Image Classification

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Remote Sensing Image Classification In R

Author : Courage Kamusoko
ISBN : 9789811380129
Genre : Technology & Engineering
File Size : 86.86 MB
Format : PDF
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This book offers an introduction to remotely sensed image processing and classification in R using machine learning algorithms. It also provides a concise and practical reference tutorial, which equips readers to immediately start using the software platform and R packages for image processing and classification. This book is divided into five chapters. Chapter 1 introduces remote sensing digital image processing in R, while chapter 2 covers pre-processing. Chapter 3 focuses on image transformation, and chapter 4 addresses image classification. Lastly, chapter 5 deals with improving image classification. R is advantageous in that it is open source software, available free of charge and includes several useful features that are not available in commercial software packages. This book benefits all undergraduate and graduate students, researchers, university teachers and other remote- sensing practitioners interested in the practical implementation of remote sensing in R.
Category: Technology & Engineering

Computer Vision Methods For Fast Image Classification And Retrieval

Author : Rafał Scherer
ISBN : 9783030121952
Genre : Technology & Engineering
File Size : 66.78 MB
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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.
Category: Technology & Engineering

Fuzzy Machine Learning Algorithms For Remote Sensing Image Classification

Author : Anil Kumar
ISBN : 9781000091540
Genre : Computers
File Size : 45.36 MB
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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 machine learning and deep learning algorithms. Both types of algorithms are described in such details that these can be implemented directly for thematic mapping of multiple-class or specific-class landcover from multispectral optical remote sensing data. These algorithms along with multi-date, multi-sensor remote sensing are capable to monitor specific stage (for e.g., phenology of growing crop) of a particular class also included. With these capabilities fuzzy machine learning algorithms have strong applications in areas like crop insurance, forest fire mapping, stubble burning, post disaster damage mapping etc. It also provides details about the temporal indices database using proposed Class Based Sensor Independent (CBSI) approach supported by practical examples. As well, this book addresses other related algorithms based on distance, kernel based as well as spatial information through Markov Random Field (MRF)/Local convolution methods to handle mixed pixels, non-linearity and noisy pixels. Further, this book covers about techniques for quantiative assessment of soft classified fraction outputs from soft classification and supported by in-house developed tool called sub-pixel multi-spectral image classifier (SMIC). It is aimed at graduate, postgraduate, research scholars and working professionals of different branches such as Geoinformation sciences, Geography, Electrical, Electronics and Computer Sciences etc., working in the fields of earth observation and satellite image processing. Learning algorithms discussed in this book may also be useful in other related fields, for example, in medical imaging. Overall, this book aims to: exclusive focus on using large range of fuzzy classification algorithms for remote sensing images; discuss ANN, CNN, RNN, and hybrid learning classifiers application on remote sensing images; describe sub-pixel multi-spectral image classifier tool (SMIC) to support discussed fuzzy and learning algorithms; explain how to assess soft classified outputs as fraction images using fuzzy error matrix (FERM) and its advance versions with FERM tool, Entropy, Correlation Coefficient, Root Mean Square Error and Receiver Operating Characteristic (ROC) methods and; combines explanation of the algorithms with case studies and practical applications.
Category: Computers

Satellite Image Analysis Clustering And Classification

Author : Surekha Borra
ISBN : 9789811364242
Genre : Technology & Engineering
File Size : 27.13 MB
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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.
Category: Technology & Engineering

