%{search_type} search results

• Cover
• Title Page
• Copyright and Credits
• Dedications
• Foreword
• Contributors
• Table of Contents
• Preface
• Part 1: Introduction to Detectron2
• Chapter 1: An Introduction to Detectron2 and Computer Vision Tasks
• Technical requirements
• Computer vision tasks
• Object detection
• Instance segmentation
• Keypoint detection
• Semantic segmentation
• Panoptic segmentation
• An introduction to Detectron2 and its architecture
• Introducing Detectron2
• Detectron2 architecture
• Detectron2 development environments

• Cloud development environment for Detectron2 applications
• Local development environment for Detectron2 applications
• Connecting Google Colab to a local development environment
• Summary
• Chapter 2: Developing Computer Vision Applications Using Existing Detectron2 Models
• Technical requirements
• Introduction to Detectron2's Model Zoo
• Developing an object detection application
• Getting the configuration file
• Getting a predictor
• Performing inferences
• Visualizing the results
• Developing an instance segmentation application
• Selecting a configuration file

• Getting a predictor
• Performing inferences
• Visualizing the results
• Developing a keypoint detection application
• Selecting a configuration file
• Getting a predictor
• Performing inferences
• Visualizing the results
• Developing a panoptic segmentation application
• Selecting a configuration file
• Getting a predictor
• Performing inferences
• Visualizing the results
• Developing a semantic segmentation application
• Selecting a configuration file and getting a predictor
• Performing inferences
• Visualizing the results
• Putting it all together
• Getting a predictor

• Performing inferences
• Visualizing the results
• Performing a computer vision task
• Summary
• Part 2: Developing Custom Object Detection Models
• Chapter 3: Data Preparation for Object Detection Applications
• Technical requirements
• Common data sources
• Getting images
• Selecting an image labeling tool
• Annotation formats
• Labeling the images
• Annotation format conversions
• Converting YOLO datasets to COCO datasets
• Converting Pascal VOC datasets to COCO datasets
• Summary
• Chapter 4: The Architecture of the Object Detection Model in Detectron2

• Technical requirements
• Introduction to the application architecture
• The backbone network
• Region Proposal Network
• The anchor generator
• The RPN head
• The RPN loss calculation
• Proposal predictions
• Region of Interest Heads
• The pooler
• The box predictor
• Summary
• Chapter 5: Training Custom Object Detection Models
• Technical requirements
• Processing data
• The dataset
• Downloading and performing initial explorations
• Data format conversion
• Displaying samples
• Using the default trainer
• Selecting the best model

Computer vision is a crucial component of many modern businesses, including automobiles, robotics, and manufacturing, and its market is growing rapidly. This book helps you explore Detectron2, Facebook's next-gen library providing cutting-edge detection and segmentation algorithms. It's used in research and practical projects at Facebook to support computer vision tasks, and its models can be exported to TorchScript or ONNX for deployment. The book provides you with step-by-step guidance on using existing models in Detectron2 for computer vision tasks (object detection, instance segmentation, key-point detection, semantic detection, and panoptic segmentation). You'll get to grips with the theories and visualizations of Detectron2's architecture and learn how each module in Detectron2 works. As you advance, you'll build your practical skills by working on two real-life projects (preparing data, training models, fine-tuning models, and deployments) for object detection and instance segmentation tasks using Detectron2. Finally, you'll deploy Detectron2 models into production and develop Detectron2 applications for mobile devices. By the end of this deep learning book, you'll have gained sound theoretical knowledge and useful hands-on skills to help you solve advanced computer vision tasks using Detectron2.

• Advanced Decisions in Technical and Medical Applications: An Introduction
• Part I: Technical applications
• Image Representation and Processing Using Autoregressive Random Fields with Multiple Roots of Characteristic Equations
• Representation and Processing of Spatially Heterogeneous Images and Image Sequences
• Matrix Approach to Solution of the Inverse Problems for Multimedia Wireless Communication Links
• Authentication and Copyright Protection of Videos Under Transmitting Specifications
• Object Selection in Computer Vision: From Multi-thresholding to Percolation Based Scene Representation
• Part II: Medical applications
• Vision-Based Assistive Systems for Deaf and Hearing Impaired People
• Methods of Endoscopic Images Enhancement and Analysis in CDSS
• Tissue Germination Evaluation on Implants Based on Shearlet Transform and Color Coding
• Histological Images Segmentation by Convolutional Neural Network with Morphological Post-filtration.

