156 Best Image Processing Books of All Time
We've ranked the best image processing books using expert recommendations, sales data, and millions of reader ratings. At Shortform, we know books. Our book guides are the best in the world. Learn why.
1The leader in the field for more than twenty years, this introduction to basic concepts and methodologies for digital image processing continues its cutting-edge focus on contemporary developments in all mainstream areas of image processing. Completely self-contained, heavily illustrated, and mathematically accessible, it has a scope of application that is not limited to the solution of specialized problems. Digital Image Fundamentals. Image Enhancement in the Spatial Domain. Image Enhancement in the Frequency Domain. Image Restoration. Color Image Processing. Wavelets and Multiresolution Processing. Image Compression. Morphological Image Processing. Image Segmentation. Representation and Description. Object Recognition.
2Principles of Digital Image Processing: Core Algorithms
This easy-to-follow textbook is the third of 3 volumes which provide a modern, algorithmic introduction to digital image processing, designed to be used both by learners desiring a firm foundation on which to build, and practitioners in search of critical analysis and modern implementations of the most important techniques.
The reader-friendly text builds on the contents of the previous volumes, presenting additional advanced methods, including those for performing dynamic point operations, non-linear edge-preserving filters and filters for color images, advanced edge detection operators, local feature extraction, curve fitting, etc. This volume continues to provide readers with proven concepts, offering a selection of advanced standard techniques and recent methods that are widely used in research and practical applications.
This reader-friendly text will equip undergraduates with a deeper understanding of the topic as well as being valuable for further developing knowledge for self-study.
The reader-friendly text builds on the contents of the previous volumes, presenting additional advanced methods, including those for performing dynamic point operations, non-linear edge-preserving filters and filters for color images, advanced edge detection operators, local feature extraction, curve fitting, etc. This volume continues to provide readers with proven concepts, offering a selection of advanced standard techniques and recent methods that are widely used in research and practical applications.
This reader-friendly text will equip undergraduates with a deeper understanding of the topic as well as being valuable for further developing knowledge for self-study.
3Deep Learning with Python
Deep learning is applicable to a widening range of artificial intelligence problems, such as image classification, speech recognition, text classification, question answering, text-to-speech, and optical character recognition. It is the technology behind photo tagging systems at Facebook and Google, self-driving cars, speech recognition systems on your smartphone, and much more.
In particular, Deep learning excels at solving machine perception problems: understanding the content of image data, video data, or sound data. Here's a simple example: say you have a large collection of images, and that you want tags associated with each image, for example, "dog," "cat," etc. Deep learning can allow you to create a system that understands how to map such tags to images, learning only from examples. This system can then be applied to new images, automating the task of photo tagging. A deep learning model only has to be fed examples of a task to start generating useful results on new data.
In particular, Deep learning excels at solving machine perception problems: understanding the content of image data, video data, or sound data. Here's a simple example: say you have a large collection of images, and that you want tags associated with each image, for example, "dog," "cat," etc. Deep learning can allow you to create a system that understands how to map such tags to images, learning only from examples. This system can then be applied to new images, automating the task of photo tagging. A deep learning model only has to be fed examples of a task to start generating useful results on new data.
4Principles of Digital Image Processing: Advanced Methods
This easy-to-follow textbook is the third of three volumes which provide a modern, algorithmic introduction to digital image processing, designed to be used both by learners desiring a firm foundation on which to build, and practitioners in search of critical analysis and concrete implementations of the most important techniques. This volume builds upon the introductory material presented in the first two volumes (Fundamental Techniques and Core Algorithms) with additional key concepts and methods in image processing.
Features and topics: practical examples and carefully constructed chapter-ending exercises drawn from the authors' years of experience teaching this material; real implementations, concise mathematical notation, and precise algorithmic descriptions designed for programmers and practitioners; easily adaptable Java code and completely worked-out examples for easy inclusion in existing (and rapid prototyping of new) applications; uses ImageJ, the image processing system developed, maintained, and freely distributed by the U.S. National Institutes of Health (NIH); provides a supplementary website with the complete Java source code, test images, and corrections www.imagingbook.com; additional presentation tools for instructors including a complete set of figures, tables, and mathematical elements.
This thorough, reader-friendly text will equip undergraduates with a deeper understanding of the topic and will be invaluable for further developing knowledge via self-study.
"
Features and topics: practical examples and carefully constructed chapter-ending exercises drawn from the authors' years of experience teaching this material; real implementations, concise mathematical notation, and precise algorithmic descriptions designed for programmers and practitioners; easily adaptable Java code and completely worked-out examples for easy inclusion in existing (and rapid prototyping of new) applications; uses ImageJ, the image processing system developed, maintained, and freely distributed by the U.S. National Institutes of Health (NIH); provides a supplementary website with the complete Java source code, test images, and corrections www.imagingbook.com; additional presentation tools for instructors including a complete set of figures, tables, and mathematical elements.
This thorough, reader-friendly text will equip undergraduates with a deeper understanding of the topic and will be invaluable for further developing knowledge via self-study.
"
5Learning Processing: A Beginner's Guide to Programming Images, Animation, and Interaction
Learning Processing, Second Edition, is a friendly start-up guide to Processing, a free, open-source alternative to expensive software and daunting programming languages. Requiring no previous experience, this book is for the true programming beginner. It teaches the basic building blocks of programming needed to create cutting-edge graphics applications including interactive art, live video processing, and data visualization. Step-by-step examples, thorough explanations, hands-on exercises, and sample code, supports your learning curve.
