Introduction To 3d Data

Render three-dimensional data and maps with ease. Written as a self-study workbook, Introduction to 3D Data demystifies the sometimes confusing controls and procedures required for 3D modeling using software packages such as ArcGIS 3D Analyst and Google Earth. Going beyond the manual that comes with the software, this profusely illustrated guide explains how to use ESRI’s ArcGIS 3D Analyst to model and analyze three-dimensional geographical surfaces, create 3D data, and produce displays ranging from topographically realistic maps to 3D scenes and spherical earth-like views. The engagingly user-friendly instruction: • Walks you through basic concepts of 3D data, progressing to more advanced techniques such as calculating surface area and volume • Introduces you to two major software packages: ArcGIS 3D Analyst (including ArcScene and ArcGlobe) and Google Earth • Reinforces your understanding through in-depth discussions with over thirty hands-on exercises and tutorial datasets on the support website at www.wiley/college/kennedy • Helps you apply the theory with real-world applications Whether you’re a student or professional in geology, landscape architecture, transportation system planning, hydrology, or a related field, Introduction to 3D Data will quickly turn you into a power user of 3D GIS.

3D Data Acquisition for Bioarchaeology, Forensic Anthropology, and Archaeology serves as a handbook for the collection and processing of 3-D scanned data and as a tool for scholars interested in pursuing research projects with 3-D models. The book's chapters enhance the reader’s understanding of the technology by covering virtual model processing protocols, alignment methods, actual data acquisition techniques, basic technological protocols, and considerations of variation in research design associated with biological anthropology and archaeology. Thoroughly guides the reader through the “how-to on different stages of 3D-data-related research Provides statistical analysis options for 3D image data Covers protocols, methods and techniques as associated with biological anthropology and archaeology

Gain expertise in advanced deep learning domains such as neural networks, meta-learning, graph neural networks, and memory augmented neural networks using the Python ecosystem Key FeaturesGet to grips with building faster and more robust deep learning architecturesInvestigate and train convolutional neural network (CNN) models with GPU-accelerated libraries such as TensorFlow and PyTorchApply deep neural networks (DNNs) to computer vision problems, NLP, and GANsBook Description In order to build robust deep learning systems, you’ll need to understand everything from how neural networks work to training CNN models. In this book, you’ll discover newly developed deep learning models, methodologies used in the domain, and their implementation based on areas of application. You’ll start by understanding the building blocks and the math behind neural networks, and then move on to CNNs and their advanced applications in computer vision. You'll also learn to apply the most popular CNN architectures in object detection and image segmentation. Further on, you’ll focus on variational autoencoders and GANs. You’ll then use neural networks to extract sophisticated vector representations of words, before going on to cover various types of recurrent networks, such as LSTM and GRU. You’ll even explore the attention mechanism to process sequential data without the help of recurrent neural networks (RNNs). Later, you’ll use graph neural networks for processing structured data, along with covering meta-learning, which allows you to train neural networks with fewer training samples. Finally, you’ll understand how to apply deep learning to autonomous vehicles. By the end of this book, you’ll have mastered key deep learning concepts and the different applications of deep learning models in the real world. What you will learnCover advanced and state-of-the-art neural network architecturesUnderstand the theory and math behind neural networksTrain DNNs and apply them to modern deep learning problemsUse CNNs for object detection and image segmentationImplement generative adversarial networks (GANs) and variational autoencoders to generate new imagesSolve natural language processing (NLP) tasks, such as machine translation, using sequence-to-sequence modelsUnderstand DL techniques, such as meta-learning and graph neural networksWho this book is for This book is for data scientists, deep learning engineers and researchers, and AI developers who want to further their knowledge of deep learning and build innovative and unique deep learning projects. Anyone looking to get to grips with advanced use cases and methodologies adopted in the deep learning domain using real-world examples will also find this book useful. Basic understanding of deep learning concepts and working knowledge of the Python programming language is assumed.

This updated bestseller provides an introduction to programming interactive computer graphics, with an emphasis on game development using DirectX 11. The book is divided into three main parts: basic mathematical tools, fundamental tasks in Direct3D, and techniques and special effects. It includes new Direct3D 11 features such as hardware tessellation, the compute shader, dynamic shader linkage and covers advanced rendering techniques such as screen-space ambient occlusion, level-of-detail handling, cascading shadow maps, volume rendering, and character animation. Includes a companion CD-ROM with code and figures. eBook Customers: Companion files are available for downloading with order number/proof of purchase by writing to the publisher at [email protected].

