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Camera Models and Fundamental Concepts Used in Geometric Computer Vision surveys the image acquisition methods used in computer vision and especially, of the vast number of camera models that have been proposed and investigated over the years, and points out similarities between different models.
This updated edition describes both the mathematical theory behind a modern photorealistic rendering system as well as its practical implementation. Through the ideas and software in this book, designers will learn to design and employ a full-featured rendering system for creating stunning imagery. Includes a companion site complete with source code for the rendering system described in the book, with support for Windows, OS X, and Linux.
This is a working camera that pops up from the pages of a book..The book concisely explains--and actively demonstrates--how a structure as humble as a folded piece of paper can tap into the intrinsic properties of light to produce a photograph.The book includes:- a piece of paper folded into a working 4x5" camera- a lightproof bag- 5 sheets of photo-paper "film"- development instructions (from complete DIY to "outsource it")- a foil-stamped cover- a satisfying demonstration of the connection between design & science / structures & functions
This comprehensive reference provides easy access to relevant information on all aspects of Computer Vision. An A-Z format of over 240 entries offers a diverse range of topics for those seeking entry into any aspect within the broad field of Computer Vision. Over 200 Authors from both industry and academia contributed to this volume. Each entry includes synonyms, a definition and discussion of the topic, and a robust bibliography. Extensive cross-references to other entries support efficient, user-friendly searches for immediate access to relevant information. Entries were peer-reviewed by a distinguished international advisory board, both scientifically and geographically diverse, ensuring balanced coverage. Over 3700 bibliographic references for further reading enable deeper exploration into any of the topics covered. The content of Computer Vision: A Reference Guide is expository and tutorial, making the book a practical resource for students who are considering entering the field, as well as professionals in other fields who need to access this vital information but may not have the time to work their way through an entire text on their topic of interest.
Work on practical computer vision projects covering advanced object detector techniques and modern deep learning and machine learning algorithms Key FeaturesLearn about the new features that help unlock the full potential of OpenCV 4Build face detection applications with a cascade classifier using face landmarksCreate an optical character recognition (OCR) model using deep learning and convolutional neural networksBook Description Mastering OpenCV, now in its third edition, targets computer vision engineers taking their first steps toward mastering OpenCV. Keeping the mathematical formulations to a solid but bare minimum, the book delivers complete projects from ideation to running code, targeting current hot topics in computer vision such as face recognition, landmark detection and pose estimation, and number recognition with deep convolutional networks. You’ll learn from experienced OpenCV experts how to implement computer vision products and projects both in academia and industry in a comfortable package. You’ll get acquainted with API functionality and gain insights into design choices in a complete computer vision project. You’ll also go beyond the basics of computer vision to implement solutions for complex image processing projects. By the end of the book, you will have created various working prototypes with the help of projects in the book and be well versed with the new features of OpenCV4. What you will learnBuild real-world computer vision problems with working OpenCV code samplesUncover best practices in engineering and maintaining OpenCV projectsExplore algorithmic design approaches for complex computer vision tasksWork with OpenCV’s most updated API (v4.0.0) through projectsUnderstand 3D scene reconstruction and Structure from Motion (SfM)Study camera calibration and overlay AR using the ArUco ModuleWho this book is for This book is for those who have a basic knowledge of OpenCV and are competent C++ programmers. You need to have an understanding of some of the more theoretical/mathematical concepts, as we move quite quickly throughout the book.
A basic problem in computer vision is to understand the structure of a real world scene given several images of it. Techniques for solving this problem are taken from projective geometry and photogrammetry. Here, the authors cover the geometric principles and their algebraic representation in terms of camera projection matrices, the fundamental matrix and the trifocal tensor. The theory and methods of computation of these entities are discussed with real examples, as is their use in the reconstruction of scenes from multiple images. The new edition features an extended introduction covering the key ideas in the book (which itself has been updated with additional examples and appendices) and significant new results which have appeared since the first edition. Comprehensive background material is provided, so readers familiar with linear algebra and basic numerical methods can understand the projective geometry and estimation algorithms presented, and implement the algorithms directly from the book.
Did you know you can make your own camera, and create striking images, for next to nothing? Pinhole cameras have existed since the 19th Century, and use a tiny hole instead of a lens to make pictures on film that can be processed in the normal way. The results have amazing qualities, and in Build Your Own Paper Camera, one of the world's leading pinhole photographers shows you how to make your own paper pinhole cameras using the unique camera templates on the disc and step-by-step instructions - you can even learn how to turn your digital SLR into a pinhole camera!
Written as an introduction for undergraduate students, this textbook covers the most important methods in digital image processing. Formal and mathematical aspects are discussed at a fundamental level and various practical examples and exercises supplement the text. The book uses the image processing environment ImageJ, freely distributed by the National Institute of Health. A comprehensive website supports the book, and contains full source code for all examples in the book, a question and answer forum, slides for instructors, etc. Digital Image Processing in Java is the definitive textbook for computer science students studying image processing and digital processing.
"This book provides a working guide to the C++ Open Source Computer Vision Library (OpenCV) version 3.x and gives a general background on the field of computer vision sufficient to help readers use OpenCV effectively."--Preface.
If you want a basic understanding of computer vision’s underlying theory and algorithms, this hands-on introduction is the ideal place to start. You’ll learn techniques for object recognition, 3D reconstruction, stereo imaging, augmented reality, and other computer vision applications as you follow clear examples written in Python. 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 applications Work with image mappings and transforms, such as texture warping and panorama creation Compute 3D reconstructions from several images of the same scene Organize images based on similarity or content, using clustering methods Build efficient image retrieval techniques to search for images based on visual content Use algorithms to classify image content and recognize objects Access the popular OpenCV library through a Python interface