A unique lab-style manual, the book gives graphic and web designers, artists, and illustrators of all stripes a jumpstart on working with the Processing programming environment by providing instruction on the basic principles of the language, followed by careful explanations of select advanced techniques. The book has been developed with a supportive learning experience at its core. From algorithms and data mining to rendering and debugging, it teaches object-oriented programming from the ground up within the fascinating context of interactive visual media.
This book is ideal for graphic designers and visual artists without programming background who want to learn programming. It will also appeal to students taking college and graduate courses in interactive media or visual computing, and for self-study.
A unique lab-style manual, the book gives graphic and web designers, artists, and illustrators of all stripes a jumpstart on working with the Processing programming environment by providing instruction on the basic principles of the language, followed by careful explanations of select advanced techniques. The book has been developed with a supportive learning experience at its core. From algorithms and data mining to rendering and debugging, it teaches object-oriented programming from the ground up within the fascinating context of interactive visual media.
This book is ideal for graphic designers and visual artists without programming background who want to learn programming. It will also appeal to students taking college and graduate courses in interactive media or visual computing, and for self-study.
6Adobe Photoshop CC Classroom in a Book (2018 Release)
Creative professionals seeking the fastest, easiest, most comprehensive way to learn Adobe Photoshop choose Adobe Photoshop CC Classroom in a Book (2018 release) from Adobe Press. The 15 project-based lessons show key step-by-step techniques for working in Photoshop, including how to correct, enhance, and distort digital images, create image composites, and prepare images for print and the web. In addition to learning the essential elements of the Photoshop interface, this revised edition for the 2018 release covers features like search capabilities, Content-Aware Crop, Select and Mask, Face-Aware Liquify, designing with multiple artboards, creating and organizing enhanced brush presets, and much more! The online companion files include all the necessary assets for readers to complete the projects featured in each chapter as well as ebook updates when Adobe releases relevant new features for Creative Cloud customers. All buyers of the book get full access to the Web Edition: a Web-based version of the complete ebook enhanced with video and multiple-choice quizzes.
7Modern Deep Learning and Advanced Computer Vision: A Perspective Approach
Computer vision has enormous progress in modern times. Deep learning has driven and inferred a range of computer vision problems, such as object detection and recognition, face detection and recognition, motion tracking and estimation, transfer learning, action recognition, image segmentation, semantic segmentation, robotic vision. The chapters in this book are persuaded towards the applications of advanced computer vision using modern deep learning techniques. The authors trust in making the readers with more interesting illustrations in understanding the concepts of deep learning and computer vision at a simpler perspective approach.
8The Deep-sky Imaging Primer, Second Edition
9Practical Deep Learning for Cloud, Mobile, and Edge: Real-World AI & Computer-Vision Projects Using Python, Keras & Tensorflow
Whether you're a software engineer aspiring to enter the world of deep learning, a veteran data scientist, or a hobbyist with a simple dream of making the next viral AI app, you might have wondered where to begin. This step-by-step guide teaches you how to build practical deep learning applications for the cloud, mobile, browsers, and edge devices using a hands-on approach.
Relying on years of industry experience transforming deep learning research into award-winning applications, Anirudh Koul, Siddha Ganju, and Meher Kasam guide you through the process of converting an idea into something that people in the real world can use.
Train, tune, and deploy computer vision models with Keras, TensorFlow, Core ML, and TensorFlow Lite
Develop AI for a range of devices including Raspberry Pi, Jetson Nano, and Google Coral
Explore fun projects, from Silicon Valley's Not Hotdog app to 40+ industry case studies
Simulate an autonomous car in a video game environment and build a miniature version with reinforcement learning
Use transfer learning to train models in minutes
Discover 50+ practical tips for maximizing model accuracy and speed, debugging, and scaling to millions of users
Relying on years of industry experience transforming deep learning research into award-winning applications, Anirudh Koul, Siddha Ganju, and Meher Kasam guide you through the process of converting an idea into something that people in the real world can use.
Train, tune, and deploy computer vision models with Keras, TensorFlow, Core ML, and TensorFlow Lite
Develop AI for a range of devices including Raspberry Pi, Jetson Nano, and Google Coral
Explore fun projects, from Silicon Valley's Not Hotdog app to 40+ industry case studies
Simulate an autonomous car in a video game environment and build a miniature version with reinforcement learning
Use transfer learning to train models in minutes
Discover 50+ practical tips for maximizing model accuracy and speed, debugging, and scaling to millions of users
10Tinyml: Machine Learning with Tensorflow Lite on Arduino and Ultra-Low-Power Microcontrollers
Neural networks are getting smaller. Much smaller. The OK Google team, for example, has run machine learning models that are just 14 kilobytes in size--small enough to work on the digital signal processor in an Android phone. With this practical book, you'll learn about TensorFlow Lite for Microcontrollers, a miniscule machine learning library that allows you to run machine learning algorithms on tiny hardware.
