The authors begin by describing what patterns are and how they can help you design object-oriented software. They then go on to systematically name, explain, evaluate, and catalog recurring designs in object-oriented systems. With Design Patterns as your guide, you will learn how these important patterns fit into the software development process, and how you can leverage them to solve your own design problems most efficiently.
Each pattern describes the circumstances in which it is applicable, when it can be applied in view of other design constraints, and the consequences and trade-offs of using the pattern within a larger design. All patterns are compiled from real systems and are based on real-world examples. Each pattern also includes code that demonstrates how it may be implemented in object-oriented programming languages like C++ or Smalltalk.
If you want to find out how to use Python to start answering critical questions of your data, pick up Python Machine Learning – whether you want to get started from scratch or want to extend your data science knowledge, this is an essential and unmissable resource.What You Will LearnExplore how to use different machine learning models to ask different questions of your dataLearn how to build neural networks using Keras and TheanoFind out how to write clean and elegant Python code that will optimize the strength of your algorithmsDiscover how to embed your machine learning model in a web application for increased accessibilityPredict continuous target outcomes using regression analysisUncover hidden patterns and structures in data with clusteringOrganize data using effective pre-processing techniquesGet to grips with sentiment analysis to delve deeper into textual and social media dataIn Detail
Machine learning and predictive analytics are transforming the way businesses and other organizations operate. Being able to understand trends and patterns in complex data is critical to success, becoming one of the key strategies for unlocking growth in a challenging contemporary marketplace. Python can help you deliver key insights into your data – its unique capabilities as a language let you build sophisticated algorithms and statistical models that can reveal new perspectives and answer key questions that are vital for success.
Python Machine Learning gives you access to the world of predictive analytics and demonstrates why Python is one of the world's leading data science languages. If you want to ask better questions of data, or need to improve and extend the capabilities of your machine learning systems, this practical data science book is invaluable. Covering a wide range of powerful Python libraries, including scikit-learn, Theano, and Keras, and featuring guidance and tips on everything from sentiment analysis to neural networks, you'll soon be able to answer some of the most important questions facing you and your organization.Style and approach
Python Machine Learning connects the fundamental theoretical principles behind machine learning to their practical application in a way that focuses you on asking and answering the right questions. It walks you through the key elements of Python and its powerful machine learning libraries, while demonstrating how to get to grips with a range of statistical models.
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.
Programming Computer Vision with Python explains computer vision in broad terms that won’t bog you down in theory. You get complete code samples with explanations on how to reproduce and build upon each example, along with exercises to help you apply what you’ve learned. This book is ideal for students, researchers, and enthusiasts with basic programming and standard mathematical skills.Learn techniques used in robot navigation, medical image analysis, and other computer vision applicationsWork with image mappings and transforms, such as texture warping and panorama creationCompute 3D reconstructions from several images of the same sceneOrganize images based on similarity or content, using clustering methodsBuild efficient image retrieval techniques to search for images based on visual contentUse algorithms to classify image content and recognize objectsAccess the popular OpenCV library through a Python interface
With over 500 functions that span many areas in vision, OpenCV is used for commercial applications such as security, medical imaging, pattern and face recognition, robotics, and factory product inspection. This book gives you a firm grounding in computer vision and OpenCV for building simple or sophisticated vision applications. Hands-on exercises in each chapter help you apply what you’ve learned.
This volume covers the entire library, in its modern C++ implementation, including machine learning tools for computer vision.Learn OpenCV data types, array types, and array operationsCapture and store still and video images with HighGUITransform images to stretch, shrink, warp, remap, and repairExplore pattern recognition, including face detectionTrack objects and motion through the visual fieldReconstruct 3D images from stereo visionDiscover basic and advanced machine learning techniques in OpenCV
It includes Matlab code of the most common methods and algorithms in the book, together with a descriptive summary and solved examples, and including real-life data sets in imaging and audio recognition.
This text is designed for electronic engineering, computer science, computer engineering, biomedical engineering and applied mathematics students taking graduate courses on pattern recognition and machine learning as well as R&D engineers and university researchers in image and signal processing/analyisis, and computer vision.Matlab code and descriptive summary of the most common methods and algorithms in Theodoridis/Koutroumbas, Pattern Recognition, Fourth EditionSolved examples in Matlab, including real-life data sets in imaging and audio recognitionAvailable separately or at a special package price with the main text (ISBN for package: 978-0-12-374491-3)
3D Printing with Delta Printers contains detailed descriptions of the innovative delta design including unique hardware, software, and maintenance requirements. The book also covers tips for building your own delta printer as well as examples of common enhancements.
