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This book gathers papers presented at the VipIMAGE 2017-VI ECCOMAS Thematic Conference on Computational Vision and Medical Image Processing. It highlights invited lecturers and full papers presented at the conference, which was held in Porto, Portugal, on October 18–20, 2017. These international contributions provide comprehensive coverage on the state-of-the-art in the following fields: 3D Vision, Computational Bio-Imaging and Visualization, Computational Vision, Computer Aided Diagnosis, Surgery, Therapy and Treatment, Data Interpolation, Registration, Acquisition and Compression, Industrial Inspection, Image Enhancement, Image Processing and Analysis, Image Segmentation, Medical Imaging, Medical Rehabilitation, Physics of Medical Imaging, Shape Reconstruction, Signal Processing, Simulation and Modelling, Software Development for Image Processing and Analysis, Telemedicine Systems and their Applications, Tracking and Analysis of Movement, and Deformation and Virtual Reality.

In addition, it explores a broad range of related techniques, methods and applications, including: trainable filters, bilateral filtering, statistical, geometrical and physical modelling, fuzzy morphology, region growing, grabcut, variational methods, snakes, the level set method, finite element method, wavelet transform, multi-objective optimization, scale invariant feature transform, Laws’ texture-energy measures, expectation maximization, the Markov random fields bootstrap, feature extraction and classification, support vector machines, random forests, decision trees, deep learning, and stereo vision.

Given its breadth of coverage, the book offers a valuable resource for academics, researchers and professionals in Biomechanics, Biomedical Engineering, Computational Vision (image processing and analysis), Computer Sciences, Computational Mechanics, Signal Processing, Medicine and Rehabilitation.

"This library is useful for practitioners, and is an excellent tool for those entering the field: it is a set of computer vision algorithms that work as advertised."-William T. Freeman, Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology

Learning OpenCV puts you in the middle of the rapidly expanding field of computer vision. Written by the creators of the free open source OpenCV library, this book introduces you to computer vision and demonstrates how you can quickly build applications that enable computers to "see" and make decisions based on that data.

Computer vision is everywhere-in security systems, manufacturing inspection systems, medical image analysis, Unmanned Aerial Vehicles, and more. It stitches Google maps and Google Earth together, checks the pixels on LCD screens, and makes sure the stitches in your shirt are sewn properly. OpenCV provides an easy-to-use computer vision framework and a comprehensive library with more than 500 functions that can run vision code in real time.

Learning OpenCV will teach any developer or hobbyist to use the framework quickly with the help of hands-on exercises in each chapter. This book includes:A thorough introduction to OpenCVGetting input from camerasTransforming imagesSegmenting images and shape matchingPattern recognition, including face detectionTracking and motion in 2 and 3 dimensions3D reconstruction from stereo visionMachine learning algorithms

Getting machines to see is a challenging but entertaining goal. Whether you want to build simple or sophisticated vision applications, Learning OpenCV is the book you need to get started.

Get up and running with the latest numerical computing library by Google and dive deeper into your data!About This BookGet the first book on the market that shows you the key aspects TensorFlow, how it works, and how to use it for the second generation of machine learningWant to perform faster and more accurate computations in the field of data science? This book will acquaint you with an all-new refreshing library—TensorFlow!Dive into the next generation of numerical computing and get the most out of your data with this quick guideWho This Book Is For

This book is dedicated to all the machine learning and deep learning enthusiasts, data scientists, researchers, and even students who want to perform more accurate, fast machine learning operations with TensorFlow. Those with basic knowledge of programming (Python and C/C++) and math concepts who want to be introduced to the topics of machine learning will find this book useful.

What You Will LearnInstall and adopt TensorFlow in your Python environment to solve mathematical problemsGet to know the basic machine and deep learning conceptsTrain and test neural networks to fit your data modelMake predictions using regression algorithmsAnalyze your data with a clustering procedureDevelop algorithms for clustering and data classificationUse GPU computing to analyze big dataIn Detail

Google's TensorFlow engine, after much fanfare, has evolved in to a robust, user-friendly, and customizable, application-grade software library of machine learning (ML) code for numerical computation and neural networks.