Authors Pete Warden and Daniel Situnayake explain how you can train models that are small enough to fit into any environment, including small embedded devices that can run for a year or more on a single coin cell battery. Ideal for software and hardware developers who want to build embedded devices using machine learning, this guide shows you how to create a TinyML project step-by-step. No machine learning or microcontroller experience is necessary.
Learn practical machine learning applications on embedded devices, including simple uses such as speech recognition and gesture detection
Train models such as speech, accelerometer, and image recognition, you can deploy on Arduino and other embedded platforms
Understand how to work with Arduino and ultralow-power microcontrollers
Use techniques for optimizing latency, energy usage, and model and binary size
Authors Pete Warden and Daniel Situnayake explain how you can train models that are small enough to fit into any environment, including small embedded devices that can run for a year or more on a single coin cell battery. Ideal for software and hardware developers who want to build embedded devices using machine learning, this guide shows you how to create a TinyML project step-by-step. No machine learning or microcontroller experience is necessary.
Learn practical machine learning applications on embedded devices, including simple uses such as speech recognition and gesture detection
Train models such as speech, accelerometer, and image recognition, you can deploy on Arduino and other embedded platforms
Understand how to work with Arduino and ultralow-power microcontrollers
Use techniques for optimizing latency, energy usage, and model and binary size
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11Digital Image Processing Using MATLAB
12Advanced Editing for Real Estate Photography: Professional techniques for processing high-quality images
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13Adobe Photoshop Classroom in a Book (2020 Release)
Creative professionals seeking the fastest, easiest, most comprehensive way to learn Adobe Photoshop choose Adobe Photoshop CC Classroom in a Book (2017 release) from Adobe Press. The 15 project-based lessons in this book show users step-by-step the key techniques for working in Photoshop and how to correct, enhance, and distort digital images, create image composites, and prepare images for print and the web. In addition to learning the key elements of the Photoshop interface, this completely revised CC (2017 release) edition covers features like new and improved search capabilities, Content-Aware Crop, Select and Mask, Face-Aware Liquify, designing with multiple artboards, and much more! The online companion files include all the necessary assets for readers to complete the projects featured in each chapter as well as ebook updates when Adobe releases new features for Creative Cloud customers. All buyers of the book get full access to the Web Edition: a Web-based version of the complete ebook enhanced with video and interactive multiple-choice quizzes. As always with the Classroom in a Book, Instructor Notes are available for teachers to download."
14The Image Processing Handbook
Consistently rated as the best overall introduction to computer-based image processing, The Image Processing Handbook covers two-dimensional (2D) and three-dimensional (3D) imaging techniques, image printing and storage methods, image processing algorithms, image and feature measurement, quantitative image measurement analysis, and more.
Incorporating image processing and analysis examples at all scales, from nano- to astro-, this Seventh Edition:
Features a greater range of computationally intensive algorithms than previous versions Provides better organization, more quantitative results, and new material on recent developments Includes completely rewritten chapters on 3D imaging and a thoroughly revamped chapter on statistical analysis Contains more than 1700 references to theory, methods, and applications in a wide variety of disciplines Presents 500+ entirely new figures and images, with more than two-thirds appearing in color
The Image Processing Handbook, Seventh Edition delivers an accessible and up-to-date treatment of image processing, offering broad coverage and comparison of algorithms, approaches, and outcomes.
Incorporating image processing and analysis examples at all scales, from nano- to astro-, this Seventh Edition:
Features a greater range of computationally intensive algorithms than previous versions Provides better organization, more quantitative results, and new material on recent developments Includes completely rewritten chapters on 3D imaging and a thoroughly revamped chapter on statistical analysis Contains more than 1700 references to theory, methods, and applications in a wide variety of disciplines Presents 500+ entirely new figures and images, with more than two-thirds appearing in color
The Image Processing Handbook, Seventh Edition delivers an accessible and up-to-date treatment of image processing, offering broad coverage and comparison of algorithms, approaches, and outcomes.
15OpenCV for Secret Agents
16Algorithms for Image Processing and Computer Vision
Programmers and software engineers are always in need of newer techniques and algorithms to manipulate and interpret images, whether they are working with MRI data, computer animation or satellite images. During the last several years, advances in the computer hardware and software have lead to algorithms and programming languages that allow for relatively sophisticated image processing among non-mathematicians. This book is an accessible cookbook of algorithms for some of today's most wanted image processing applications including morphing, Optical Character Recognition (OCR), and Symbol Recognition, that will save graphics programmers from many hours of lengthy mathematical solutions. Includes CD-ROM with... • Complete code for examples in the book • A gallery of images illustrating the results of advanced techniques • Free GNU C compiler-- allows readers to run source code regardless of platform.
17Fundamentals of Digital Image Processing: A Practical Approach with Examples in Matlab
This is an introductory to intermediate level text on the science of image processing, which employs the Matlab programming language to illustrate some of the elementary, key concepts in modern image processing and pattern recognition. The approach taken is essentially practical and the book offers a framework within which the concepts can be understood by a series of well chosen examples, exercises and computer experiments, drawing on specific examples from within science, medicine and engineering. Clearly divided into eleven distinct chapters, the book begins with a fast-start introduction to image processing to enhance the accessibility of later topics. Subsequent chapters offer increasingly advanced discussion of topics involving more challenging concepts, with the final chapter looking at the application of automated image classification (with Matlab examples) .
