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Image Recognition And Classification
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Book Synopsis Image Recognition and Classification by : Bahram Javidi
Download or read book Image Recognition and Classification written by Bahram Javidi and published by CRC Press. This book was released on 2002-06-14 with total page 519 pages. Available in PDF, EPUB and Kindle. Book excerpt: "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."
Book Synopsis Pattern Recognition and Classification by : Geoff Dougherty
Download or read book Pattern Recognition and Classification written by Geoff Dougherty and published by Springer Science & Business Media. This book was released on 2012-10-28 with total page 203 pages. Available in PDF, EPUB and Kindle. Book excerpt: The use of pattern recognition and classification is fundamental to many of the automated electronic systems in use today. However, despite the existence of a number of notable books in the field, the subject remains very challenging, especially for the beginner. Pattern Recognition and Classification presents a comprehensive introduction to the core concepts involved in automated pattern recognition. It is designed to be accessible to newcomers from varied backgrounds, but it will also be useful to researchers and professionals in image and signal processing and analysis, and in computer vision. Fundamental concepts of supervised and unsupervised classification are presented in an informal, rather than axiomatic, treatment so that the reader can quickly acquire the necessary background for applying the concepts to real problems. More advanced topics, such as semi-supervised classification, combining clustering algorithms and relevance feedback are addressed in the later chapters. This book is suitable for undergraduates and graduates studying pattern recognition and machine learning.
Book Synopsis Machine Learning in Image Analysis and Pattern Recognition by : Munish Kumar
Download or read book Machine Learning in Image Analysis and Pattern Recognition written by Munish Kumar and published by MDPI. This book was released on 2021-09-08 with total page 112 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is to chart the progress in applying machine learning, including deep learning, to a broad range of image analysis and pattern recognition problems and applications. In this book, we have assembled original research articles making unique contributions to the theory, methodology and applications of machine learning in image analysis and pattern recognition.
Book Synopsis Content-Based Image Classification by : Rik Das
Download or read book Content-Based Image Classification written by Rik Das and published by CRC Press. This book was released on 2020-12-17 with total page 197 pages. Available in PDF, EPUB and Kindle. Book excerpt: Content-Based Image Classification: Efficient Machine Learning Using Robust Feature Extraction Techniques is a comprehensive guide to research with invaluable image data. Social Science Research Network has revealed that 65% of people are visual learners. Research data provided by Hyerle (2000) has clearly shown 90% of information in the human brain is visual. Thus, it is no wonder that visual information processing in the brain is 60,000 times faster than text-based information (3M Corporation, 2001). Recently, we have witnessed a significant surge in conversing with images due to the popularity of social networking platforms. The other reason for embracing usage of image data is the mass availability of high-resolution cellphone cameras. Wide usage of image data in diversified application areas including medical science, media, sports, remote sensing, and so on, has spurred the need for further research in optimizing archival, maintenance, and retrieval of appropriate image content to leverage data-driven decision-making. This book demonstrates several techniques of image processing to represent image data in a desired format for information identification. It discusses the application of machine learning and deep learning for identifying and categorizing appropriate image data helpful in designing automated decision support systems. The book offers comprehensive coverage of the most essential topics, including: Image feature extraction with novel handcrafted techniques (traditional feature extraction) Image feature extraction with automated techniques (representation learning with CNNs) Significance of fusion-based approaches in enhancing classification accuracy MATLAB® codes for implementing the techniques Use of the Open Access data mining tool WEKA for multiple tasks The book is intended for budding researchers, technocrats, engineering students, and machine learning/deep learning enthusiasts who are willing to start their computer vision journey with content-based image recognition. The readers will get a clear picture of the essentials for transforming the image data into valuable means for insight generation. Readers will learn coding techniques necessary to propose novel mechanisms and disruptive approaches. The WEKA guide provided is beneficial for those uncomfortable coding for machine learning algorithms. The WEKA tool assists the learner in implementing machine learning algorithms with the click of a button. Thus, this book will be a stepping-stone for your machine learning journey. Please visit the author's website for any further guidance at https://www.rikdas.com/
Book Synopsis Deep Learning for Computer Vision by : Jason Brownlee
Download or read book Deep Learning for Computer Vision written by Jason Brownlee and published by Machine Learning Mastery. This book was released on 2019-04-04 with total page 564 pages. Available in PDF, EPUB and Kindle. Book excerpt: Step-by-step tutorials on deep learning neural networks for computer vision in python with Keras.