This book will enable you to build, configure, and enhance your delta printer. The topics covered will reveal the often-mysterious nuances of the delta design that will enable your printer to compete with the best of what your 3D printer friends can build.
This book is for Java developers with basic Java programming knowledge. No previous knowledge of neural networks is required as this book covers the concepts from scratch.What You Will LearnGet to grips with the basics of neural networks and what they are used forDevelop neural networks using hands-on examplesExplore and code the most widely-used learning algorithms to make your neural network learn from most types of dataDiscover the power of neural network's unsupervised learning process to extract the intrinsic knowledge hidden behind the dataApply the code generated in practical examples, including weather forecasting and pattern recognitionUnderstand how to make the best choice of learning parameters to ensure you have a more effective applicationSelect and split data sets into training, test, and validation, and explore validation strategiesDiscover how to improve and optimize your neural networkIn Detail
Vast quantities of data are produced every second. In this context, neural networks become a powerful technique to extract useful knowledge from large amounts of raw, seemingly unrelated data. One of the most preferred languages for neural network programming is Java as it is easier to write code using it, and most of the most popular neural network packages around already exist for Java. This makes it a versatile programming language for neural networks.
This book gives you a complete walkthrough of the process of developing basic to advanced practical examples based on neural networks with Java.
You will first learn the basics of neural networks and their process of learning. We then focus on what Perceptrons are and their features. Next, you will implement self-organizing maps using the concepts you've learned. Furthermore, you will learn about some of the applications that are presented in this book such as weather forecasting, disease diagnosis, customer profiling, and characters recognition (OCR). Finally, you will learn methods to optimize and adapt neural networks in real time.
All the examples generated in the book are provided in the form of illustrative source code, which merges object-oriented programming (OOP) concepts and neural network features to enhance your learning experience.Style and approach
This book adopts a step-by-step approach to neural network development and provides many hands-on examples using Java programming. Each neural network concept is explored through real-world problems and is delivered in an easy-to-comprehend manner.
This book introduces key game production concepts in an artist-friendly way, and rapidly teaches the basic scripting skills you'll need with Unity. It goes on to show how you, as an independent game artist, can create interactive games, ideal in scope for today's casual and mobile markets, while also giving you a firm foundation in game logic and design.
The first part of the book explains the logic involved in game interaction, and soon has you creating game assets through simple examples that you can build upon and gradually expand. In the second part, you'll build the foundations of a point-and-click style first-person adventure game—including reusable state management scripts, dialogue trees for character interaction, load/save functionality, a robust inventory system, and a bonus feature: a dynamically configured maze and mini-map. With the help of the provided 2D and 3D content, you'll learn to evaluate and deal with challenges in bite-sized pieces as the project progresses, gaining valuable problem-solving skills in interactive design. By the end of the book, you will be able to actively use the Unity 3D game engine, having learned the necessary workflows to utilize your own assets. You will also have an assortment of reusable scripts and art assets with which to build future games.
· Thoroughly developed to include many more worked examples to give greater understanding of the various methods and techniques
· Many more diagrams included--now in two color--to provide greater insight through visual presentation
· Matlab code of the most common methods are given at the end of each chapter.
· More Matlab code is available, together with an accompanying manual, via this site
· Latest hot topics included to further the reference value of the text including non-linear dimensionality reduction techniques, relevance feedback, semi-supervised learning, spectral clustering, combining clustering algorithms.
· An accompanying book with Matlab code of the most common methods and algorithms in the book, together with a descriptive summary, and solved examples including real-life data sets in imaging, and audio recognition. The companion book will be available separately or at a special packaged price (ISBN: 9780123744869).Thoroughly developed to include many more worked examples to give greater understanding of the various methods and techniques Many more diagrams included--now in two color--to provide greater insight through visual presentation Matlab code of the most common methods are given at the end of each chapter An accompanying book with Matlab code of the most common methods and algorithms in the book, together with a descriptive summary and solved examples, and including real-life data sets in imaging and audio recognition. The companion book is available separately or at a special packaged price (Book ISBN: 9780123744869. Package ISBN: 9780123744913) Latest hot topics included to further the reference value of the text including non-linear dimensionality reduction techniques, relevance feedback, semi-supervised learning, spectral clustering, combining clustering algorithms Solutions manual, powerpoint slides, and additional resources are available to faculty using the text for their course. Register at www.textbooks.elsevier.com and search on "Theodoridis" to access resources for instructor.
Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how.
By using concrete examples, minimal theory, and two production-ready Python frameworks—scikit-learn and TensorFlow—author Aurélien Géron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. You’ll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks. With exercises in each chapter to help you apply what you’ve learned, all you need is programming experience to get started.Explore the machine learning landscape, particularly neural netsUse scikit-learn to track an example machine-learning project end-to-endExplore several training models, including support vector machines, decision trees, random forests, and ensemble methodsUse the TensorFlow library to build and train neural netsDive into neural net architectures, including convolutional nets, recurrent nets, and deep reinforcement learningLearn techniques for training and scaling deep neural netsApply practical code examples without acquiring excessive machine learning theory or algorithm details
Peter Christen’s book is divided into three parts: Part I, “Overview”, introduces the subject by presenting several sample applications and their special challenges, as well as a general overview of a generic data matching process. Part II, “Steps of the Data Matching Process”, then details its main steps like pre-processing, indexing, field and record comparison, classification, and quality evaluation. Lastly, part III, “Further Topics”, deals with specific aspects like privacy, real-time matching, or matching unstructured data. Finally, it briefly describes the main features of many research and open source systems available today.By providing the reader with a broad range of data matching concepts and techniques and touching on all aspects of the data matching process, this book helps researchers as well as students specializing in data quality or data matching aspects to familiarize themselves with recent research advances and to identify open research challenges in the area of data matching. To this end, each chapter of the book includes a final section that provides pointers to further background and research material. Practitioners will better understand the current state of the art in data matching as well as the internal workings and limitations of current systems. Especially, they will learn that it is often not feasible to simply implement an existing off-the-shelf data matching system without substantial adaption and customization. Such practical considerations are discussed for each of the major steps in the data matching process.
New features of the 2nd Edition:Contains more than 1000 new terms, notably an increased focus on image processing and machine vision terms; Includes the addition of reference links across the majority of terms pointing readers to further information about the concept under discussion so that they can continue to expand their understanding; Now available as an eBook with enhanced content: approximately 50 videos to further illustrate specific terms; active cross-linking between terms so that readers can easily navigate from one related term to another and build up a full picture of the topic in question; and hyperlinked references to fully embed the text in the current literature.
The book offers a rich blend of theory and practice. It is suitable for students, researchers and practitioners interested in Web mining and data mining both as a learning text and as a reference book. Professors can readily use it for classes on data mining, Web mining, and text mining. Additional teaching materials such as lecture slides, datasets, and implemented algorithms are available online.
If you want to do computational photography and computer vision on Apple's mobile devices, then this book is for you. No previous experience with app development or OpenCV is required. However, basic knowledge of C++ or Objective-C is recommended.What You Will LearnUse Xcode and Interface Builder to develop iOS appsObtain OpenCV's standard modules and build extra modules from sourceControl all the parameters of the iOS device's cameraCapture, save, and share photos and videosAnalyze colors, shapes, and textures in ordinary and specialized photographsBlend and compare images to create special photographic effects and augmented reality toolsDetect faces and morph facial featuresClassify coins and other objectsIn Detail
iOS Application Development with OpenCV 3 enables you to turn your smartphone camera into an advanced tool for photography and computer vision. Using the highly optimized OpenCV library, you will process high-resolution images in real time. You will locate and classify objects, and create models of their geometry. As you develop photo and augmented reality apps, you will gain a general understanding of iOS frameworks and developer tools, plus a deeper understanding of the camera and image APIs.
After completing the book's four projects, you will be a well-rounded iOS developer with valuable experience in OpenCV.Style and approach
The book is practical, creative, and precise. It shows you the steps to create and customize five projects that solve important problems for beginners in mobile app development and computer vision. Complete source code and numerous visual aids are included in each chapter. Experimentation is an important part of the book. You will use computer vision to explore the real world, and then you will refine the projects based on your findings.