Matlab is frequently used in the book as a tool for demonstrations, conducting experiments and for solving problems, as it is both ideally suited to this role and is widely available. Prior experience of Matlab is not required and those without access to Matlab can still benefit from the independent presentation of topics and numerous examples.
Features a companion website www.wiley.com/go/solomon/fundamentals containing a Matlab fast-start primer, further exercises, examples, instructor resources and accessibility to all files corresponding to the examples and exercises within the book itself. Includes numerous examples, graded exercises and computer experiments to support both students and instructors alike.
Matlab is frequently used in the book as a tool for demonstrations, conducting experiments and for solving problems, as it is both ideally suited to this role and is widely available. Prior experience of Matlab is not required and those without access to Matlab can still benefit from the independent presentation of topics and numerous examples.
Features a companion website www.wiley.com/go/solomon/fundamentals containing a Matlab fast-start primer, further exercises, examples, instructor resources and accessibility to all files corresponding to the examples and exercises within the book itself. Includes numerous examples, graded exercises and computer experiments to support both students and instructors alike.
18Digital Image Processing: An Algorithmic Introduction Using Java
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.
19The Pocket Handbook of Image Processing Algorithms
This handy desktop reference gathers together into one easy-to-use volume the most popular image processing algorithms. Designed to be used at the computer terminal, it features an illustrated, annotated dictionary format - with clear, concise definitions, examples, and C program code. Covers algorithms for adaptive filters, coding and compression, color image processing, histogram operations, image fundamentals, mensuration, morphological filters, nonlinear filters, segmentation, spatial filters, spatial frequency filters, storage formats, and transforms. Includes graphic oriented techniques such as warping, morphing, zooming, and dithering. Provides algorithms for image noise generation. MARKETS: For users and developers of image processing systems and programs.
20Computer Vision with OpenCV 3 and Qt5: Build visually appealing, multithreaded, cross-platform computer vision applications
Developers have been using OpenCV library to develop computer vision applications for a long time. However, they now need a more effective tool to get the job done and in a much better and modern way. Qt is one of the major frameworks available for this task at the moment. This book will teach you to develop applications with the combination ...
21Image Processing
This comprehensive analysis provides methods for calculating the statistical properties of images and reducing images to a series of discrete values, as well as applications in the fields of fractal representation, mathematical morphology, wavelet representation, and contour-and-shape detection are shown.
22Lessons from the Masters: Current Concepts in Astronomical Image Processing
There are currently thousands of amateur astronomers around the world engaged in astrophotography at a sophisticated level. Their ranks far outnumber professional astronomers doing the same and their contributions both technically and artistically are the dominant drivers of progress in the field today. This book is a unique collaboration of individuals world-renowned in their particular area and covers in detail each of the major sub-disciplines of astrophotography. This approach offers the reader the greatest opportunity to learn the most current information and the latest techniques directly from the foremost innovators in the field today.
"Lessons from the Masters" includes a brilliant body of recognized leaders in astronomical imaging, assembled by Robert Gendler, who delivers the most current, sophisticated and useful information on digital enhancement techniques in astrophotography available today. Each chapter focuses on a particular technique, but the book as a whole covers all types of astronomical image processing, including processing of events such as eclipses, using DSLRs, and deep-sky, planetary, widefield, and high resolution astronomical image processing. Recognized contributors include deep-sky experts such as Jay GaBany, Tony Hallas, and Ken Crawford, high-resolution planetary expert Damian Peach, and the founder of TWAN (The World at Night) Babak A. Tafreshi.
A large number of illustrations (150, 75 in color) present the challenges and accomplishments involved in the processing of astronomical images by enthusiasts.
"Lessons from the Masters" includes a brilliant body of recognized leaders in astronomical imaging, assembled by Robert Gendler, who delivers the most current, sophisticated and useful information on digital enhancement techniques in astrophotography available today. Each chapter focuses on a particular technique, but the book as a whole covers all types of astronomical image processing, including processing of events such as eclipses, using DSLRs, and deep-sky, planetary, widefield, and high resolution astronomical image processing. Recognized contributors include deep-sky experts such as Jay GaBany, Tony Hallas, and Ken Crawford, high-resolution planetary expert Damian Peach, and the founder of TWAN (The World at Night) Babak A. Tafreshi.
A large number of illustrations (150, 75 in color) present the challenges and accomplishments involved in the processing of astronomical images by enthusiasts.
23Fundamentals of Digital Image Processing
Presents a thorough overview of the major topics of digital image processing, beginning with the basic mathematical tools needed for the subject. Includes a comprehensive chapter on stochastic models for digital image processing. Covers aspects of image representation including luminance, color, spatial and temporal properties of vision, and digitization. Explores various image processing techniques. Discusses algorithm development (software/firmware) for image transforms, enhancement, reconstruction, and image coding.
24Architectural Photography: Composition, Capture, and Digital Image Processing
Architectural photography is more than simply choosing a subject and pressing the shutter-release button; it s more than just documenting a project. An architectural photograph shows the form and appeal of a building far better than any other medium. With the advent of the digital photographic workflow, architects, real estate firms, and interior designers are discovering exciting new opportunities to present and market their work.
But what are the ingredients for a successful architectural photograph? What equipment do you need? How can you improve your images in the digital darkroom? Why does a building look different in reality than it does in a photograph? In this book you will find the answers to these questions and much more.