This book applies novel theories to improve algorithms in complex data analysis in various fields, including object detection, remote sensing, data transmission, data fusion, gesture recognition, and medical image processing and analysis. It is intended for Ph.D. students, academics, researchers, and software developers working in the areas of digital video processing and computer vision technologies. .
(source: Nielsen Book Data)

• Dimension reduction.- Classification.- Clustering.- Regression.- Neural Networks.
• (source: Nielsen Book Data)

The book introduces the latest methods and algorithms developed in machine and deep learning (hybrid symbolic-numeric computations, robust statistical techniques for clustering and eliminating data as well as convolutional neural networks) dealing not only with images and the use of computers, but also their applications to visualization tasks generalized by up-to-date points of view. Associated algorithms are deposited on iCloud.
(source: Nielsen Book Data)

• Introduction: Why you need to understand data analytics
• Section 1. Getting started: Keep up with your quants: an innumerate's guide to navigating big data / by Thomas H. Davenport
• A simple exercise to help you think like a data scientist: an easy way to learn the process of data analytics / by Thomas C. Redman
• Section 2. Gather the right information: Do you need all that data?: questions to ask for a focused search / by Ron Ashkenas
• How to ask your data scientists for data and analytics: factors to keep in mind to get the information you need / by Michael Li, Madina Kassengaliyeva, and Raymond Perkins
• How to design a business experiment: tips for using the scientific method / by Oliver Hauser and Michael Luca
• Know the difference between your data and your metrics: understand what you're measuring / by Jeff Bladt and Bob Filbin
• The fundamentals of A/ B testing: how it works and mistakes to avoid / by Amy Gallo
• Can your data be trusted?: gauge whether your data is safe to use / by Thomas C. Redman
• Section 3. Analyze the data: A predictive analytics primer: look to the future by looking at the past / by Thomas H. Davenport
• Understanding regression analysis: evaluate the relationship between variables / by Amy Gallo
• When to act on a correlation, and when not to: assess your confidence in your findings and the risk of being wrong / by David Ritter
• Can machine learning solve your business problem?: steps to take before investing in AI / by Anastassia Fedyk
• A refresher on statistical significance: check if your results are real or just luck / by Amy Gallo
• Linear thinking in a nonlinear world: a common mistake that leads to errors in judgment / by Bart de Langhe, Stefano Puntoni, and Richard Larrick
• Pitfalls of data-driven decisions: the cognitive traps to avoid / by Megan MacGarvie and Kristina McElheran
• Don't let your analytics cheat the truth: always ask for the outliers / by Michael Schrage
• Section 4. Communicate your findings: Data is worthless if you don't communicate it: tell people what it means / by Thomas H. Davenport
• When data visualization works, and when it doesn't: not all data is worth the effort / by Jim Stikeleather
• How to make charts that pop and persuade: questions to help give your numbers meaning / by Nancy Duarte
• Why it's so hard for us to communicate uncertainty: illustrating
• and understanding
• the likelihood of events: an interview with Scott Berinato / by Nicole Torres
• Responding to someone who angrily challenges your data: ensure the data is thorough, then make them an ally / by Jon M. Jachimowicz
• Decisions don't start with data: influence others through story and emotion / by Nick Morgan.

Infrastructure Computer Vision delves into this field of computer science that works on enabling computers to see, identify, process images and provide appropriate output in the same way that human vision does. However, implementing these advanced information and sensing technologies is difficult for many engineers. This book provides civil engineers with the technical detail of this advanced technology and how to apply it to their individual projects.
(source: Nielsen Book Data)

• 1. Introduction
• 2. Build your first OpenVX program
• 3. Using the Graph API to write efficient portable code
• 4. Building an OpenVX graph
• 5. Deploying an OpenVX graph to a target platform
• 6. Basic image transformations
• 7. Background subtraction and object detection
• 8. Computational photography
• 9. Efficient data input/output
• 10. Tracking
• 11. Use OpenVX for deep neural networks
• 12. OpenVX safety critical applications
• 13. Using OpenVX with other vision frameworks
• 14. Making the most of your OpenVX code.
• (source: Nielsen Book Data)