Darwin Information Typing Architecture (DITA) is today’s most powerful toolbox for constructing information. By implementing DITA, organizations can gain more value from their technical documentation than ever before. Now, three DITA pioneers offer the first complete roadmap for successful DITA adoption, implementation, and usage.
Drawing on years of experience helping large organizations adopt DITA, the authors answer crucial questions the “official” DITA documents ignore, including: Where do you start? What should you know up front? What are the pitfalls in implementing DITA? How can you avoid those pitfalls?
The authors begin with topic-based writing, presenting proven best practices for developing effective topics and short descriptions. Next, they address content architecture, including how best to set up and implement DITA maps, linking strategies, metadata, conditional processing, and content reuse. Finally, they offer “in the trenches” solutions for ensuring quality implementations, including guidance on content conversion.
Coverage includes:Knowing how and when to use each DITA element–and when not to Writing “minimalist,” task-oriented information that quickly meets users’ needs Creating effective task, concept, and reference topics for any product, technology, or service Writing effective short descriptions that work well in all contexts Structuring DITA maps to bind topics together and provide superior navigation Using links to create information webs that improve retrievability and navigation Gaining benefits from metadata without getting lost in complexity Using conditional processing to eliminate redundancy and rework Systematically promoting reuse to improve quality and reduce costs Planning, resourcing, and executing effective content conversion Improving quality by editing DITA content and XML markup¿
If you’re a writer, editor, information architect, manager, or consultant who evaluates, deploys, or uses DITA, this book will guide you all the way to success.
Also see the other books in this IBM Press series:Developing Quality Technical Information: A Handbook for Writers and Editors The IBM Style Guide: Conventions for Writers and Editors
The book covers the full range of the state of the art of NPR with every chapter authored by internationally renowned experts in the field, covering both classical and contemporary techniques. It will enable both graduate students in computer graphics, computer vision or image processing and professional developers alike to quickly become familiar with contemporary techniques, enabling them to apply 2D NPR algorithms in their own projects.
The author presents the first extended treatment of MM algorithms, which are ideal for high-dimensional optimization problems in data mining, imaging, and genomics; derives numerous algorithms from a broad diversity of application areas, with a particular emphasis on statistics, biology, and data mining; and summarizes a large amount of literature that has not reached book form before.÷
What is one-shot color imaging? Typically, astronomical cooled-chip CCD cameras record only one color at a time - rather like old-fashioned black & white cameras fitted with color filters. Three images are taken in sequence - in red, blue, and green light - and these are then merged by software in a PC to form a color image. Each of the three images must be taken separately through a suitable color filter, which means that the total exposure time for every object is more than tripled. When exposure times can run into tens of minutes or even hours for each of the three colors, this can be a major drawback for the time-pressed amateur.
"One-Shot Color Astronomical Imaging" describes the most cost-effective and time-efficient way for any amateur astronomer to begin to photograph the deep-sky.
The potential of consumer depth cameras extends well beyond entertainment and gaming, to real-world commercial applications such virtual fitting rooms, training for athletes, and assistance for the elderly. This authoritative text/reference reviews the scope and impact of this rapidly growing field, describing the most promising Kinect-based research activities, discussing significant current challenges, and showcasing exciting applications.
Topics and features: presents contributions from an international selection of preeminent authorities in their fields, from both academic and corporate research; addresses the classic problem of multi-view geometry of how to correlate images from different viewpoints to simultaneously estimate camera poses and world points; examines human pose estimation using video-rate depth images for gaming, motion capture, 3D human body scans, and hand pose recognition for sign language parsing; provides a review of approaches to various recognition problems, including category and instance learning of objects, and human activity recognition; with a Foreword by Dr. Jamie Shotton of Microsoft Research, Cambridge, UK.
This broad-ranging overview is a must-read for researchers and graduate students of computer vision and robotics wishing to learn more about the state of the art of this increasingly “hot” topic.
source code listings in C**actual case studies in a wide range of applications, including radar signal detection, stock market prediction, musical composition, ship pattern recognition, and biopotential waveform classification**CASE tools for neural networks and hybrid expert system/neural networks**practical hints and suggestions on when and how to use neural network tools to solve real-world problems.