Author Adrian Schulz an architect and photographer by training uses real-world projects to teach you how to: Capture outstanding images of buildings, inside and out Choose the right equipment and use it effectively Compose architectural shots Work with ambient and artificial light Process images in an efficient workflow based on Adobe Photoshop This book is a step-by-step guide to architectural photography for both the aspiring amateur photographer interested in architectural photography and the professional photographer who wants to expand his skills in this domain. Moreover, architects themselves will find this book motivating and inspiring. This third edition has been extensively revised and includes 90 new images and illustrations. Updates include information on topics such as: Photographic technology, including digital cameras, lens quality and construction, and large format cameras Shooting techniques The real life of a professional architectural photographer Traveling Analog to digital shooting Stadium photography Image Processing, including screenshots from the latest image-processing software such as Adobe Photoshop CC With this book, you ll learn a variety of creative tips, tricks, and guidelines for making the perfect architectural image."
But what are the ingredients for a successful architectural photograph? What equipment do you need? How can you improve your images in the digital darkroom? Why does a building look different in reality than it does in a photograph? In this book you will find the answers to these questions and much more.
Author Adrian Schulz an architect and photographer by training uses real-world projects to teach you how to: Capture outstanding images of buildings, inside and out Choose the right equipment and use it effectively Compose architectural shots Work with ambient and artificial light Process images in an efficient workflow based on Adobe Photoshop This book is a step-by-step guide to architectural photography for both the aspiring amateur photographer interested in architectural photography and the professional photographer who wants to expand his skills in this domain. Moreover, architects themselves will find this book motivating and inspiring. This third edition has been extensively revised and includes 90 new images and illustrations. Updates include information on topics such as: Photographic technology, including digital cameras, lens quality and construction, and large format cameras Shooting techniques The real life of a professional architectural photographer Traveling Analog to digital shooting Stadium photography Image Processing, including screenshots from the latest image-processing software such as Adobe Photoshop CC With this book, you ll learn a variety of creative tips, tricks, and guidelines for making the perfect architectural image."
25Modern Algorithms for Image Processing: Computer Imagery by Example Using C#
Utilize modern methods for digital image processing and take advantage of the many time-saving templates provided for all of the projects in this book.
Modern Algorithms for Image Processing approaches the topic of image processing through teaching by example. Throughout the book, you will create projects that resolve typical problems that you might encounter in the world of digital image processing. Some projects teach you methods for addressing the quality of images, such as reducing random errors or noise and suppressing pulse noise (salt and pepper), a method valuable for improving the quality of historical images. Other methods detail how to correct inhomogeneous illumination, not by means of subtracting the mean illumination, but through division, a far more efficient method. Additional projects cover contrasting, and a process for edge detection, more efficient than Canny's, for detecting edges in color images directly, without converting them into black and white images.
What You'll Learn
Apply innovative methods for suppressing pulse noise, enhancing contrast, and edge detection
Know the pros and cons of enlisting a particular method
Use new approaches for image compression and recognizing circles in photos
Utilize a valuable method for straightening photos of paintings taken at an oblique angle, a critical concept to understand when using flash at a right angle
Understand the problem statement of polygonal approximation of boundaries or edges and its solution
Use a new method for detecting bicycles in traffic
Access complete source code examples in C# for all of the projects
Who This Book Is For
C# developers who work with digital image processing or are interested in informatics. The reader should have programming experience and access to an integrated development environment (IDE), ideally .NET.
This book does not prove or disprove theorems, but suggests methods for learning valuable concepts that will enable you to customize your own image processing projects.
Modern Algorithms for Image Processing approaches the topic of image processing through teaching by example. Throughout the book, you will create projects that resolve typical problems that you might encounter in the world of digital image processing. Some projects teach you methods for addressing the quality of images, such as reducing random errors or noise and suppressing pulse noise (salt and pepper), a method valuable for improving the quality of historical images. Other methods detail how to correct inhomogeneous illumination, not by means of subtracting the mean illumination, but through division, a far more efficient method. Additional projects cover contrasting, and a process for edge detection, more efficient than Canny's, for detecting edges in color images directly, without converting them into black and white images.
What You'll Learn
Apply innovative methods for suppressing pulse noise, enhancing contrast, and edge detection
Know the pros and cons of enlisting a particular method
Use new approaches for image compression and recognizing circles in photos
Utilize a valuable method for straightening photos of paintings taken at an oblique angle, a critical concept to understand when using flash at a right angle
Understand the problem statement of polygonal approximation of boundaries or edges and its solution
Use a new method for detecting bicycles in traffic
Access complete source code examples in C# for all of the projects
Who This Book Is For
C# developers who work with digital image processing or are interested in informatics. The reader should have programming experience and access to an integrated development environment (IDE), ideally .NET.
This book does not prove or disprove theorems, but suggests methods for learning valuable concepts that will enable you to customize your own image processing projects.
26Principles and Practice of Radiation Therapy
Learn everything you need to know about radiation therapy with the only comprehensive text written for radiation therapy students by radiation therapists. Principles and Practice of Radiation Therapy is designed to help you understand cancer management, improve clinical techniques for delivering doses of radiation, and apply complex concepts to treatment planning and delivery. This edition features enhanced learning tools and thoroughly updated content, including three new chapters to inform you of increasingly important technologies and practices. The up-to-date and authoritative coverage of this text make it a resource you'll want to consult throughout your radiation therapy courses and beyond.