Book Synopsis Classification Techniques for Medical Image Analysis and Computer Aided Diagnosis by : Nilanjan Dey
Download or read book Classification Techniques for Medical Image Analysis and Computer Aided Diagnosis written by Nilanjan Dey and published by Academic Press. This book was released on 2019-07-31 with total page 218 pages. Available in PDF, EPUB and Kindle. Book excerpt: Classification Techniques for Medical Image Analysis and Computer Aided Diagnosis covers the most current advances on how to apply classification techniques to a wide variety of clinical applications that are appropriate for researchers and biomedical engineers in the areas of machine learning, deep learning, data analysis, data management and computer-aided diagnosis (CAD) systems design. The book covers several complex image classification problems using pattern recognition methods, including Artificial Neural Networks (ANN), Support Vector Machines (SVM), Bayesian Networks (BN) and deep learning. Further, numerous data mining techniques are discussed, as they have proven to be good classifiers for medical images. Examines the methodology of classification of medical images that covers the taxonomy of both supervised and unsupervised models, algorithms, applications and challenges Discusses recent advances in Artificial Neural Networks, machine learning, and deep learning in clinical applications Introduces several techniques for medical image processing and analysis for CAD systems design
Book Synopsis Remote Sensing Image Classification in R by : Courage Kamusoko
Download or read book Remote Sensing Image Classification in R written by Courage Kamusoko and published by Springer. This book was released on 2019-07-24 with total page 189 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Image Recognition and Classification by : Bahram Javidi
Download or read book Image Recognition and Classification written by Bahram Javidi and published by CRC Press. This book was released on 2002-06-14 with total page 530 pages. Available in PDF, EPUB and Kindle. Book excerpt: "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."
Book Synopsis Deep Learning for Image Processing Applications by : D.J. Hemanth
Download or read book Deep Learning for Image Processing Applications written by D.J. Hemanth and published by IOS Press. This book was released on 2017-12 with total page 284 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep learning and image processing are two areas of great interest to academics and industry professionals alike. The areas of application of these two disciplines range widely, encompassing fields such as medicine, robotics, and security and surveillance. The aim of this book, ‘Deep Learning for Image Processing Applications’, is to offer concepts from these two areas in the same platform, and the book brings together the shared ideas of professionals from academia and research about problems and solutions relating to the multifaceted aspects of the two disciplines. The first chapter provides an introduction to deep learning, and serves as the basis for much of what follows in the subsequent chapters, which cover subjects including: the application of deep neural networks for image classification; hand gesture recognition in robotics; deep learning techniques for image retrieval; disease detection using deep learning techniques; and the comparative analysis of deep data and big data. The book will be of interest to all those whose work involves the use of deep learning and image processing techniques.
Book Synopsis Pattern Recognition and Classification in Time Series Data by : Volna, Eva
Download or read book Pattern Recognition and Classification in Time Series Data written by Volna, Eva and published by IGI Global. This book was released on 2016-07-22 with total page 295 pages. Available in PDF, EPUB and Kindle. Book excerpt: Patterns can be any number of items that occur repeatedly, whether in the behaviour of animals, humans, traffic, or even in the appearance of a design. As technologies continue to advance, recognizing, mimicking, and responding to all types of patterns becomes more precise. Pattern Recognition and Classification in Time Series Data focuses on intelligent methods and techniques for recognizing and storing dynamic patterns. Emphasizing topics related to artificial intelligence, pattern management, and algorithm development, in addition to practical examples and applications, this publication is an essential reference source for graduate students, researchers, and professionals in a variety of computer-related disciplines.