Transfer Learning For Image Classification

Author : Ying Lu
ISBN : OCLC:1062365427
Genre :
File Size : 29.67 MB
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When learning a classification model for a new target domain with only a small amount of training samples, brute force application of machine learning algorithms generally leads to over-fitted classifiers with poor generalization skills. On the other hand, collecting a sufficient number of manually labeled training samples may prove very expensive. Transfer Learning methods aim to solve this kind of problems by transferring knowledge from related source domain which has much more data to help classification in the target domain. Depending on different assumptions about target domain and source domain, transfer learning can be further categorized into three categories: Inductive Transfer Learning, Transductive Transfer Learning (Domain Adaptation) and Unsupervised Transfer Learning. We focus on the first one which assumes that the target task and source task are different but related. More specifically, we assume that both target task and source task are classification tasks, while the target categories and source categories are different but related. We propose two different methods to approach this ITL problem. In the first work we propose a new discriminative transfer learning method, namely DTL, combining a series of hypotheses made by both the model learned with target training samples, and the additional models learned with source category samples. Specifically, we use the sparse reconstruction residual as a basic discriminant, and enhance its discriminative power by comparing two residuals from a positive and a negative dictionary. On this basis, we make use of similarities and dissimilarities by choosing both positively correlated and negatively correlated source categories to form additional dictionaries. A new Wilcoxon-Mann-Whitney statistic based cost function is proposed to choose the additional dictionaries with unbalanced training data. Also, two parallel boosting processes are applied to both the positive and negative data distributions to further improve classifier performance. On two different image classification databases, the proposed DTL consistently out performs other state-of-the-art transfer learning methods, while at the same time maintaining very efficient runtime. In the second work we combine the power of Optimal Transport and Deep Neural Networks to tackle the ITL problem. Specifically, we propose a novel method to jointly fine-tune a Deep Neural Network with source data and target data. By adding an Optimal Transport loss (OT loss) between source and target classifier predictions as a constraint on the source classifier, the proposed Joint Transfer Learning Network (JTLN) can effectively learn useful knowledge for target classification from source data. Furthermore, by using different kind of metric as cost matrix for the OT loss, JTLN can incorporate different prior knowledge about the relatedness between target categories and source categories. We carried out experiments with JTLN based on Alexnet on image classification datasets and the results verify the effectiveness of the proposed JTLN in comparison with standard consecutive fine-tuning. To the best of our knowledge, the proposed JTLN is the first work to tackle ITL with Deep Neural Networks while incorporating prior knowledge on relatedness between target and source categories. This Joint Transfer Learning with OT loss is general and can also be applied to other kind of Neural Networks.
Category:

Deep Learning For Computer Vision

Author : Jason Brownlee
ISBN :
Genre : Computers
File Size : 34.49 MB
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Step-by-step tutorials on deep learning neural networks for computer vision in python with Keras.
Category: Computers

Signal Processing Image Processing And Pattern Recognition

Author : Dominik Slezak
ISBN : 9783642105456
Genre : Computers
File Size : 56.85 MB
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As future generation information technology (FGIT) becomes specialized and fr- mented, it is easy to lose sight that many topics in FGIT have common threads and, because of this, advances in one discipline may be transmitted to others. Presentation of recent results obtained in different disciplines encourages this interchange for the advancement of FGIT as a whole. Of particular interest are hybrid solutions that c- bine ideas taken from multiple disciplines in order to achieve something more signi- cant than the sum of the individual parts. Through such hybrid philosophy, a new principle can be discovered, which has the propensity to propagate throughout mul- faceted disciplines. FGIT 2009 was the first mega-conference that attempted to follow the above idea of hybridization in FGIT in a form of multiple events related to particular disciplines of IT, conducted by separate scientific committees, but coordinated in order to expose the most important contributions. It included the following international conferences: Advanced Software Engineering and Its Applications (ASEA), Bio-Science and Bio-Technology (BSBT), Control and Automation (CA), Database Theory and Application (DTA), D- aster Recovery and Business Continuity (DRBC; published independently), Future G- eration Communication and Networking (FGCN) that was combined with Advanced Communication and Networking (ACN), Grid and Distributed Computing (GDC), M- timedia, Computer Graphics and Broadcasting (MulGraB), Security Technology (SecTech), Signal Processing, Image Processing and Pattern Recognition (SIP), and- and e-Service, Science and Technology (UNESST).
Category: Computers

Image Recognition And Classification

Author : Bahram Javidi
ISBN : 9780824744328
Genre : Technology & Engineering
File Size : 24.60 MB
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"Details the latest image processing algorithms and imaging systems for image recognition with diverse applications to the military; the transportation, aerospace, information security, and biomedical industries; radar systems; and image tracking systems."
Category: Technology & Engineering

Image Analysis And Recognition

Author : Mohamed Kamel
ISBN : 9783540290698
Genre : Computers
File Size : 81.35 MB
Format : PDF
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This book constitutes the refereed proceedings of the Second International Conference on Image Analysis and Recognition, ICIAR 2005, held in Toronto, Canada, in September 2005.The 153 revised full papers presented together with 2 invited papers were carefully reviewed and selected from 295 submissions. The papers are organized in topical sections on image segmentation, image and video processing and analysis, image and video coding, shape and matching, image description and recognition, image retrieval and indexing, 3D imaging, morphology, colour analysis, texture analysis, motion analysis, tracking, biomedical applications, face recognition and biometrics, image secret sharing, single-sensor imaging, and real-time imaging.
Category: Computers