Complete coverage of radiation therapy provides all introductory content plus the full scope of information on physics, simulation, and treatment planning.
This popular and well-reviewed text continues to be regarded by many radiation therapy professionals as a strong, comprehensive, and authoritative source.
Contributions from a broad range of practitioners bring you the expertise of radiation therapists, physicians, nurses, administrators, and educators who are part of cancer management teams.
Chapters on image guided radiation therapy, intensity modulated radiation therapy, and CT simulation keep you up-to-date with emerging technologies.
Objectives, bulleted summaries, and spotlights join an already impressive list of pedagogical features including chapter outlines, key terms, review questions with answers, critical thinking questions, and a complete, updated glossary.
Color inserts show significant procedures and imaging technologies clearly.
Complete coverage of radiation therapy provides all introductory content plus the full scope of information on physics, simulation, and treatment planning.
This popular and well-reviewed text continues to be regarded by many radiation therapy professionals as a strong, comprehensive, and authoritative source.
Contributions from a broad range of practitioners bring you the expertise of radiation therapists, physicians, nurses, administrators, and educators who are part of cancer management teams.
Chapters on image guided radiation therapy, intensity modulated radiation therapy, and CT simulation keep you up-to-date with emerging technologies.
Objectives, bulleted summaries, and spotlights join an already impressive list of pedagogical features including chapter outlines, key terms, review questions with answers, critical thinking questions, and a complete, updated glossary.
Color inserts show significant procedures and imaging technologies clearly.
27Introduction to Digital Image Processing with MATLAB
Is an introduction to digital image processing from an elementary perspective. The book covers topics that can be introduced with simple mathematics so students can learn the concepts without getting overwhelmed by mathematical detail.
28Principles of Digital Image Processing: Fundamental Techniques (Undergraduate Topics in Computer Science)
29Digital Image Processing And Analysis: Human And Computer Vision Applications With Cvi Ptools, Second Edition
30Learning OpenCV 3: Computer Vision in C++ with the OpenCV Library
Get started in the rapidly expanding field of computer vision with this practical guide. Written by Adrian Kaehler and Gary Bradski, creator of the open source OpenCV library, this book provides a thorough introduction for developers, academics, roboticists, and hobbyists. You'll learn what it takes to build applications that enable computers to "see" and make decisions based on that data.
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 operations
Capture and store still and video images with HighGUI
Transform images to stretch, shrink, warp, remap, and repair
Explore pattern recognition, including face detection
Track objects and motion through the visual field
Reconstruct 3D images from stereo vision
Discover basic and advanced machine learning techniques in OpenCV
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 operations
Capture and store still and video images with HighGUI
Transform images to stretch, shrink, warp, remap, and repair
Explore pattern recognition, including face detection
Track objects and motion through the visual field
Reconstruct 3D images from stereo vision
Discover basic and advanced machine learning techniques in OpenCV
31Digital Video Compression (with CD-ROM)
32Programming Computer Vision with Python: Tools and algorithms for analyzing images
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
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
33Standard Codecs: Image Compression to Advanced Video Coding
A substantially updated edition of Video Coding: An Introduction to Standard Codecs (IEE, 1999, winner of IEE Rayleigh Award), this book discusses the growth of digital television technology and the revolution in image and video compression (such as JPEG2000, broadcast TV, video phone). It highlights the need for standardisation in processing static and moving images and their exchange between computer systems. ITU and ISO/IEC standards are now widely accepted in the picture/video coding field. The book gives an authoritative explanation of picture and video coding algorithms, working from basic principles through to the advanced video compression systems now being developed. It discusses the reasons behind the introduction of a standard codec for a specific application and its chosen parameter. This book will enable readers to appreciate the fundamentals needed to design a video codec for any given application and should prove to be a valuable resource for engineers working in this field.
34Digital Image Processing using MATLAB: ZERO to HERO Practical Approach with Source Code
35Geometric Partial Differential Equations and Image Analysis
This book provides an introduction to the use of geometric partial differential equations in image processing and computer vision. It brings a number of new concepts into the field, providing a very fundamental and formal approach to image processing. State-of-the-art practical results in a large number of real problems are achieved with the techniques described. Applications covered include image segmentation, shape analysis, image enhancement, and tracking. The volume provides information for people investigating new solutions to image processing problems as well as for people searching for existent advanced solutions.
36Feature Extraction and Image Processing for Computer Vision
Feature Extraction and Image Processing for Computer Vision is an essential guide to the implementation of image processing and computer vision techniques, with tutorial introductions and sample code in Matlab. Algorithms are presented and fully explained to enable complete understanding of the methods and techniques demonstrated. As one reviewer noted, The main strength of the proposed book is the exemplar code of the algorithms. Fully updated with the latest developments in feature extraction, including expanded tutorials and new techniques, this new edition contains extensive new material on Haar wavelets, Viola-Jones, bilateral filtering, SURF, PCA-SIFT, moving object detection and tracking, development of symmetry operators, LBP texture analysis, Adaboost, and a new appendix on color models. Coverage of distance measures, feature detectors, wavelets, level sets and texture tutorials has been extended.