Book Synopsis Practical Machine Learning and Image Processing by : Himanshu Singh
Download or read book Practical Machine Learning and Image Processing written by Himanshu Singh and published by Apress. This book was released on 2019-02-26 with total page 177 pages. Available in PDF, EPUB and Kindle. Book excerpt: Gain insights into image-processing methodologies and algorithms, using machine learning and neural networks in Python. This book begins with the environment setup, understanding basic image-processing terminology, and exploring Python concepts that will be useful for implementing the algorithms discussed in the book. You will then cover all the core image processing algorithms in detail before moving onto the biggest computer vision library: OpenCV. You’ll see the OpenCV algorithms and how to use them for image processing. The next section looks at advanced machine learning and deep learning methods for image processing and classification. You’ll work with concepts such as pulse coupled neural networks, AdaBoost, XG boost, and convolutional neural networks for image-specific applications. Later you’ll explore how models are made in real time and then deployed using various DevOps tools. All the concepts in Practical Machine Learning and Image Processing are explained using real-life scenarios. After reading this book you will be able to apply image processing techniques and make machine learning models for customized application. What You Will LearnDiscover image-processing algorithms and their applications using Python Explore image processing using the OpenCV library Use TensorFlow, scikit-learn, NumPy, and other libraries Work with machine learning and deep learning algorithms for image processing Apply image-processing techniques to five real-time projects Who This Book Is For Data scientists and software developers interested in image processing and computer vision.
Book Synopsis Image Recognition by : Charles Z. Liu
Download or read book Image Recognition written by Charles Z. Liu and published by Nova Science Publishers. This book was released on 2020-04 with total page 370 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book focuses on research trends in image processing and recognition and corresponding developments. Among them, the book focuses on recent research, especially in the field of advanced human-computer interaction and intelligent computing. Given the existing interaction and recognition of the station, some novel topics are proposed, including how to establish a cognitive model in human-computer interaction and how to express and transfer human knowledge into human-machine image recognition. In an interactive implementation, how to implement user experience through image recognition during machine interaction.The main contents of this book are arranged as follows. Chapter 1 introduces the research background, research questions, goals, research questions and overviews of this book. Chapter 2 focuses on image calculation methods based on principal component analysis (PCA) and related extensions. Chapter 3 presents an image processing scheme that takes into account the user experience and the optimal balance between QoE and QoS management. Chapter 4 focuses on the performance analysis of methods for classifying image textures based on local binary patterns. Chapter 5 introduces the generation of the anti-network (GAN) and its methods. Chapter 6 mainly discusses the recognition of the interest target as the visual consciousness of the image computing system and proposes a fuzzy target-based interest target differentiation system, which is applied to the extinction enhancement as a display.Chapter 7 focuses on the implementation and application of PCA image processing and its application in computer vision in the fields of image compression, visual tracking, image recognition, and super-resolution image reconstruction. Chapter 8 introduces various applications of feature extraction and classification techniques in seizures. Chapter 9 introduces some typical image processing based on GAN, involving multiple fields. Chapter 10 introduces an agent-based collaborative information processing framework with stereo vision applications. Chapter 11 introduces the MR application system as a synthesis of the methods and algorithms in each of the above chapters and discusses system design and implementation in terms of functions, modules, and workflows. Chapter 12 evaluates the book, draws conclusions, and proposes advances in image recognition and its advances in image recognition, limitations, and future work, and applies them to intelligent HCI in system design. Objects, human knowledge and user experience, QoE-QoS management, system management, and confidentiality and security.
Book Synopsis Handbook of Research on Disease Prediction Through Data Analytics and Machine Learning by : Rani, Geeta
Download or read book Handbook of Research on Disease Prediction Through Data Analytics and Machine Learning written by Rani, Geeta and published by IGI Global. This book was released on 2020-10-16 with total page 586 pages. Available in PDF, EPUB and Kindle. Book excerpt: By applying data analytics techniques and machine learning algorithms to predict disease, medical practitioners can more accurately diagnose and treat patients. However, researchers face problems in identifying suitable algorithms for pre-processing, transformations, and the integration of clinical data in a single module, as well as seeking different ways to build and evaluate models. The Handbook of Research on Disease Prediction Through Data Analytics and Machine Learning is a pivotal reference source that explores the application of algorithms to making disease predictions through the identification of symptoms and information retrieval from images such as MRIs, ECGs, EEGs, etc. Highlighting a wide range of topics including clinical decision support systems, biomedical image analysis, and prediction models, this book is ideally designed for clinicians, physicians, programmers, computer engineers, IT specialists, data analysts, hospital administrators, researchers, academicians, and graduate and post-graduate students.
