. It can also be used as a reference book for graduate students and senior undergraduates in the area of biomedical imaging, image feature extraction, pattern recognition an
Автор: Duda, Richard O. Название: Pattern classification with computer manual, 2r.e. ISBN: 0471703508 ISBN-13(EAN): 9780471703501 Издательство: Wiley Рейтинг: Цена: 184750.00 T Наличие на складе: Поставка под заказ. Описание: The first edition, published in 1973, has become a classic reference in the field. Now with the second edition, readers will find information on key new topics such as neural networks and statistical pattern recognition, the theory of machine learning, and the theory of invariances. Also included are worked examples, comparisons between different methods, extensive graphics, expanded exercises and computer project topics.
An Instructor's Manual presenting detailed solutions to all the problems in the book is available from the Wiley editorial department.
Автор: Surekha Borra; Rohit Thanki; Nilanjan Dey Название: Satellite Image Analysis: Clustering and Classification ISBN: 9811364230 ISBN-13(EAN): 9789811364235 Издательство: Springer Рейтинг: Цена: 46570.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание:
Thanks to recent advances in sensors, communication and satellite technology, data storage, processing and networking capabilities, satellite image acquisition and mining are now on the rise. In turn, satellite images play a vital role in providing essential geographical information. Highly accurate automatic classification and decision support systems can facilitate the efforts of data analysts, reduce human error, and allow the rapid and rigorous analysis of land use and land cover information. Integrating Machine Learning (ML) technology with the human visual psychometric can help meet geologists’ demands for more efficient and higher-quality classification in real time.
This book introduces readers to key concepts, methods and models for satellite image analysis; highlights state-of-the-art classification and clustering techniques; discusses recent developments and remaining challenges; and addresses various applications, making it a valuable asset for engineers, data analysts and researchers in the fields of geographic information systems and remote sensing engineering.
Автор: J?n Atli Benediktsson and Pedram Ghamisi Название: Spectral-Spatial Classification of Hyperspectral Remote Sensing Images ISBN: 1608078124 ISBN-13(EAN): 9781608078127 Издательство: Artech House Рейтинг: Цена: 94870.00 T Наличие на складе: Нет в наличии. Описание: This comprehensive new resource brings you up to date on recent developments in the classification of hyperspectral images using both spectral and spatial information, including advanced statistical approaches and methods. The inclusion of spatial information to traditional approaches for hyperspectral classification has been one of the most active and relevant innovative lines of research in remote sensing during recent years. This book gives you insight into several important challenges when performing hyperspectral image classification related to the imbalance between high dimensionality and limited availability of training samples, or the presence of mixed pixels in the data. This book also shows you how to integrate spatial and spectral information in order to take advantage of the benefits that both sources of information provide
Автор: Leszek Rutkowski Название: New Soft Computing Techniques for System Modeling, Pattern Classification and Image Processing ISBN: 3642058205 ISBN-13(EAN): 9783642058202 Издательство: Springer Рейтинг: Цена: 181670.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: The present vol- ume, based mostly on his own work, is a milestone in the devel- opment of soft computing, integrating various disciplines from the fields of information science and engineering.
Автор: Paula Brito; Patrice Bertrand; Guy Cucumel; Franci Название: Selected Contributions in Data Analysis and Classification ISBN: 3540735585 ISBN-13(EAN): 9783540735588 Издательство: Springer Рейтинг: Цена: 130430.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Presents methodological developments in data analysis and classification. This work covers topics including, methods for classification and clustering, dissimilarity analysis, graph analysis, consensus methods, conceptual analysis of data, analysis of symbolic data, data mining, and knowledge discovery in databases.
Автор: Ingo Balderjahn; Rudolf Mathar; Martin Schader Название: Classification, Data Analysis, and Data Highways ISBN: 3540639098 ISBN-13(EAN): 9783540639091 Издательство: Springer Рейтинг: Цена: 121110.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This volume presents 43 articles dealing with models and methods of data analysis and classification, statistics and stochastics, information systems and WWW- and Internet-related topics as well as many applications.
Автор: R?diger Klar; Otto Opitz Название: Classification and Knowledge Organization ISBN: 3540629815 ISBN-13(EAN): 9783540629818 Издательство: Springer Рейтинг: Цена: 81050.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Large collections of data and information necessitate adequate methods for their analysis. The book presents such methods, proposes and discusses recent approaches and implementations and describes a series of practical applications.
Автор: Otto Opitz; Berthold Lausen; R?diger Klar Название: Information and Classification ISBN: 3540567364 ISBN-13(EAN): 9783540567363 Издательство: Springer Рейтинг: Цена: 81050.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: In many fields of science and practice large amounts of dataand informationare collected for analyzing and visualizinglatent structures as orderings or classifications forexample. So, in the first sectionwe find papers on Classification Methods, FuzzyClassification, Multidimensional Scaling, DiscriminantAnalysis and Conceptual Analysis.
Автор: Rokach Lior Название: Pattern Classification Using Ensemble Methods ISBN: 9814271063 ISBN-13(EAN): 9789814271066 Издательство: World Scientific Publishing Рейтинг: Цена: 89760.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Researchers from various disciplines such as pattern recognition, statistics, and machine learning have explored the use of ensemble methodology since the late seventies. This book aims to impose a degree of order upon this diversity by presenting a coherent and unified repository of ensemble methods, theories, trends, challenges and applications.
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