This book takes you through the practical software implementation of various machine learning techniques with TensorFlow. In the first few chapters, you'll gain familiarity with the framework and perform the mathematical operations required for data analysis. As you progress further, you'll learn to implement various machine learning techniques such as classification, clustering, neural networks, and deep learning through practical examples.

By the end of this book, you'll have gained hands-on experience of using TensorFlow and building classification, image recognition systems, language processing, and information retrieving systems for your application.

Style and approach

Get quickly up and running with TensorFlow using this fast-paced guide. You will get to know everything that can be done with TensorFlow and we'll show you how to implement it in your environment. The examples in the book are from the core of the computation industry—something you can connect to and will find familiar.

Create advanced applications with Python and OpenCV, exploring the potential of facial recognition, machine learning, deep learning, web computing and augmented reality.Key FeaturesDevelop your computer vision skills by mastering algorithms in Open Source Computer Vision 4 (OpenCV 4)and PythonApply machine learning and deep learning techniques with TensorFlow, Keras, and PyTorchDiscover the modern design patterns you should avoid when developing efficient computer vision applicationsBook Description

OpenCV is considered to be one of the best open source computer vision and machine learning software libraries. It helps developers build complete projects in relation to image processing, motion detection, or image segmentation, among many others. OpenCV for Python enables you to run computer vision algorithms smoothly in real time, combining the best of the OpenCV C++ API and the Python language.

In this book, you'll get started by setting up OpenCV and delving into the key concepts of computer vision. You'll then proceed to study more advanced concepts and discover the full potential of OpenCV. The book will also introduce you to the creation of advanced applications using Python and OpenCV, enabling you to develop applications that include facial recognition, target tracking, or augmented reality. Next, you'll learn machine learning techniques and concepts, understand how to apply them in real-world examples, and also explore their benefits, including real-time data production and faster data processing. You'll also discover how to translate the functionality provided by OpenCV into optimized application code projects using Python bindings. Toward the concluding chapters, you'll explore the application of artificial intelligence and deep learning techniques using the popular Python libraries TensorFlow, and Keras.

By the end of this book, you'll be able to develop advanced computer vision applications to meet your customers' demands.

What you will learnHandle files and images, and explore various image processing techniquesExplore image transformations, including translation, resizing, and croppingGain insights into building histogramsBrush up on contour detection, filtering, and drawingWork with Augmented Reality to build marker-based and markerless applicationsWork with the main machine learning algorithms in OpenCVExplore the deep learning Python libraries and OpenCV deep learning capabilitiesCreate computer vision and deep learning web applicationsWho this book is for

This book is designed for computer vision developers, engineers, and researchers who want to develop modern computer vision applications. Basic experience of OpenCV and Python programming is a must.