OpenVX is the computer vision API adopted by many high-performance processor vendors. It is quickly becoming the preferred way to write fast and power-efficient code on embedded systems. OpenVX Programming Guidebook presents definitive information on OpenVX 1.2 and 1.3, the Neural Network, and other extensions as well as the OpenVX Safety Critical standard. This book gives a high-level overview of the OpenVX standard, its design principles, and overall structure. It covers computer vision functions and the graph API, providing examples of usage for the majority of the functions. It is intended both for the first-time user of OpenVX and as a reference for experienced OpenVX developers.
(source: Nielsen Book Data)

• 1. Introduction
• 2. Tone and color retargeting
• 3. Color2Gray
• 4. Style retargeting
• 5. Spatial retargeting
• 6. Quality assessment (QA)

• Image Data Image Processing Image Analysis Dense Motion Analysis Image Segmentation Cameras, Coordinates, and Calibration 3D Shape Reconstruction Stereo Matching Feature Detection and Tracking Object Detection.
• (source: Nielsen Book Data)

This textbook provides an accessible general introduction to the essential topics in computer vision. Classroom-tested programming exercises and review questions are also supplied at the end of each chapter. Features: provides an introduction to the basic notation and mathematical concepts for describing an image and the key concepts for mapping an image into an image; explains the topologic and geometric basics for analysing image regions and distributions of image values and discusses identifying patterns in an image; introduces optic flow for representing dense motion and various topics in sparse motion analysis; describes special approaches for image binarization and segmentation of still images or video frames; examines the basic components of a computer vision system; reviews different techniques for vision-based 3D shape reconstruction; includes a discussion of stereo matchers and the phase-congruency model for image features; presents an introduction into classification and learning.
(source: Nielsen Book Data)

• pt. 1. Local binary pattern operators
• pt. 2. Analysis of still images
• pt. 3. Motion analysis
• pt. 4. Face analysis
• pt. 5. LBP in various computer vision applications.

The recent emergence of Local Binary Patterns (LBP) has led to significant progress in applying texture methods to various computer vision problems and applications. The focus of this research has broadened from 2D textures to 3D textures and spatiotemporal (dynamic) textures. Also, where texture was once utilized for applications such as remote sensing, industrial inspection and biomedical image analysis, the introduction of LBP-based approaches have provided outstanding results in problems relating to face and activity analysis, with future scope for face and facial expression recognition, biometrics, visual surveillance and video analysis. Computer Vision Using Local Binary Patterns provides a detailed description of the LBP methods and their variants both in spatial and spatiotemporal domains. This comprehensive reference also provides an excellent overview as to how texture methods can be utilized for solving different kinds of computer vision and image analysis problems. Source codes of the basic LBP algorithms, demonstrations, some databases and a comprehensive LBP bibliography can be found from an accompanying web site. Topics include: - Local binary patterns and their variants in spatial and spatiotemporal domains - Texture classification and segmentation, description of interest regions - Applications in image retrieval and 3D recognition - Recognition and segmentation of dynamic textures - Background subtraction, recognition of actions - Face analysis using still images and image sequences, visual speech recognition - LBP in various applications Written by pioneers of LBP, this book is an essential resource for researchers, professional engineers and graduate students in computer vision, image analysis and pattern recognition. The book will also be of interest to all those who work with specific applications of machine vision.

• 1. Introduction
• 2. Review of mathematical principles
• 3. Writing programs to process images
• 4. Images: description and characterization
• 5. Linear operators and kernels
• 6. Image relaxation: restoration and feature extraction
• 7. Mathematical morphology
• 8. Segmentation
• 9. Shape
• 10. Consistent labeling
• 11. Parametric transform
• 12. Graphs and graph-theoretic concepts
• 13. Image matching
• 14. Statistical pattern recognition
• 15. Clustering
• 16. Syntactic pattern recognition
• 17. Applications
• 18. Automatic target recognition.
• (source: Nielsen Book Data)

