Phishing detection using content based image classification, Khandelwal, Shekhar (ibm Software Labs) Das, Rik (xavier Institute Of Social Service, Ranchi)
Автор: Kumar, Anil , Kumar, A. Senthil , Upadhyay, Priy Название: Fuzzy Machine Learning Algorithms for Remote Sensing Image Classification ISBN: 036735571X ISBN-13(EAN): 9780367355715 Издательство: Taylor&Francis Рейтинг: Цена: 15004.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book covers the state of art image classification methods for discrimination of earth objects from remote sensing satellite data with an emphasis on fuzzy based learning methods including applications for preparing land cover classification outputs from actual satellite data. All algorithms are supported by in-house developed tool as SMIC.
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.
Описание: Introduction.- Hyperspectral Imaging System.- Classification Techniques for HSI.- Preprocessing: Noise Reduction/ Band Categorization for HSI.- Spatial Feature Extraction Using Segmentation.- Multiple Deep learning models for feature extraction in classification.- Deep learning for merging spatial and spectral information in classification.- Sparse cording for Hyperspectral Data.- Classification Applications of HSI classification.- Conclusion.
Автор: Xiang Xu; Xingkun Wu; Feng Lin Название: Cellular Image Classification ISBN: 3319476289 ISBN-13(EAN): 9783319476285 Издательство: Springer Рейтинг: Цена: 18167.00 р. 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: Surekha Borra; Rohit Thanki; Nilanjan Dey Название: Satellite Image Analysis: Clustering and Classification ISBN: 9811364230 ISBN-13(EAN): 9789811364235 Издательство: Springer Рейтинг: Цена: 6986.00 р. 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: Vipin Tyagi Название: Content-Based Image Retrieval ISBN: 9811349444 ISBN-13(EAN): 9789811349447 Издательство: Springer Рейтинг: Цена: 13974.00 р. Наличие на складе: Поставка под заказ.
Описание: The book describes several techniques used to bridge the semantic gap and reflects on recent advancements in content-based image retrieval (CBIR).
The book presents selected methods for accelerating image retrieval and classification in large collections of images using what are referred to as ‘hand-crafted features.’ It introduces readers to novel rapid image description methods based on local and global features, as well as several techniques for comparing images.
Developing content-based image comparison, retrieval and classification methods that simulate human visual perception is an arduous and complex process. The book’s main focus is on the application of these methods in a relational database context. The methods presented are suitable for both general-type and medical images. Offering a valuable textbook for upper-level undergraduate or graduate-level courses on computer science or engineering, as well as a guide for computer vision researchers, the book focuses on techniques that work under real-world large-dataset conditions.
Описание: This reader-friendly textbook presents a comprehensive review of the essentials of image data mining, and the latest cutting-edge techniques used in the field. discusses techniques for indexing, image ranking, and image presentation, along with image database visualization methods;
Описание: This book focuses on deep learning-based methods for hyperspectral image (HSI) analysis. Unsupervised spectral-spatial adaptive band-noise factor-based formulation is devised for HSI noise detection and band categorization. The method to characterize the bands along with the noise estimation of HSIs will benefit subsequent remote sensing techniques significantly. This book develops on two fronts: On the one hand, it is aimed at domain professionals who want to have an updated overview of how hyperspectral acquisition techniques can combine with deep learning architectures to solve specific tasks in different application fields. On the other hand, the authors want to target the machine learning and computer vision experts by giving them a picture of how deep learning technologies are applied to hyperspectral data from a multidisciplinary perspective. The presence of these two viewpoints and the inclusion of application fields of remote sensing by deep learning are the original contributions of this review, which also highlights some potentialities and critical issues related to the observed development trends.
Автор: Rik Das, Sourav De, Siddhartha Bhattacharyya Название: Feature Dimension Reduction for Content-Based Image Identification ISBN: 152255775X ISBN-13(EAN): 9781522557753 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 31324.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Image data has portrayed immense potential as a foundation of information for numerous applications. Recent trends in multimedia computing have witnessed a rapid growth in digital image collections, resulting in a need for increased image data management.Feature Dimension Reduction for Content-Based Image Identification is a pivotal reference source that explores the contemporary trends and techniques of content-based image recognition. Including research covering topics such as feature extraction, fusion techniques, and image segmentation, this book explores different theories to facilitate timely identification of image data and managing, archiving, maintaining, and extracting information. This book is ideally designed for engineers, IT specialists, researchers, academicians, and graduate-level students seeking interdisciplinary research on image processing and analysis.
ООО "Логосфера " Тел:+7(495) 980-12-10 www.logobook.ru