Featuring hundreds of full-color photomicrographs, Rodak’s Hematology: Clinical Principles and Applications, 5th Edition prepares you for a job in the clinical lab by exploring the essential aspects of hematology. It shows how to accurately identify cells, simplifies hemostasis and thrombosis concepts, and covers normal hematopoiesis through diseases of erythroid, myeloid, lymphoid, and megakaryocytic origins. This text also makes it easy to understand complementary testing areas such as flow cytometry, cytogenetics, and molecular diagnostics. Clinical lab experts Elaine Keohane, Larry Smith, and Jeanine Walenga also cover key topics such as working in a hematology lab, the parts and functions of the cell, and laboratory testing of blood cells and body fluid cells.Instructions for lab procedures include sources of possible errors along with comments.Case studies in each chapter provide opportunities to apply hematology concepts to real-life scenarios.Hematology instruments are described, compared, and contrasted. UPDATED, full-color illustrations make it easier to visualize hematology concepts and show what you’ll encounter in the lab, with images appearing near their mentions in the text so you don’t have to flip pages back and forth.Hematology/hemostasis reference ranges are listed on the inside front and back covers for quick reference.A bulleted summary makes it easy to review the important points in every chapter.Learning objectives begin each chapter and indicate what you should achieve, with review questions appearing at the end.A glossary of key terms makes it easy to find and learn definitions. NEW coverage of hematogones in the chapter on pediatric and geriatric hematology helps you identify these cells, a skill that is useful in diagnosing some pediatric leukemias.UPDATED chapter on molecular diagnostics covers new technology and techniques used in the lab.
Elsevier's Medical Laboratory Science Examination Review is a brand-new resource that offers all the review, practice, and support you need to prepare for the either the MLS or MLT certification examination. Each chapter in the book offers a thorough review on one of the core areas of Medical Laboratory Science as outlined by the ASCP Board of Certification. Practice questions are also featured at the end of each chapter and explanations and rationales for each correct answer appear at the end of the text. Plus, an eight-page full-color insert displays photomicrographs of hematological and microbiological specimens exactly as they appear under the microscope and on the MLS and MLT certification exams. A mock certifications exam is included in the print book as well as online at the companion Evolve website – which also houses additional practice questions – totaling 1,000 questions in all.Inclusion of both MLS and MLT level content and questions enables the book to be used for both certification examsPrint mock exam at the end of the book contains 100 certification examination preparation questions.Content reviews in outline form enables each topic to be easily reviewed but covered in an appropriate depth.Online mock exams on the companion Evolve website include all the practice questions from the book plus additional unique questions that can be used to create mock exams for extra practice.Eight-page full-color insert within the book features 50 illustrations that show hematological and microbiological photomicrographs.Test-taking tips and suggestions discuss the exam, how it’s set up and scored, when to answer, guess and not answers questions, how to identify distracters, and more.
With an illustrated, storyboard format for procedures, Phlebotomy: Worktext and Procedures Manual, 4th Edition describes all aspects of phlebotomy, with current coverage of equipment, safety procedures, arterial blood gases, point-of-care testing, and practical phlebotomy skills. Procedures cover core functions and are outlined with step-by-step instructions and new full-color photos. Clinical scenarios, practice tips, and new Avoid That Error features keep the focus on application and practice. Written by phlebotomy expert Robin Warekois, this practical worktext also includes competency checklists, a mock certification exam, a detachable bookmark that can serve as a tube guide, and a new video collection on the Evolve companion website.A detailed, storyboard format outlines common procedures, with steps accompanied by new full-color photos.Study and certification exam preparation questions in each chapter help you review and remember the material.A mock certification exam in the appendix mirrors the format of the actual phlebotomy certification exam, allowing you to review for the exam with 150 multiple-choice questions.Competency Checklists at the end of the book summarize the most critical and important steps in phlebotomy procedures.Clinical scenarios and tips encourage you apply your knowledge to real-life challenges in the workplace.Student resources on an Evolve companion website include a pre-test, animations, a new procedural video collection, interactive exercises, a mock certification exam, and an audio glossary.An anatomy and physiology section offers illustrated, in-depth information on body systems.A perforated bookmark on the back cover serves as a quick, portable reminder of which stopper tops to use for various diagnostic tests.Flashbacks and Flashforwards provide a cross reference to related information in previous or upcoming chapters. NEW video collection on the Evolve companion website demonstrates how critical procedures are performed.NEW photos have been added, in addition to new content on professionalism and HIPAA, equipment, and technology.NEW! Avoid That Error scenarios help you develop critical thinking skills and provide helpful tips on resolving problematic situations.
Explore machine learning concepts using the latest numerical computing library — TensorFlow — with the help of this comprehensive cookbookAbout This BookYour quick guide to implementing TensorFlow in your day-to-day machine learning activitiesLearn advanced techniques that bring more accuracy and speed to machine learningUpgrade your knowledge to the second generation of machine learning with this guide on TensorFlowWho This Book Is For

This book is ideal for data scientists who are familiar with C++ or Python and perform machine learning activities on a day-to-day basis. Intermediate and advanced machine learning implementers who need a quick guide they can easily navigate will find it useful.