This 2004 book is an accessible and comprehensive introduction to machine vision. It provides all the necessary theoretical tools and shows how they are applied in actual image processing and machine vision systems. A key feature is the inclusion of many programming exercises that give insights into the development of practical image processing algorithms. The authors begin with a review of mathematical principles and go on to discuss key issues in image processing such as the description and characterization of images, edge detection, restoration and feature extraction, segmentation, texture and shape. They also discuss image matching, statistical pattern recognition, clustering, and syntactic pattern recognition. Important applications are described, including optical character recognition and automatic target recognition. Software and data used in the book can be found at www.cambridge.org/9780521830461. A useful reference for practitioners, the book is aimed at graduate students in electrical engineering, computer science and mathematics.
(source: Nielsen Book Data)

• Technical Fundamentals
• Introduction to the Algorithmics
• Integrating Vision Toolkit
• Surveillance Technology
• Bar Codes and Matrix Codes
• Workpiece Gauging
• Histogram-based Object Recognition
• Correlation-based Object Recognition
• Scale- and Rotation-Invariant Object Recognition
• Laser Scanning using the Light-Section Method
• Depth Image Acquisition with a Stereo Camera System
• 3D Tracking with a Stereo Camera System
• Installation of IVT, OpenCV and Qt Under Windows and Linux
• Mathematics
• Industrial Image Processing: A Practical Experience Report
• Index.
• (source: Nielsen Book Data)

Computer vision is probably the most exciting branch of image processing, and the number of applications in robotics, automation technology and quality control is constantly increasing. Unfortunately entering this research area is, as yet, not simple. Those who are interested must first go through a lot of books, publications and software libraries. With this book, however, the first step is easy. The theoretically well-founded content is understandable and is supplemented by many practical examples. Source code is provided with the specially developed platform-independent open source library IVT in the programming language C/C++. The use of the IVT is not necessary, but it does make for a much easier entry and allows first developments to be quickly produced.Among other subjects, the following are dealt with in the fundamentals section of the book: Lighting, optics, camera technology, transfer standards, camera calibration, image enhancement, segmentation, filters, correlation and stereo vision. The practical section provides the efficient implementation of the algorithms, followed by many interesting applications such as interior surveillance, bar code scanning, object recognition, 3D scanning, 3D tracking, a stereo camera system and much more.
(source: Nielsen Book Data)

"Learning OpenCV" puts you right in the middle of the rapidly expanding field of computer vision. Written by the creators of "OpenCV", the widely used free open-source 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 the data. Computer vision is everywhere - in security systems, manufacturing inspection systems, medical image analysis, Unmanned Aerial Vehicles, and more. It helps robot cars drive by themselves, stitches Google maps and Google Earth together, checks the pixels on your laptop's LCD screen, and makes sure the stitches in your shirt are OK. OpenCV provides an easy-to-use computer vision infrastructure along with a comprehensive library containing more than 500 functions that can run vision code in real time.With "Learning OpenCV", any developer or hobbyist can get up and running with the framework quickly, whether it's to build simple or sophisticated vision applications. The book includes: a thorough introduction to OpenCV; getting input from cameras; transforming images; shape matching; pattern recognition, including face detection; segmenting images; tracking and motion in 2 and 3 dimensions; and, machine learning algorithms. Hands-on exercises at the end of each chapter help you absorb the concepts, and an appendix explains how to set up an OpenCV project in Visual Studio. OpenCV is written in performance optimized C/C++ code, runs on Windows, Linux, and Mac OS X, and is free for commercial and research use under a BSD license. Getting machines to see is a challenging but entertaining goal. If you're intrigued by the possibilities, "Learning OpenCV" gets you started on building computer vision applications of your own.
(source: Nielsen Book Data)

