This thoroughly revised edition of this integrated guide explains and lists readily available graphics software tools and their applications, while also serving as a shortcut to graphics theory and programming. It grounds readers in fundamental concepts and helps them use visualization, modeling, simulation, and virtual reality to complement and improve their work.
• Comprehensive and practical coverage of software graphics tools
• Includes 6 new chapters on OpenGL Programming in Java, Curved Models, Vertex Shading, Pixel Shading and Parallel Processing, Programming in Java3D, OpenGL Shading Language, Direct3D Shader Programming [NEW]
• Updated graphics software tools, with new information and format [NEW]
• Additional descriptions and examples [NEW]
• Provides a uniquely categorized compendium of 293 3D graphics software tools
• Concise listings of platforms and pricing, applications, examples, functions, and related Web resources
• Shortcuts to practical graphics principles and methods
• Contains extensive appendices including the addition of basic mathematics in 3D graphics [NEW]
• Extensive pointers to websites and other proven helpful sources
• Combines theory and OpenGL programming with an easy-to-follow approach
A concise, practical introduction to graphics theory and programming, practitioners as well as advanced students will find this accessible revised text an authoritative and useful catalogue of working software tools and methods.
Professor Jim X. Chen is the Director of the Computer Graphics Laboratory at George Mason University and Editor of the Visualization column, and the Visualization Portal, for the IEEE magazine, Computing in Science and Engineering. In addition he is the author of the successful Foundations of 3D Graphics Programming: Using JOGL and Java3D, also now in its second edition.
OpenGL Programming in Java (NEW)
Curved Models (NEW)
Vertex and Pixel Shading in Cg on Java Platform (NEW)
Programming in Java3D (NEW)
OpenGL Shading Language (GLSL) in C/C++ (NEW)
Direct3D and High Level Shading Language Programming (NEW)
Objects and Models
Transformation and Viewing
Color and Lighting
Blending and Texture Mapping
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
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.
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