What You Will LearnBecome familiar with the basics of the TensorFlow machine learning libraryGet to know Linear Regression techniques with TensorFlowLearn SVMs with hands-on recipesImplement neural networks and improve predictionsApply NLP and sentiment analysis to your dataMaster CNN and RNN through practical recipesTake TensorFlow into productionIn Detail

TensorFlow is an open source software library for Machine Intelligence. The independent recipes in this book will teach you how to use TensorFlow for complex data computations and will let you dig deeper and gain more insights into your data than ever before. You'll work through recipes on training models, model evaluation, sentiment analysis, regression analysis, clustering analysis, artificial neural networks, and deep learning – each using Google's machine learning library TensorFlow.

This guide starts with the fundamentals of the TensorFlow library which includes variables, matrices, and various data sources. Moving ahead, you will get hands-on experience with Linear Regression techniques with TensorFlow. The next chapters cover important high-level concepts such as neural networks, CNN, RNN, and NLP.

Once you are familiar and comfortable with the TensorFlow ecosystem, the last chapter will show you how to take it to production.

Style and approach

This book takes a recipe-based approach where every topic is explicated with the help of a real-world example.

Learn how to model and train advanced neural networks to implement a variety of Computer Vision tasksKey Features Train different kinds of deep learning model from scratch to solve specific problems in Computer Vision Combine the power of Python, Keras, and TensorFlow to build deep learning models for object detection, image classification, similarity learning, image captioning, and more Includes tips on optimizing and improving the performance of your models under various constraintsBook Description

Deep learning has shown its power in several application areas of Artificial Intelligence, especially in Computer Vision. Computer Vision is the science of understanding and manipulating images, and finds enormous applications in the areas of robotics, automation, and so on. This book will also show you, with practical examples, how to develop Computer Vision applications by leveraging the power of deep learning.

In this book, you will learn different techniques related to object classification, object detection, image segmentation, captioning, image generation, face analysis, and more. You will also explore their applications using popular Python libraries such as TensorFlow and Keras. This book will help you master state-of-the-art, deep learning algorithms and their implementation.

What you will learnSet up an environment for deep learning with Python, TensorFlow, and KerasDefine and train a model for image and video classificationUse features from a pre-trained Convolutional Neural Network model for image retrievalUnderstand and implement object detection using the real-world Pedestrian Detection scenarioLearn about various problems in image captioning and how to overcome them by training images and text togetherImplement similarity matching and train a model for face recognitionUnderstand the concept of generative models and use them for image generationDeploy your deep learning models and optimize them for high performanceWho this book is for

This book is targeted at data scientists and Computer Vision practitioners who wish to apply the concepts of Deep Learning to overcome any problem related to Computer Vision. A basic knowledge of programming in Python—and some understanding of machine learning concepts—is required to get the best out of this book.

Introducing the practices and procedures of phlebotomy, Procedures in Phlebotomy, 4th Edition provides easy-to-read guidelines for both basic and special phlebotomy techniques. It describes proper procedures for venipuncture, special collection procedures, and pediatric and geriatric considerations, and addresses essential topics such as infection control, OSHA guidelines, and anatomy and physiology. It also discusses professional issues such as interpersonal communication, department management, total quality, and medical-legal topics. Written by expert phlebotomy educator John C. Flynn, Jr., this edition includes more in-depth content, a new chapter on medical terminology, new case studies, and a practice exam that prepares you for the phlebotomy certification exam.A 150-question practice exam provides a comprehensive review of content and prepares you for the phlebotomy certification examination with questions that mirror the exam's multiple-choice format.

Competency score sheets allow you to evaluate your mastery of newly acquired skills related to the most critical and important steps in phlebotomy procedures.

A color Tube Guide provides a quick reference for determining the type of tube to use for blood collection of common tests. Review questions at the end of each chapter reinforce your understanding and provide a self-assessment tool.

A glossary provides a quick reference to definitions for all of the book's terms. A companion Evolve website enhances learning with interactive quizzes and WebLinks for further reading and research. NEW content includes a new chapter on medical terminology, and also addresses patient quality issues, geriatric considerations, and point-of-care tests.

Case studies with critical thinking questions allow you to apply chapter content to real-life scenarios.

Lists of key terms identify new terminology.

Learning objectives begin each chapter, setting measurable outcomes you will achieve.