Computer vision encompasses the construction of integrated vision systems and the application of vision to problems of real-world importance. The process of creating 3D models is still rather difficult, requiring mechanical measurement of the camera positions or manual alignment of partial 3D views of a scene. However using algorithms, it is possible to take a collection of stereo-pair images of a scene and then automatically produce a photo-realistic, geometrically accurate digital 3D model. This book provides a comprehensive introduction to the methods, theories and algorithms of 3D computer vision. Almost every theoretical issue is underpinned with practical implementation or a working algorithm using pseudo-code and complete code written in C++ and MatLab®. There is the additional clarification of an accompanying website with downloadable software, case studies and exercises. Organised in three parts, Cyganek and Siebert give a brief history of vision research, and subsequently: present basic low-level image processing operations for image matching, including a separate chapter on image matching algorithms; explain scale-space vision, as well as space reconstruction and multiview integration; demonstrate a variety of practical applications for 3D surface imaging and analysis; provide concise appendices on topics such as the basics of projective geometry and tensor calculus for image processing, distortion and noise in images plus image warping procedures. An Introduction to 3D Computer Vision Algorithms and Techniques is a valuable reference for practitioners and programmers working in 3D computer vision, image processing and analysis as well as computer visualisation. It would also be of interest to advanced students and researchers in the fields of engineering, computer science, clinical photography, robotics, graphics and mathematics.

This book is aimed at an audience consisting of two kinds of readers. The first is people who are curious about 3D printing and want more information without necessarily getting deeply into it. For this audience, the first two chapters will be of greatest interest. They provide an overview of 3D print technology. They also serve to take the confusion out of the jargon and make sense out of such shortcuts as SLA, FFM, FFF, FDM, DLP, LOM, SLM, DMLS, SLS, EBM, EBAM, CAD and others. They describe the basic processes, the materials used and the application of the technology in industry, space, medicine, housing, clothing and consumer-oriented products such as jewelry, video game figures, footwear, tools and what must now seem like an infinity of bunnies, eagles and busts of Star Wars and Star Trek figurines in a dazzling array of colors. This book also addresses the needs of people new to the field who require information in a hurry. Chapter 3 serves as a guide to generating a 3D model by reviewing scanning methodology, the various types of software available to create a model and the steps needed to insure a useful printed object from the 3D model. The chapter has numerous references which, together with the information in the text, will help one find quickly any additional information available on the internet. Keywords: 3D Printing, 3D Software, 3D Hardware, Printing Materials, Scanning, 3D Modeling, Jewelry, Medicine, Housing, Space

This unique resource details the theory, working methods, and applications of electron tomographic techniques for imaging asymmetric, noncrystalline biological specimens.

Introduction to Data Science: Data Analysis and Prediction Algorithms with R introduces concepts and skills that can help you tackle real-world data analysis challenges. It covers concepts from probability, statistical inference, linear regression, and machine learning. It also helps you develop skills such as R programming, data wrangling, data visualization, predictive algorithm building, file organization with UNIX/Linux shell, version control with Git and GitHub, and reproducible document preparation. This book is a textbook for a first course in data science. No previous knowledge of R is necessary, although some experience with programming may be helpful. The book is divided into six parts: R, data visualization, statistics with R, data wrangling, machine learning, and productivity tools. Each part has several chapters meant to be presented as one lecture. The author uses motivating case studies that realistically mimic a data scientist’s experience. He starts by asking specific questions and answers these through data analysis so concepts are learned as a means to answering the questions. Examples of the case studies included are: US murder rates by state, self-reported student heights, trends in world health and economics, the impact of vaccines on infectious disease rates, the financial crisis of 2007-2008, election forecasting, building a baseball team, image processing of hand-written digits, and movie recommendation systems. The statistical concepts used to answer the case study questions are only briefly introduced, so complementing with a probability and statistics textbook is highly recommended for in-depth understanding of these concepts. If you read and understand the chapters and complete the exercises, you will be prepared to learn the more advanced concepts and skills needed to become an expert.