• Preface
• 1. Computational vision in neural and machine systems Michael Jenkin and Laurence Harris
• Part I. Dynamical Systems: 2. Exploring contrast-controlled adaptation processes in human vision (with help from Buffy the Vampire Slayer) Norma Graham and S. SabinaWolfson
• 3. Image comparison and motion detection by a contrario methods Frederic Cao, Thomas Veit and Patrick Bouthemy
• 4. Computer vision in the Mars Exploration Rover (MER) mission Larry Matthies, Mark Maimone, Yang Cheng, Andrew Johnson and Reg Willson
• 5. Calibration and shape recovery from videos of dynamic scenes Marc Pollefeys, Sudipta Sinha and Jingyu Yan
• 6. Specular planar target surface recovery via coded target stereopsis Arlene Ripsman, Piotr Jasiobedzki and Michael Jenkin
• 7. Neural construction of objects from parts Charles E. Connor
• Part II. Attention, Motion and Eye Movements: 8. Attention and action James J. Clark, Ziad M. Hafed and Li Jie
• 9. Cueing visual search in clutter Preeti Verghese
• 10. Transsaccadic memory of visual features Steven L. Prime, Matthias Niemeier and J. Douglas Crawford
• 11. Modeling what attracts human gaze over dynamic natural scenes Laurent Itti and Pierre Baldi
• Part III. Stereo: 12. Global stereo in polynomial time Carlo Tomasi
• 13. Computational analysis of binocular half occlusions Mikhail Sizintsev and Richard P.Wildes
• 14. Speed versus quality - measuring and optimizing Stereo for Telepresence Jane Mulligan
• 15. Binocular combination: measurements and a model Jian Ding and George Sperling
• Index.
• (source: Nielsen Book Data)

Computational vision deals with the underlying mathematical and computational models for how visual information is processed. Whether the processing is biological or machine, there are fundamental questions related to how the information is processed. How should information be represented? How should information be transduced in order to highlight features of interest while suppressing noise and other artefacts of the image capture process? Computational Vision in Neural and Machine Systems address these and other questions in 13 chapters, divided into three sections, which overlap between biological and computational systems: dynamical systems; attention, motion, and eye-movements; and stereovision. The editors have brought together the best and brightest minds in the field of computational vision, combining research from both biology and computing and enhancing the developing synergy between computational and biological visual modelling communities. Aimed at researchers and graduate students in computational or biological vision, neuroscience, and psychology.
(source: Nielsen Book Data)

• Preface
• 1. Background
• 2. Point visibility
• 3. Weak visibility and shortest paths
• 4. L-R visibility and shortest paths
• 5. Visibility graphs
• 6. Visibility graph theory
• 7. Visibility and link paths
• 8. Visibility and path queries
• Bibliography
• Index.
• (source: Nielsen Book Data)

A human observer can effortlessly identify visible portions of geometric objects present in the environment. However, computations of visible portions of objects from a viewpoint involving thousands of objects is a time consuming task even for high speed computers. To solve such visibility problems, efficient algorithms have been designed. This book presents some of these visibility algorithms in two dimensions. Specifically, basic algorithms for point visibility, weak visibility, shortest paths, visibility graphs, link paths and visibility queries are all discussed. Several geometric properties are also established through lemmas and theorems. With over 300 figures and hundreds of exercises, this book is ideal for graduate students and researchers in the field of computational geometry. It will also be useful as a reference for researchers working in algorithms, robotics, computer graphics and geometric graph theory, and some algorithms from the book can be used in a first course in computational geometry.
(source: Nielsen Book Data)

• Preface. Contributors. 1. Introduction. I. FUNDAMENTALS IN COMPUTER VISION.
• 2. Camera Calibration. Zhengyou Zhang. Introduction. Notation and Problem Statement. Camera Calibration with 3D Objects. Camera Calibration with 2D Objects: Plane-Based Technique. Solving Camera Calibration with 1D Objects. Self-Calibration. Conclusion. Appendix: Estimating Homography Between Plane and Image. Bibliography. 3. Multiple View Geometry.
• Anders Heyden and Marc Pollefeys. Introduction. Projective Geometry. Tensor Calculus. Modeling Cameras. Multiple View Geometry. Structure and Motion I. Structure and Motion II. Autocalibration. Dense Depth Estimation. Visual Modeling. Conclusion. Bibliography. 4. Robust Techniques for Computer Vision.
• Peter Meer. Robustness in Visual Tasks. Models and Estimation Problems. Location Estimation. Robust Regression. Conclusion. Bibliography. 5. The Tensor Voting Framework. Gerard Medioni and Philippos Mordohai. Introduction. Related Work. Tensor Voting in 2D. Tensor Voting in 3D. Tensor Voting in ND. Application to Computer Vision Problems. Conclusion and Future Work. Acknowledgments. Bibliography. II. APPLICATIONS IN COMPUTER VISION. 6. Image-Based Lighting.
• Paul E. Debevec. Basic Image-Based Lighting. Advanced Image-Based Lighting. Image-Based Relighting. Conclusion. Bibliography. 7. Computer Vision In Visual Effects.
• Doug Roble. Introduction. Computer Vision Problems Unique to Film. Feature Tracking. Optical Flow. Camera Tracking and Structure from Motion. The Future. Bibliography. 8. Content-Based Image Retrieval: An Overview. Theo Gevers and Arnold W. M. Smeulders Overview of Chapter. Image Domains. Image Features. Representation and Indexing. Similarity and Search. Interaction and Learning. Conclusion. Bibliography.
• 9. Face Detection, Alignment, and Recognition.
• Stan Z. Li and Juwei Lu. Introduction. Face Detection. Face Alignment. Face Recognition. Bibliography. 10. Perceptual Interfaces. Matthew Turk and Mathias Koelsch Introduction. Perceptual Interfaces and HCI. Multimodal Interfaces. Vision-Based Interfaces. Brain-Computer Interfaces. Summary. Bibliography. III. PROGRAMMING FOR COMPUTER VISION. 11. Open Source Computer Vision Library. Gary Bradski. Overview. Functional Groups: What's Good for What. Pictorial Tour. Programming Examples Using C/C++. Other Interfaces. Appendix A. Appendix B. Bibliography. 12. Software Architecture For Computer Vision. Alexandre R. J. Francois. Introduction. SAI: A Software Architecture Model. MFSM: An Architectural Middleware. Conclusion. Acknowledgments. Bibliography. Index.
• (source: Nielsen Book Data)