Spanish phrases related to phlebotomy are included in the appendix for quick reference.
In the second edition of Electronics for Guitarists author Denton Dailey teaches the basic theory of operation and design principles of analog guitar signal processing circuits and amplifiers. The design and operation of common effects circuits such as tone controls, preamps, phasers, flangers, envelope followers, distortion and overdrives are covered, as are both solid-state amplifiers and power supplies. Written primarily for the guitarist, this book balances coverage of theoretical analysis and design while providing many examples of practical experimental circuits.

The main thrust of the material is analog circuitry, focusing on fundamental principles of transistors, integrated circuit and vacuum tube-based amplifier operation and theory, and operation of typical guitar signal processing effects circuits. Updated to the new edition include:

• New coverage of tone control circuits, MOSFETS and their applications as small-signal amplifiers, rail splitters and charge pumps, amplifiers using germanium transistors, and tube power amp design
• Expanded coverage of numerous subjects such as vacuum tube power supplies, the digital oscilloscope, Darlington and Sziklai transistors, and signal spectra and transfer function symmetry
• Additional examples of various circuits such as overdrive, distortion, chorus, delay, tremolo and auto-wah circuits as well as amplifier design

Electronics for Guitarists is ideal for the musician or engineer interested in analog signal processing. The material is also useful to general electronics hobbyists, technologists and engineers with an interest in guitar and music-related electronics applications.
Perfect your lab skills with the gold standard in microbiology! Serving as both the #1 bench reference for practicing microbiologists and as a favorite text for students in clinical laboratory science programs, Bailey & Scott’s Diagnostic Microbiology, 14th Edition covers all the topical information and critical thinking practice you need for effective laboratory testing. This new edition also features hundreds step-by-step procedures, updated visuals, new case studies, and new material on the latest trends and equipment in clinical microbiology — including automation, automated streaking, MALDI-TOF, and incubator microscopes. It’s everything you need to get quality lab results in class and in clinical practice!More than 800 detailed, full-color illustrations aid comprehension and help in visualizing concepts.Expanded sections on parasitology, mycology, and virology eliminate the need to purchase separate books on this material.General and Species boxes in the organism chapters highlight the important topics that will be discussed in the chapter.Case studies provide the opportunity to apply information to a variety of diagnostic scenarios, and help improve decision-making and critical thinking skills.Hands-on procedures include step-by-step instructions, full-color photos, and expected results.A glossary of terms is found at the back of the book for quick reference.Learning objectives begin each chapter, offering a measurable outcome to achieve by the completing the material.Learning resources on the Evolve companion website enhance learning with review questions and procedures.NEW! Coverage of automation, automated streaking, MALDI-TOF, and incubator microscopes keeps you in the know on these progressing topics.NEW! Updated images provide a more vivid look into book content and reflect the latest procedures.NEW! Thoroughly reviewed and updated chapters equip you with the most current information.NEW! Significant lab manual improvements provide an excellent learning resource at no extra cost.NEW! 10 extra case studies on the Evolve companion website offer more opportunities to improve critical thinking skills.
Expand your OpenCV knowledge and master key concepts of machine learning using this practical, hands-on guide.About This BookLoad, store, edit, and visualize data using OpenCV and PythonGrasp the fundamental concepts of classification, regression, and clusteringUnderstand, perform, and experiment with machine learning techniques using this easy-to-follow guideEvaluate, compare, and choose the right algorithm for any taskWho This Book Is For

This book targets Python programmers who are already familiar with OpenCV; this book will give you the tools and understanding required to build your own machine learning systems, tailored to practical real-world tasks.

What You Will LearnExplore and make effective use of OpenCV's machine learning moduleLearn deep learning for computer vision with PythonMaster linear regression and regularization techniquesClassify objects such as flower species, handwritten digits, and pedestriansExplore the effective use of support vector machines, boosted decision trees, and random forestsGet acquainted with neural networks and Deep Learning to address real-world problemsDiscover hidden structures in your data using k-means clusteringGet to grips with data pre-processing and feature engineeringIn Detail

Machine learning is no longer just a buzzword, it is all around us: from protecting your email, to automatically tagging friends in pictures, to predicting what movies you like. Computer vision is one of today's most exciting application fields of machine learning, with Deep Learning driving innovative systems such as self-driving cars and Google's DeepMind.