Image Analysis Classification And Change Detection In Remote Sensing

Author : Morton J. Canty
ISBN : 0849372518
Genre : Technology & Engineering
File Size : 66.28 MB
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With an ever-increasing availability of aerial and satellite Earth observation data, image analysis has become an essential part of remote sensing. Image Analysis, Classification and Change Detection in Remote Sensing: With Algorithms for ENVI/IDL combines theory, algorithms, and computer codes and conveys required proficiency in vector algebra and basic statistics. It covers such topics as basic Fourier transforms, wavelets, principle components, minimum noise fraction transformation, and othorectification. The text also discusses panchromatic sharpening, explores multivariate change detection, examines supervised and unsupervised land cover classification and hyperspectral analysis. With programming examples in IDL and applications that support ENVI, it offers many extensions, such as for data fusion, statistical change detection, clustering and supervised classification with neural networks, all available as downloadable source code. Focusing on pixel-oriented analysis of visual/infrared Earth observation satellite imagery, this book extends the ENVI interface in IDL in order to implement new methods and algorithms of arbitrary sophistication. All of the illustrations and applications in the text are programmed in RSI's ENVI/IDL. The software and source code is available for download at: http://www.crcpress.com/product/isbn/9780849372513 Ideal for undergraduate and graduate student, this book provides exercises and small programming projects at the end of each chapter. A solutions manual is also available.
Category: Technology & Engineering

Neural Networks And Transfer Learning For Image Classification

Author : John William Soper
ISBN : OCLC:1140511192
Genre : Digital images
File Size : 37.15 MB
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This paper examines the effectiveness of Transfer Learning for classification across three datasets. The Keras deep learning library supplies popular CNN architectures, many are image competition winners, which are pretrained with ImageNet weights. Transfer learning with these models had higher classification accuracy with dog breeds and flower species than with fully trained basic CNNs. Image augmentation was found to be helpful or neutral. Fine-tuning could shift performance is either direction. The Inception type models performed better than the others, and an ensemble of them performed best. A quality difference between two dog breed image datasets reflected in different performances. However, it masked an issue about whether a dataset derived from ImageNet got an unfair advantage in transfer learning. A flower dataset with opposite properties from the dog breeds (i.e. not in ImageNet, few classes, many samples per class), showed the same training trends without surprises.
Category: Digital images

Digital Image Processing

Author : Bernd Jähne
ISBN : 3540240357
Genre : Technology & Engineering
File Size : 29.50 MB
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This long-established and well-received monograph offers an integral view of image processing - from image acquisition to the extraction of the data of interest – written by a physical scientists for other scientists. Supplements discussion of the general concepts is supplemented with examples from applications on PC-based image processing systems and ready-to-use implementations of important algorithms. Completely revised and extended, the most notable extensions being a detailed discussion on random variables and fields, 3-D imaging techniques and a unified approach to regularized parameter estimation.
Category: Technology & Engineering

Signal Processing Image Processing And Pattern Recognition

Author : Tai-hoon Kim
ISBN : 9783642271823
Genre : Computers
File Size : 73.87 MB
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This book comprises selected papers of the International Conference on Signal Processing, Image Processing and Pattern Recognition, SIP 2011, held as Part of the Future Generation Information Technology Conference, FGIT 2011, in Conjunction with GDC 2011, in Conjunction with GDC 2011, Jeju Island, Korea, in December 2011. The papers presented were carefully reviewed and selected from numerous submissions and focus on the various aspects of signal processing, image processing and pattern recognition.
Category: Computers

Image Classification Using Fuzzy Fca

Author : Niruktha Roy Gotoor
ISBN : OCLC:1143250820
Genre :
File Size : 66.67 MB
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Formal concept analysis(FCA) is a mathematical theory based on lattice and order theory used for data analysis and knowledge representation. It has been used in various domains such as data mining, machine learning, semantic web, Sciences, for the purpose of data analysis and Ontology over the last few decades. Various extensions of FCA are being researched to expand it's scope over more departments. In this thesis,we review the theory of Formal Concept Analysis(FCA) and its ex- tension Fuzzy FCA. Many studies to use FCA in data mining and text learning have been pursued. We extend these studies to include classification problems as well. Formal Concept Analysis is a mathematical theory of concepts and conceptual hierarchies, called concept lattices. It studies how objects can hierarchically be grouped together according to their common attributes. FCA is based on a mathematical order theory for data analysis, which extracts concepts and builds a conceptual hierarchy from given data. In order to analyze vague data set of uncertainty information, Fuzzy Formal Concept Analysis(Fuzzy FCA) incorporates fuzzy set theory into FCA. We propose an implementation of fuzzy FCA, a novel approach based on FCA and probability theory for learning and classification problems. It uses an enhanced version of FuzzyLattice which has been developed to store class labels and probability vectors and has the capability to be used for classifying instances with encoded and unlabeled features. We also propose a novel approach to generate intents and extents for each of the nodes in the lattice. We intend to scan the MNIST image database and classify using Fuzzy FCA. Our proposed algorithm for Fuzzy FCA generates a concept lattice creating clusters of images that share similar attributes which meet a minimum threshold value. We evaluate the algorithm on the MNIST images and compare the results with well- known classification algorithms.
Category:

Ubiquitous Computing And Multimedia Applications

Author : Tai-hoon Kim
ISBN : 9783642209970
Genre : Computers
File Size : 72.38 MB
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This two-volume set (CCIS 150 and CCIS 151) constitutes the refereed proceedings of the Second International Conference on Ubiquitous Computing and Multimedia Applications, UCMA 2011, held in Daejeon, Korea, in April 2011. The 86 revised full papers presented were carefully reviewed and selected from 570 submissions. Focusing on various aspects of advances in multimedia applications and ubiquitous computing with computational sciences, mathematics and information technology the papers present current research in the area of multimedia and ubiquitous environment including models and systems, new directions, novel applications associated with the utilization, and acceptance of ubiquitous computing devices and systems.
Category: Computers

Supervised And Unsupervised Pattern Recognition

Author : Evangelia Miche Tzanakou
ISBN : 1420049771
Genre : Technology & Engineering
File Size : 72.11 MB
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There are many books on neural networks, some of which cover computational intelligence, but none that incorporate both feature extraction and computational intelligence, as Supervised and Unsupervised Pattern Recognition does. This volume describes the application of a novel, unsupervised pattern recognition scheme to the classification of various types of waveforms and images. This substantial collection of recent research begins with an introduction to Neural Networks, classifiers, and feature extraction methods. It then addresses unsupervised and fuzzy neural networks and their applications to handwritten character recognition and recognition of normal and abnormal visual evoked potentials. The third section deals with advanced neural network architectures-including modular design-and their applications to medicine and three-dimensional NN architecture simulating brain functions. The final section discusses general applications and simulations, such as the establishment of a brain-computer link, speaker identification, and face recognition. In the quickly changing field of computational intelligence, every discovery is significant. Supervised and Unsupervised Pattern Recognition gives you access to many notable findings in one convenient volume.
Category: Technology & Engineering

Unsupervised Classification

Author : Sanghamitra Bandyopadhyay
ISBN : 9783642324512
Genre : Computers
File Size : 85.18 MB
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Clustering is an important unsupervised classification technique where data points are grouped such that points that are similar in some sense belong to the same cluster. Cluster analysis is a complex problem as a variety of similarity and dissimilarity measures exist in the literature. This is the first book focused on clustering with a particular emphasis on symmetry-based measures of similarity and metaheuristic approaches. The aim is to find a suitable grouping of the input data set so that some criteria are optimized, and using this the authors frame the clustering problem as an optimization one where the objectives to be optimized may represent different characteristics such as compactness, symmetrical compactness, separation between clusters, or connectivity within a cluster. They explain the techniques in detail and outline many detailed applications in data mining, remote sensing and brain imaging, gene expression data analysis, and face detection. The book will be useful to graduate students and researchers in computer science, electrical engineering, system science, and information technology, both as a text and as a reference book. It will also be useful to researchers and practitioners in industry working on pattern recognition, data mining, soft computing, metaheuristics, bioinformatics, remote sensing, and brain imaging.
Category: Computers

Remote Sensing Image Analysis Including The Spatial Domain

Author : Steven M. de Jong
ISBN : 1402025602
Genre : Science
File Size : 59.82 MB
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Remote Sensing image analysis is mostly done using only spectral information on a pixel by pixel basis. Information captured in neighbouring cells, or information about patterns surrounding the pixel of interest often provides useful supplementary information. This book presents a wide range of innovative and advanced image processing methods for including spatial information, captured by neighbouring pixels in remotely sensed images, to improve image interpretation or image classification. Presented methods include different types of variogram analysis, various methods for texture quantification, smart kernel operators, pattern recognition techniques, image segmentation methods, sub-pixel methods, wavelets and advanced spectral mixture analysis techniques. Apart from explaining the working methods in detail a wide range of applications is presented covering land cover and land use mapping, environmental applications such as heavy metal pollution, urban mapping and geological applications to detect hydrocarbon seeps. The book is meant for professionals, PhD students and graduates who use remote sensing image analysis, image interpretation and image classification in their work related to disciplines such as geography, geology, botany, ecology, forestry, cartography, soil science, engineering and urban and regional planning.
Category: Science

Image Processing For Remote Sensing

Author : C.H. Chen
ISBN : 142006665X
Genre : Technology & Engineering
File Size : 74.83 MB
Format : PDF
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Edited by leaders in the field, with contributions by a panel of experts, Image Processing for Remote Sensing explores new and unconventional mathematics methods. The coverage includes the physics and mathematical algorithms of SAR images, a comprehensive treatment of MRF-based remote sensing image classification, statistical approaches for
Category: Technology & Engineering