This book focuses on the core skill set you need to feel at home with Flash CS4, and also introduces you to some of the biggest names in today's Flash community through interviews and actual "How To" examples, so you can learn from the masters. You will start by studying the Flash CS4 interface, and while you're at it, you'll be guided toward mastery of the fundamentals, such as movie clips, text, and graphics, which will lead you into some of the more fascinating aspects of Flash, including audio, video, animation, and 3D transformations.
By the time you finish, you will have created an MP3 player and a Flash video player, been introduced to the basics of ActionScript 3.0, learned how to combine Flash with XML, styled Flash text with CSS, created animated scenes, and worked your way through a host of additional projects. All of these exercises are designed to give you the knowledge necessary to master Flash CS4 from the ground up. If you're already a seasoned Flash designer, this book will get you up to speed with the fourth version in relatively short order.
This book covers all of the new Flash CS4 features, such as the new animation and 3D tools, the new Adobe Media Encoder, and a pair of the coolest new additions to the tools panel: a spray brush tool and a deco tool.
You can discover more about this book, download source code, and more at the book's companion website: www.foundationflashcs4.com.
Printing in Plastic is aimed at creative people comfortable using power tools such as a table saw, circular saw, and drill press. Authors James Kelly and Patrick Hood-Daniel lead you through building a personal fabrication machine based upon a set of blueprints downloaded from their website. Example projects get you started in designing and fabricating your own parts. Bring your handyman skills, and apply patience during the build process. You too can be the proud owner of a personal fabricator—a three-dimensional printer.
Leads you through building a personal fabrication machine capable of creating small parts and objects from plastic Provides example projects to get you started on the road to designing and fabricating your own parts Provides an excellent parent/child, or small group project
Designed for undergraduate and graduate students in computer science and electrical engineering, "Introduction to Biometrics" is also suitable for researchers and biometric and computer security professionals.
Rapidly evolving computer and communications technologies have achieved data transmission rates and data storage capacities high enough for digital video. But video involves much more than just pushing bits! Achieving the best possible image quality, accurate color, and smooth motion requires understanding many aspects of image acquisition, coding, processing, and display that are outside the usual realm of computer graphics. At the same time, video system designers are facing new demands to interface with film and computer system that require techniques outside conventional video engineering.
Charles Poynton's 1996 book A Technical Introduction to Digital Video became an industry favorite for its succinct, accurate, and accessible treatment of standard definition television (SDTV). In Digital Video and HDTV, Poynton augments that book with coverage of high definition television (HDTV) and compression systems.
For more information on HDTV Retail markets, go to: http://www.insightmedia.info/newsletters.php#hdtv
With the help of hundreds of high quality technical illustrations, this book presents the following topics:
* Basic concepts of digitization, sampling, quantization, gamma, and filtering
* Principles of color science as applied to image capture and display
* Scanning and coding of SDTV and HDTV
* Video color coding: luma, chroma (4:2:2 component video, 4fSC composite video)
* Analog NTSC and PAL
* Studio systems and interfaces
* Compression technology, including M-JPEG and MPEG-2
* Broadcast standards and consumer video equipment
This book describes existing and advanced methods to reduce the dimensionality of numerical databases. For each method, the description starts from intuitive ideas, develops the necessary mathematical details, and ends by outlining the algorithmic implementation. Methods are compared with each other with the help of different illustrative examples.
The purpose of the book is to summarize clear facts and ideas about well-known methods as well as recent developments in the topic of nonlinear dimensionality reduction. With this goal in mind, methods are all described from a unifying point of view, in order to highlight their respective strengths and shortcomings.
The book is primarily intended for statisticians, computer scientists and data analysts. It is also accessible to other practitioners having a basic background in statistics and/or computational learning, like psychologists (in psychometry) and economists.
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.
Perfect for hobbyists, makers, artists, and gamers, Making Things See shows you how to build every project with inexpensive off-the-shelf components, including the open source Processing programming language and the Arduino microcontroller. You’ll learn basic skills that will enable you to pursue your own creative applications with Kinect.Create Kinect applications on Mac OS X, Windows, or Linux Track people with pose detection and skeletonization, and use blob tracking to detect objects Analyze and manipulate point clouds Make models for design and fabrication, using 3D scanning technology Use MakerBot, RepRap, or Shapeways to print 3D objects Delve into motion tracking for animation and games Build a simple robot arm that can imitate your arm movements Discover how skilled artists have used Kinect to build fascinating projects