OpenCV lies at the intersection of these topics, providing a comprehensive open-source library for classic as well as state-of-the-art computer vision and machine learning algorithms. In combination with Python Anaconda, you will have access to all the open-source computing libraries you could possibly ask for.

Machine learning for OpenCV begins by introducing you to the essential concepts of statistical learning, such as classification and regression. Once all the basics are covered, you will start exploring various algorithms such as decision trees, support vector machines, and Bayesian networks, and learn how to combine them with other OpenCV functionality. As the book progresses, so will your machine learning skills, until you are ready to take on today's hottest topic in the field: Deep Learning.

By the end of this book, you will be ready to take on your own machine learning problems, either by building on the existing source code or developing your own algorithm from scratch!

Style and approach

OpenCV machine learning connects the fundamental theoretical principles behind machine learning to their practical applications in a way that focuses on asking and answering the right questions. This book walks you through the key elements of OpenCV and its powerful machine learning classes, while demonstrating how to get to grips with a range of models.

A long life in a healthy, vigorous, youthful body has always been one of humanity's greatest dreams. Recent progress in genetic manipulations and calorie-restricted diets in laboratory animals hold forth the promise that someday science will enable us to exert total control over our own biological aging.
Nearly all scientists who study the biology of aging agree that we will someday be able to substantially slow down the aging process, extending our productive, youthful lives. Dr. Aubrey de Grey is perhaps the most bullish of all such researchers. As has been reported in media outlets ranging from 60 Minutes to The New York Times, Dr. de Grey believes that the key biomedical technology required to eliminate aging-derived debilitation and death entirely—technology that would not only slow but periodically reverse age-related physiological decay, leaving us biologically young into an indefinite future—is now within reach.

In Ending Aging, Dr. de Grey and his research assistant Michael Rae describe the details of this biotechnology. They explain that the aging of the human body, just like the aging of man-made machines, results from an accumulation of various types of damage. As with man-made machines, this damage can periodically be repaired, leading to indefinite extension of the machine's fully functional lifetime, just as is routinely done with classic cars. We already know what types of damage accumulate in the human body, and we are moving rapidly toward the comprehensive development of technologies to remove that damage. By demystifying aging and its postponement for the nonspecialist reader, de Grey and Rae systematically dismantle the fatalist presumption that aging will forever defeat the efforts of medical science.

Unleash the power of computer vision with Python using OpenCVAbout This BookCreate impressive applications with OpenCV and PythonFamiliarize yourself with advanced machine learning conceptsHarness the power of computer vision with this easy-to-follow guideWho This Book Is For

Intended for novices to the world of OpenCV and computer vision, as well as OpenCV veterans that want to learn about what's new in OpenCV 3, this book is useful as a reference for experts and a training manual for beginners, or for anybody who wants to familiarize themselves with the concepts of object classification and detection in simple and understandable terms. Basic knowledge about Python and programming concepts is required, although the book has an easy learning curve both from a theoretical and coding point of view.

What You Will LearnInstall and familiarize yourself with OpenCV 3's Python APIGrasp the basics of image processing and video analysisIdentify and recognize objects in images and videosDetect and recognize faces using OpenCVTrain and use your own object classifiersLearn about machine learning concepts in a computer vision contextWork with artificial neural networks using OpenCVDevelop your own computer vision real-life applicationIn Detail

OpenCV 3 is a state-of-the-art computer vision library that allows a great variety of image and video processing operations. Some of the more spectacular and futuristic features such as face recognition or object tracking are easily achievable with OpenCV 3. Learning the basic concepts behind computer vision algorithms, models, and OpenCV's API will enable the development of all sorts of real-world applications, including security and surveillance.