The state-of-the art in computer vision: theory, applications, and programming Whether you're a working engineer, developer, researcher, or student, this is your single authoritative source for today's key computer vision innovations. Gerard Medioni and Sing Bing Kang present advances in computer vision such as camera calibration, multi-view geometry, and face detection, and introduce important new topics such as vision for special effects and the tensor voting framework. They begin with the fundamentals, cover select applications in detail, and introduce two popular approaches to computer vision programming. Camera calibration using 3D objects, 2D planes, 1D lines, and self-calibration Extracting camera motion and scene structure from image sequences Robust regression for model fitting using M-estimators, RANSAC, and Hough transforms Image-based lighting for illuminating scenes and objects with real-world light images Content-based image retrieval, covering queries, representation, indexing, search, learning, and more Face detection, alignment, and recognition--with new solutions for key challenges Perceptual interfaces for integrating vision, speech, and haptic modalities Development with the Open Source Computer Vision Library (OpenCV) The new SAI framework and patterns for architecting computer vision applications.
(source: Nielsen Book Data)

• Examples and Modelling of Switched and Impulsive Systems.- Analysis of Switched and Impulsive Systems.- Stability of Linear Switched and Impulsive Systems.- Stability of Nonlinear Switched and Impulsive Systems.- Impulsive Synchronization of Chaotic Systems.- Chaos Based Secure Communication Systems.- Scheduling of Switched Server Systems.- Relative Differentiated Quality of Service of the Internet.- Switched Scalable Video Coding Systems.- Future Research Directions and Potential Applications.
• (source: Nielsen Book Data)

In this volume the important concept of switched and impulsive control is discussed, with a wide field of applications in the analysis and control of complex systems. This monograph provides the reader with a comprehensive coverage of switched and impulsive systems, including new original work with various applications such as switched server systems, scalable video coding systems, chaotic based secure communication, or quality of service on the internet. Switched and Impulsive Systems can be used as a reference or a text for a course at graduate level.
(source: Nielsen Book Data)

• 1. Introduction
• 2. Review of mathematical principles
• 3. Writing programs to process images
• 4. Images: description and characterization
• 5. Linear operators and kernels
• 6. Image relaxation: restoration and feature extraction
• 7. Mathematical morphology
• 8. Segmentation
• 9. Shape
• 10. Consistent labeling
• 11. Parametric transform
• 12. Graphs and graph-theoretic concepts
• 13. Image matching
• 14. Statistical pattern recognition
• 15. Clustering
• 16. Syntactic pattern recognition
• 17. Applications
• 18. Automatic target recognition.
• (source: Nielsen Book Data)