Book Synopsis Machine Learning by Tutorials (Second Edition) by : raywenderlich Tutorial Team
Download or read book Machine Learning by Tutorials (Second Edition) written by raywenderlich Tutorial Team and published by . This book was released on 2020-05-19 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Learn Machine Learning!Machine learning is one of those topics that can be daunting at first blush. It's not clear where to start, what path someone should take and what APIs to learn in order to get started teaching machines how to learn.This is where Machine Learning by Tutorials comes in! In this book, we'll hold your hand through a number of tutorials, to get you started in the world of machine learning. We'll cover a wide range of popular topics in the field of machine learning, while developing apps that work on iOS devices.Who This Book Is ForThis books is for the intermediate iOS developer who already knows the basics of iOS and Swift development, but wants to understand how machine learning works.Topics covered in Machine Learning by TutorialsCoreML: Learn how to add a machine learning model to your iOS apps, and how to use iOS APIs to access it.Create ML: Learn how to create your own model using Apple's Create ML Tool.Turi Create and Keras: Learn how to tune parameters to improve your machine learning model using more advanced tools.Image Classification: Learn how to apply machine learning models to predict objects in an image.Convolutional Networks: Learn advanced machine learning techniques for predicting objects in an image with Convolutional Neural Networks (CNNs).Sequence Classification: Learn how you can use recurrent neural networks (RNNs) to classify motion from an iPhone's motion sensor.Text-to-text Transform: Learn how to use machine learning to convert bodies of text between two languages.By the end of this book, you'll have a firm understanding of what machine learning is, what it can and cannot do, and how you can use machine learning in your next app!
Book Synopsis Computer Vision – ECCV 2012 by : Andrew Fitzgibbon
Download or read book Computer Vision – ECCV 2012 written by Andrew Fitzgibbon and published by Springer. This book was released on 2012-09-26 with total page 909 pages. Available in PDF, EPUB and Kindle. Book excerpt: The seven-volume set comprising LNCS volumes 7572-7578 constitutes the refereed proceedings of the 12th European Conference on Computer Vision, ECCV 2012, held in Florence, Italy, in October 2012. The 408 revised papers presented were carefully reviewed and selected from 1437 submissions. The papers are organized in topical sections on geometry, 2D and 3D shapes, 3D reconstruction, visual recognition and classification, visual features and image matching, visual monitoring: action and activities, models, optimisation, learning, visual tracking and image registration, photometry: lighting and colour, and image segmentation.
Book Synopsis Object-Based Image Analysis by : Thomas Blaschke
Download or read book Object-Based Image Analysis written by Thomas Blaschke and published by Springer Science & Business Media. This book was released on 2008-08-09 with total page 804 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book brings together a collection of invited interdisciplinary persp- tives on the recent topic of Object-based Image Analysis (OBIA). Its c- st tent is based on select papers from the 1 OBIA International Conference held in Salzburg in July 2006, and is enriched by several invited chapters. All submissions have passed through a blind peer-review process resulting in what we believe is a timely volume of the highest scientific, theoretical and technical standards. The concept of OBIA first gained widespread interest within the GIScience (Geographic Information Science) community circa 2000, with the advent of the first commercial software for what was then termed ‘obje- oriented image analysis’. However, it is widely agreed that OBIA builds on older segmentation, edge-detection and classification concepts that have been used in remote sensing image analysis for several decades. Nevert- less, its emergence has provided a new critical bridge to spatial concepts applied in multiscale landscape analysis, Geographic Information Systems (GIS) and the synergy between image-objects and their radiometric char- teristics and analyses in Earth Observation data (EO).
Book Synopsis Computer Vision -- ECCV 2010 by : Kostas Daniilidis
Download or read book Computer Vision -- ECCV 2010 written by Kostas Daniilidis and published by Springer Science & Business Media. This book was released on 2010-08-30 with total page 828 pages. Available in PDF, EPUB and Kindle. Book excerpt: The six-volume set comprising LNCS volumes 6311 until 6313 constitutes the refereed proceedings of the 11th European Conference on Computer Vision, ECCV 2010, held in Heraklion, Crete, Greece, in September 2010. The 325 revised papers presented were carefully reviewed and selected from 1174 submissions. The papers are organized in topical sections on object and scene recognition; segmentation and grouping; face, gesture, biometrics; motion and tracking; statistical models and visual learning; matching, registration, alignment; computational imaging; multi-view geometry; image features; video and event characterization; shape representation and recognition; stereo; reflectance, illumination, color; medical image analysis.