37Machine Learning for OpenCV: Intelligent image processing with Python
38Mastering OpenCV 4 with Python: A practical guide covering topics from image processing, augmented reality to deep learning with OpenCV 4 and Python 3.7
Quick start guide to learning the fundamentals of computer vision and image processing using Python and OpenCV.
39Opencv with Python Blueprints
Design and develop advanced computer vision projects using OpenCV with PythonAbout This BookProgram advanced computer vision applications in Python using different features of the OpenCV libraryPractical end-to-end project covering an important computer vision problemAll projects in the book include a step-by-step guide to create computer vision applicationsWho This Book Is For
This book is for intermediate users of OpenCV who aim to master their skills by developing advanced practical applications. Readers are expected to be familiar with OpenCV's concepts and Python libraries. Basic knowledge of Python programming is expected and assumed.What You Will LearnGenerate real-time visual effects using different filters and image manipulation techniques such as dodging and burningRecognize hand gestures in real time and perform hand-shape analysis based on the output of a Microsoft Kinect sensorLearn feature extraction and feature matching for tracking arbitrary objects of interestReconstruct a 3D real-world scene from 2D camera motion and common camera reprojection techniquesTrack visually salient objects by searching for and focusing on important regions of an imageDetect faces using a cascade classifier and recognize emotional expressions in human faces using multi-layer peceptrons (MLPs)Recognize street signs using a multi-class adaptation of support vector machines (SVMs)Strengthen your OpenCV2 skills and learn how to use new OpenCV3 featuresIn Detail
OpenCV is a native cross platform C++ Library for computer vision, machine learning, and image processing. It is increasingly being adopted in Python for development. OpenCV has C++/C, Python, and Java interfaces with support for Windows, Linux, Mac, iOS, and Android. Developers using OpenCV build applications to process visual data; this can include live streaming data from a device like a camera, such as photographs or videos. OpenCV offers extensive libraries with over 500 functions
This book demonstrates how to develop a series of intermediate to advanced projects using OpenCV and Python, rather than teaching the core concepts of OpenCV in theoretical lessons. Instead, the working projects developed in this book teach the reader how to apply their theoretical knowledge to topics such as image manipulation, augmented reality, object tracking, 3D scene reconstruction, statistical learning, and object categorization.
By the end of this book, readers will be OpenCV experts whose newly gained experience allows them to develop their own advanced computer vision applications.Style and approach
This book covers independent hands-on projects that teach important computer vision concepts like image processing and machine learning for OpenCV with multiple examples.
This book is for intermediate users of OpenCV who aim to master their skills by developing advanced practical applications. Readers are expected to be familiar with OpenCV's concepts and Python libraries. Basic knowledge of Python programming is expected and assumed.What You Will LearnGenerate real-time visual effects using different filters and image manipulation techniques such as dodging and burningRecognize hand gestures in real time and perform hand-shape analysis based on the output of a Microsoft Kinect sensorLearn feature extraction and feature matching for tracking arbitrary objects of interestReconstruct a 3D real-world scene from 2D camera motion and common camera reprojection techniquesTrack visually salient objects by searching for and focusing on important regions of an imageDetect faces using a cascade classifier and recognize emotional expressions in human faces using multi-layer peceptrons (MLPs)Recognize street signs using a multi-class adaptation of support vector machines (SVMs)Strengthen your OpenCV2 skills and learn how to use new OpenCV3 featuresIn Detail
OpenCV is a native cross platform C++ Library for computer vision, machine learning, and image processing. It is increasingly being adopted in Python for development. OpenCV has C++/C, Python, and Java interfaces with support for Windows, Linux, Mac, iOS, and Android. Developers using OpenCV build applications to process visual data; this can include live streaming data from a device like a camera, such as photographs or videos. OpenCV offers extensive libraries with over 500 functions
This book demonstrates how to develop a series of intermediate to advanced projects using OpenCV and Python, rather than teaching the core concepts of OpenCV in theoretical lessons. Instead, the working projects developed in this book teach the reader how to apply their theoretical knowledge to topics such as image manipulation, augmented reality, object tracking, 3D scene reconstruction, statistical learning, and object categorization.
By the end of this book, readers will be OpenCV experts whose newly gained experience allows them to develop their own advanced computer vision applications.Style and approach
This book covers independent hands-on projects that teach important computer vision concepts like image processing and machine learning for OpenCV with multiple examples.
40Concise Computer Vision: An Introduction Into Theory and Algorithms
Many textbooks on computer vision can be unwieldy and intimidating in their coverage of this extensive discipline. This textbook addresses the need for a concise overview of the fundamentals of this field.
"Concise Computer Vision" provides an accessible general introduction to the essential topics in computer vision, highlighting the role of important algorithms and mathematical concepts. Classroom-tested programming exercises and review questions are also supplied at the end of each chapter.
Topics and 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 such topics in sparse motion analysis as keypoint detection and descriptor definition, and feature tracking using the Kalman filter; describes special approaches for image binarization and segmentation of still images or video frames; examines the three basic components of a computer vision system, namely camera geometry and photometry, coordinate systems, and camera calibration; reviews different techniques for vision-based 3D shape reconstruction, including the use of structured lighting, stereo vision, and shading-based shape understanding; includes a discussion of stereo matchers, and the phase-congruency model for image features; presents an introduction into classification and learning, with a detailed description of basic AdaBoost and the use of random forests.