This book is an accessible and comprehensive introduction to machine vision. It provides all the necessary theoretical tools and shows how they are applied in actual image processing and machine vision systems. A key feature is the inclusion of many programming exercises that give insights into the development of practical image processing algorithms. The authors begin with a review of mathematical principles and go on to discuss key issues in image processing such as the description and characterization of images, edge detection, restoration and feature extraction, segmentation, texture and shape. They also discuss image matching, statistical pattern recognition, clustering, and syntactic pattern recognition. Important applications are described, including optical character recognition and automatic target recognition. A CD-ROM containing software and data used in the book is included. The book is aimed at graduate students in electrical engineering, computer science, and mathematics. It will also be a useful reference for practitioners.
(source: Nielsen Book Data)

• "Table of Contents"
• "Preface"
• "Acknowledgments"
• "Chapter 1 Introduction"
• "1.1 Visual perception from 2-D images to 3-D models"
• "1.2 A mathematical approach"
• "1.3 A historical perspective"
• "Part I Introductory Material"
• "Chapter 2 Representation of a Three-Dimensional Moving Scene"
• "2.1 Three-dimensional Euclidean space"
• "2.2 Rigid-body motion"
• "2.3 Rotational motion and its representations"
• "2.4 Rigid-body motion and its representations"
• "2.5 Coordinate and velocity transformations"
• "2.6 Summary"
• "2.7 Exercises"
• "2.A Quaternions and Euler angles for rotations"
• "Chapter 3 Image Formation"
• "3.1 Representation of images"
• "3.2 Lenses, light, and basic photometry"
• "3.3 A geometric model of image formation"
• "3.4 Summary"
• "3.5 Exercises"
• "3.A Basic photometry with light sources and surfaces"
• "3.B Image formation in the language of projective geometry"
• "Chapter 4 Image Primitives and Correspondence"
• "4.1 Correspondence of geometric features"
• "4.2 Local deformation models"
• "4.3 Matching point features"
• "4.4 Tracking line features"
• "4.5 Summary"
• "4.6 Exercises"
• "4.A Computing image gradients"
• "Part II Geometry of Two Views"
• "Chapter 5 Reconstruction from Two Calibrated Views"
• "5.1 Epipolar geometry"
• "5.2 Basic reconstruction algorithms"
• "5.3 Planar scenes and homography"
• "5.4 Continuous motion case"
• "5.5 Summary"
• "5.6 Exercises"
• "5.A Optimization subject to the epipolar constraint"
• "Chapter 6 Reconstruction from Two Uncalibrated Views"
• "6.1 Uncalibrated camera or distorted space?"
• "6.2 Uncalibrated epipolar geometry"
• "6.3 Ambiguities and constraints in image formation"
• "6.4 Strati ed reconstruction"
• "6.5 Calibration with scene knowledge"
• "6.6 Dinner with Kruppa"
• "6.7 Summary"
• "6.8 Exercises".

• "6.A From images to fundamental matrices"
• "6.B Properties of Kruppa's equations"
• "Chapter 7 Estimation of Multiple Motions from Two Views"
• "7.1 Multibody epipolar constraint and the fundamental matrix"
• "7.2 A rank condition for the number of motions"
• "7.3 Geometric properties of the multibody fundamental matrix"
• "7.4 Multibody motion estimation and segmentation"
• "7.5 Multibody structure from motion"
• "7.6 Summary"
• "7.7 Exercises"
• "7.A Homogeneous polynomial factorization"
• "Part III Geometry of Multiple Views"
• "Chapter 8 Multiple-View Geometry of Points and Lines"
• "8.1 Basic notation for the (pre)image and coimage of points and lines"
• "8.2 Preliminary rank conditions of multiple images"
• "8.3 Geometry of point features"
• "8.4 Geometry of line features"
• "8.5 Uncalibrated factorization and strati cation"
• "8.6 Summary"
• "8.7 Exercises"
• "8.A Proof for the properties of bilinear and trilinear constraints"
• "Chapter 9 Extension to General Incidence Relations"
• "9.1 Incidence relations among points, lines, and planes"
• "9.2 Rank conditions for incidence relations"
• "9.3 Universal rank conditions on the multiple-view matrix"
• "9.4 Summary"
• "9.5 Exercises"
• "9.A Incidence relations and rank conditions"
• "9.B Beyond constraints among four views"
• "9.C Examples of geometric interpretation of the rank conditions"
• "Chapter 10 Geometry and Reconstruction from Symmetry"
• "10.1 Symmetry and multiple-view geometry"
• "10.2 Symmetry-based 3-D reconstruction"
• "10.3 Camera calibration from symmetry"
• "10.4 Summary"
• "10.5 Exercises"
• "Part IV Applications"
• "Chapter 11 Step-by-Step Building of a 3-D Model from Images"
• "11.1 Feature selection"
• "11.2 Feature correspondence"
• "11.3 Projective reconstruction"
• "11.4 Upgrade from projective to Euclidean reconstruction".