Starting with basic image processing operations, the book will take you through to advanced computer vision concepts. Computer vision is a rapidly evolving science whose applications in the real world are exploding, so this book will appeal to computer vision novices as well as experts of the subject wanting to learn the brand new OpenCV 3.0.0. You will build a theoretical foundation of image processing and video analysis, and progress to the concepts of classification through machine learning, acquiring the technical know-how that will allow you to create and use object detectors and classifiers, and even track objects in movies or video camera feeds. Finally, the journey will end in the world of artificial neural networks, along with the development of a hand-written digits recognition application.

Style and approach

This book is a comprehensive guide to the brand new OpenCV 3 with Python to develop real-life computer vision applications.

Build real-world computer vision applications and develop cool demos using OpenCV for PythonAbout This BookLearn how to apply complex visual effects to images using geometric transformations and image filtersExtract features from an image and use them to develop advanced applicationsBuild algorithms to help you understand the image content and perform visual searchesWho This Book Is For

This book is intended for Python developers who are new to OpenCV and want to develop computer vision applications with OpenCV-Python. This book is also useful for generic software developers who want to deploy computer vision applications on the cloud. It would be helpful to have some familiarity with basic mathematical concepts such as vectors, matrices, and so on.

What You Will LearnApply geometric transformations to images, perform image filtering, and convert an image into a cartoon-like imageDetect and track various body parts such as the face, nose, eyes, ears, and mouthStitch multiple images of a scene together to create a panoramic imageMake an object disappear from an imageIdentify different shapes, segment an image, and track an object in a live videoRecognize an object in an image and build a visual search engineReconstruct a 3D map from imagesBuild an augmented reality applicationIn Detail

Computer vision is found everywhere in modern technology. OpenCV for Python enables us to run computer vision algorithms in real time. With the advent of powerful machines, we are getting more processing power to work with. Using this technology, we can seamlessly integrate our computer vision applications into the cloud. Web developers can develop complex applications without having to reinvent the wheel.

This book will walk you through all the building blocks needed to build amazing computer vision applications with ease. We start off with applying geometric transformations to images. We then discuss affine and projective transformations and see how we can use them to apply cool geometric effects to photos. We will then cover techniques used for object recognition, 3D reconstruction, stereo imaging, and other computer vision applications.

This book will also provide clear examples written in Python to build OpenCV applications. The book starts off with simple beginner's level tasks such as basic processing and handling images, image mapping, and detecting images. It also covers popular OpenCV libraries with the help of examples.

The book is a practical tutorial that covers various examples at different levels, teaching you about the different functions of OpenCV and their actual implementation.

Style and approach

This is a conversational-style book filled with hands-on examples that are really easy to understand. Each topic is explained very clearly and is followed by a programmatic implementation so that the concept is solidified. Each topic contributes to something bigger in the following chapters, which helps you understand how to piece things together to build something big and complex.

Master text-taming techniques and build effective text-processing applications with RAbout This BookDevelop all the relevant skills for building text-mining apps with R with this easy-to-follow guideGain in-depth understanding of the text mining process with lucid implementation in the R languageExample-rich guide that lets you gain high-quality information from text dataWho This Book Is For

If you are an R programmer, analyst, or data scientist who wants to gain experience in performing text data mining and analytics with R, then this book is for you. Exposure to working with statistical methods and language processing would be helpful.

What You Will LearnGet acquainted with some of the highly efficient R packages such as OpenNLP and RWeka to perform various steps in the text mining processAccess and manipulate data from different sources such as JSON and HTTPProcess text using regular expressionsGet to know the different approaches of tagging texts, such as POS tagging, to get started with text analysisExplore different dimensionality reduction techniques, such as Principal Component Analysis (PCA), and understand its implementation in RDiscover the underlying themes or topics that are present in an unstructured collection of documents, using common topic models such as Latent Dirichlet Allocation (LDA)Build a baseline sentence completing applicationPerform entity extraction and named entity recognition using RIn Detail

Text Mining (or text data mining or text analytics) is the process of extracting useful and high-quality information from text by devising patterns and trends. R provides an extensive ecosystem to mine text through its many frameworks and packages.

