Genetic Programming for Image Classification: An Automated Approach to Feature Learning, Bi Ying, Xue Bing, Zhang Mengjie
Автор: Bi Ying, Xue Bing, Zhang Mengjie Название: Genetic Programming for Image Classification: An Automated Approach to Feature Learning ISBN: 3030659267 ISBN-13(EAN): 9783030659264 Издательство: Springer Цена: 139750.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book offers several new GP approaches to feature learning for image classification. This book shows how different techniques, e.g., image operators, ensembles, and surrogate, are proposed and employed to improve the accuracy and/or computational efficiency of GP for image classification.
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: 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: Xiang Xu; Xingkun Wu; Feng Lin Название: Cellular Image Classification ISBN: 3319837869 ISBN-13(EAN): 9783319837864 Издательство: Springer Рейтинг: Цена: 93160.00 T Наличие на складе: Нет в наличии. Описание: 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.
Автор: Leonardo Vanneschi; Steven Gustafson; Alberto Mora Название: Genetic Programming ISBN: 3642011802 ISBN-13(EAN): 9783642011801 Издательство: Springer Рейтинг: Цена: 69870.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Constitutes the refereed proceedings of the 11th European Conference on Genetic Programming, EuroGP 2009, held in Tubingen, Germany, in April 2009 co located with the Evo 2009 events. This book reflects research in the field of genetic programming, including work on representations, theory, operators and analysis, and feature selection.
Автор: Taguchi Y-H Название: Unsupervised Feature Extraction Applied to Bioinformatics: A Pca Based and TD Based Approach ISBN: 3030224589 ISBN-13(EAN): 9783030224585 Издательство: Springer Рейтинг: Цена: 149060.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book proposes applications of tensor decomposition to unsupervised feature extraction and feature selection. The author includes advanced descriptions about tensor decomposition including Tucker decomposition using high order singular value decomposition as well as higher order orthogonal iteration, and train tenor decomposition.
Автор: Murty M. Narasimha, Devi Der V. Susheela Название: Pattern Recognition: An Algorithmic Approach ISBN: 9814335452 ISBN-13(EAN): 9789814335454 Издательство: World Scientific Publishing Рейтинг: Цена: 132000.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Adopts a detailed and methodological algorithmic approach to explain the concepts of pattern recognition. This book deals with topics such as: pattern representation, nearest neighour based classifiers, neural networks, support vector machines, and decision trees.
Автор: Lukas Sekanina; Ting Hu; Nuno Louren?o; Hendrik Ri Название: Genetic Programming ISBN: 3030166694 ISBN-13(EAN): 9783030166694 Издательство: Springer Рейтинг: Цена: 46570.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book constitutes the refereed proceedings of the 22nd European Conference on Genetic Programming, EuroGP 2019, held as part of Evo* 2019, in Leipzig, Germany, in April 2019, co-located with the Evo* events EvoCOP, EvoMUSART, and EvoApplications.The 12 revised full papers and 6 short papers presented in this volume were carefully reviewed and selected from 36 submissions. They cover a wide range of topics and reflect the current state of research in the field. With a special focus on real-world applications in 2019, the papers are devoted to topics such as the test data design in software engineering, fault detection and classification of induction motors, digital circuit design, mosquito abundance prediction, machine learning and cryptographic function design.
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