• "11.5 Visualization"
• "11.6 Additional techniques for image-based modeling"
• "Chapter 12 Visual Feedback"
• "12.1 Structure and motion estimation as a ltering problem"
• "12.2 Application to virtual insertion in live video"
• "12.3 Visual feedback for autonomous car driving"
• "12.4 Visual feedback for autonomous helicopter landing"
• "Part V Appendices"
• "Appendix A Basic Facts from Linear Algebra"
• "A.1 Basic notions associated with a linear space"
• "A.2 Linear transformations and matrix groups"
• "A.3 Gram-Schmidt and the QR decomposition"
• "A.4 Range, null space (kernel), rank and eigenvectors of a matrix"
• "A.5 Symmetric matrices and skew-symmetric matrices"
• "A.6 Lyapunov map and Lyapunov equation"
• "A.7 The singular value decomposition (SVD)"
• "Appendix B Least-Variance Estimation and Filtering"
• "B.1 Least-variance estimators of random vectors"
• "B.2 The Kalman-Bucy lter"
• "B.3 The extended Kalman lter"
• "C Basic Facts from Nonlinear Optimization"
• "C.1 Unconstrained optimization: gradient-based methods"
• "C.2 Constrained optimization: Lagrange multiplier method"
• "References"
• "Glossary of Notation".

This book gives senior undergraduate and beginning graduate students and researchers in computer vision, applied mathematics, computer graphics, and robotics a self-contained introduction to the geometry of 3D vision; that is the reconstruction of 3D models of objects from a collection of 2D images. Following a brief introduction, Part I provides background materials for the rest of the book. The two fundamental transformations, namely rigid body motion and perspective projection are introduced and image formation and feature extraction discussed. Part II covers the classic theory of two view geometry based on the so-called epipolar constraint. Part III shows that a more proper tool for studying the geometry of multiple views is the so- called rank considtion on the multiple view matrix. Part IV develops practical reconstruction algorithms step by step as well as discusses possible extensions of the theory. Exercises are provided at the end of each chapter. Software for examples and algorithms are available on the author's website.

• Acknowledgments*List of Figures*Notation and Symbols*Introduction * I Spline Models*Non-parametric spline models * Parametric spline models * Auto-Associative Models * II Markov Models*Fundamental Aspects * Bayesian estimation * Simulation and optimization * Parameter Estimation *III Modeling in Action* Model-building * Degradation in Imaging * Detection of filamentary Entities * Reconstruction and Projections * Matching * References.
• (source: Nielsen Book Data)

More mathematicians have been taking part in the development of digital image processing as a science and the contributions are reflected in the increasingly important role modeling has played solving complex problems. This book is mostly concerned with energy-based models. Most of these models come from industrial projects in which the author was involved in robot vision and radiography: tracking 3D lines, radiographic image processing, 3D reconstruction and tomography, matching, deformation learning. Numerous graphical illustrations accompany the text.
(source: Nielsen Book Data)

• * Short introduction to calculus of variations * Short introduction to differential geometry * Curve evolution theory and invariant signatures * The Osher-Sethian level-set method * The level-set method: numerical considerations * Mathematical morphology * Distance maps and skeletons * Fast marching methods * 2D and 3D image segmentation * Geometric framework in image processing.
• (source: Nielsen Book Data)

Numerical Geometry of Images examines computational methods and algorithms in image processing. It explores applications like shape from shading, color-image enhancement and segmentation, edge integration, offset curve computation, symmetry axis computation, path planning, minimal geodesic computation, and invariant signature calculation. In addition, it describes and utilizes tools from mathematical morphology, differential geometry, numerical analysis, and calculus of variations. Graduate students, professionals, and researchers with interests in computational geometry, image processing, computer graphics, and algorithms will find this new text / reference an indispensable source of insight of instruction.
(source: Nielsen Book Data)