Starting with basic information about the statistics concepts used in text mining, this book will teach you how to access, cleanse, and process text using the R language and will equip you with the tools and the associated knowledge about different tagging, chunking, and entailment approaches and their usage in natural language processing. Moving on, this book will teach you different dimensionality reduction techniques and their implementation in R. Next, we will cover pattern recognition in text data utilizing classification mechanisms, perform entity recognition, and develop an ontology learning framework.

By the end of the book, you will develop a practical application from the concepts learned, and will understand how text mining can be leveraged to analyze the massively available data on social media.

Style and approach

This book takes a hands-on, example-driven approach to the text mining process with lucid implementation in R.

A condensed, easier-to-understand student version of the acclaimed Tietz Textbook of Clinical Chemistry and Molecular Diagnostics, Tietz Fundamentals of Clinical Chemistry and Molecular Diagnostics, 7th Edition uses a laboratory perspective in providing the clinical chemistry fundamentals you need to work in a real-world, clinical lab. Coverage ranges from laboratory principles to analytical techniques and instrumentation, analytes, pathophysiology, and more. New content keeps you current with the latest developments in molecular diagnostics. From highly respected clinical chemistry experts Carl Burtis and David Bruns, this textbook shows how to select and perform diagnostic lab tests, and accurately evaluate results.Authoritative, respected author team consists of two well-known experts in the clinical chemistry world.Coverage of analytical techniques and instrumentation includes optical techniques, electrochemistry, electrophoresis, chromatography, mass spectrometry, enzymology, immunochemical techniques, microchips, automation, and point of care testing.Learning objectives begin each chapter, providing measurable outcomes to achieve after completing the material.Key words are listed and defined at the beginning of each chapter, and bolded in the text. A glossary at the end of the book makes it quick and easy to look up definitions of key terms.More than 500 illustrations plus easy-to-read tables help you understand and remember key concepts. New chapters on molecular diagnostics include the principles of molecular biology, nucleic acid techniques and applications, and genomes and nucleic acid alterations, reflecting the changes in this rapidly evolving field.New content on clinical evaluation of methods, kidney function tests, and diabetes is added to this edition. NEW multiple-choice review questions at the end of each chapter allow you to measure your comprehension of the material.NEW case studies on the Evolve companion website use real-life scenarios to reinforce concepts.
Explore the mathematical computations and algorithms for image processing using popular Python tools and frameworks.Key FeaturesPractical coverage of every image processing task with popular Python librariesIncludes topics such as pseudo-coloring, noise smoothing, computing image descriptorsCovers popular machine learning and deep learning techniques for complex image processing tasksBook Description

Image processing plays an important role in our daily lives with various applications such as in social media (face detection), medical imaging (X-ray, CT-scan), security (fingerprint recognition) to robotics & space. This book will touch the core of image processing, from concepts to code using Python.

The book will start from the classical image processing techniques and explore the evolution of image processing algorithms up to the recent advances in image processing or computer vision with deep learning. We will learn how to use image processing libraries such as PIL, scikit-mage, and scipy ndimage in Python. This book will enable us to write code snippets in Python 3 and quickly implement complex image processing algorithms such as image enhancement, filtering, segmentation, object detection, and classification. We will be able to use machine learning models using the scikit-learn library and later explore deep CNN, such as VGG-19 with Keras, and we will also use an end-to-end deep learning model called YOLO for object detection. We will also cover a few advanced problems, such as image inpainting, gradient blending, variational denoising, seam carving, quilting, and morphing.

By the end of this book, we will have learned to implement various algorithms for efficient image processing.

What you will learnPerform basic data pre-processing tasks such as image denoising and spatial filtering in PythonImplement Fast Fourier Transform (FFT) and Frequency domain filters (e.g., Weiner) in PythonDo morphological image processing and segment images with different algorithmsLearn techniques to extract features from images and match imagesWrite Python code to implement supervised / unsupervised machine learning algorithms for image processingUse deep learning models for image classification, segmentation, object detection and style transferWho this book is for

This book is for Computer Vision Engineers, and machine learning developers who are good with Python programming and want to explore details and complexities of image processing. No prior knowledge of the image processing techniques is expected.

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