This concise and easy to read textbook/reference is ideal for an introductory course at third- or fourth-year level in an undergraduate computer science or engineering programme.
"Concise Computer Vision" provides an accessible general introduction to the essential topics in computer vision, highlighting the role of important algorithms and mathematical concepts. Classroom-tested programming exercises and review questions are also supplied at the end of each chapter.
Topics and 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 such topics in sparse motion analysis as keypoint detection and descriptor definition, and feature tracking using the Kalman filter; describes special approaches for image binarization and segmentation of still images or video frames; examines the three basic components of a computer vision system, namely camera geometry and photometry, coordinate systems, and camera calibration; reviews different techniques for vision-based 3D shape reconstruction, including the use of structured lighting, stereo vision, and shading-based shape understanding; includes a discussion of stereo matchers, and the phase-congruency model for image features; presents an introduction into classification and learning, with a detailed description of basic AdaBoost and the use of random forests.
This concise and easy to read textbook/reference is ideal for an introductory course at third- or fourth-year level in an undergraduate computer science or engineering programme.
41Sparse and Redundant Representations: From Theory to Applications in Signal and Image Processing
A long long time ago, echoing philosophical and aesthetic principles that existed since antiquity, William of Ockham enounced the principle of parsimony, better known today as Ockham's razor: "Entities should not be multiplied without neces sity. " This principle enabled scientists to select the "best" physical laws and theories to explain the workings of the Universe and continued to guide scienti?c research, leadingtobeautifulresultsliketheminimaldescriptionlength approachtostatistical inference and the related Kolmogorov complexity approach to pattern recognition. However, notions of complexity and description length are subjective concepts anddependonthelanguage"spoken"whenpresentingideasandresults. The?eldof sparse representations, that recently underwent a Big Bang like expansion, explic itly deals with the Yin Yang interplay between the parsimony of descriptions and the "language" or "dictionary" used in them, and it became an extremely exciting area of investigation. It already yielded a rich crop of mathematically pleasing, deep and beautiful results that quickly translated into a wealth of practical engineering applications. You are holding in your hands the ?rst guide book to Sparseland, and I am sure you'll ?nd in it both familiar and new landscapes to see and admire, as well as ex cellent pointers that will help you ?nd further valuable treasures. Enjoy the journey to Sparseland! Haifa, Israel, December 2009 Alfred M. Bruckstein vii Preface This book was originally written to serve as the material for an advanced one semester (fourteen 2 hour lectures) graduate course for engineering students at the Technion, Israel.
42Digital Image Processing: A Practical Introduction Using Java (With CD-ROM)
43Image Processing with Imagej
44Introduction to Video and Image Processing: Building Real Systems and Applications
In order to build successful video processing systems, it is important to consider not only traditional image processing methods, but also techniques which account for temporal aspects like object motion and changes in lighting.
This Introduction to Video and Image Processing presents the fundamental concepts and methods in an unique, easy-to-read style which ensures the material is accessible to a wide audience. Exploring more than just the basics of image processing, the text provides a specific focus on the practical design and implementation of real systems for processing video data.
Topics and features: includes more than 100 exercises, as well as C-code snippets of the key algorithms; equips the reader with a solid foundation for understanding and working with images and video, including basic methods for image manipulation and image analysis; covers topics on image acquisition, color images, point processing, neighborhood processing, morphology, BLOB analysis, segmentation in video, tracking, geometric transformation, and visual effects; requires only a minimal understanding of mathematics, with the necessary basics covered in an appendix; presents two chapters dedicated to applications, in which the different methods are applied in real systems, supported by further application examples at an associated website; concludes each chapter with pointers to additional concepts, methods and details not covered in the book; provides a guide to defining suitable values for parameters in video and image processing systems, and to conversion between the RGB color representation and the HIS, HSV and YUV/YCbCr color representations.
This concise and easy-to-follow textbook/reference is an ideal introduction to the subject for both first-year engineering students and for non-engineering students without a strong mathematical background. Suitable for self-study, professionals outside of the field will also find the work an excellent overview of the basics.
This Introduction to Video and Image Processing presents the fundamental concepts and methods in an unique, easy-to-read style which ensures the material is accessible to a wide audience. Exploring more than just the basics of image processing, the text provides a specific focus on the practical design and implementation of real systems for processing video data.
Topics and features: includes more than 100 exercises, as well as C-code snippets of the key algorithms; equips the reader with a solid foundation for understanding and working with images and video, including basic methods for image manipulation and image analysis; covers topics on image acquisition, color images, point processing, neighborhood processing, morphology, BLOB analysis, segmentation in video, tracking, geometric transformation, and visual effects; requires only a minimal understanding of mathematics, with the necessary basics covered in an appendix; presents two chapters dedicated to applications, in which the different methods are applied in real systems, supported by further application examples at an associated website; concludes each chapter with pointers to additional concepts, methods and details not covered in the book; provides a guide to defining suitable values for parameters in video and image processing systems, and to conversion between the RGB color representation and the HIS, HSV and YUV/YCbCr color representations.
This concise and easy-to-follow textbook/reference is an ideal introduction to the subject for both first-year engineering students and for non-engineering students without a strong mathematical background. Suitable for self-study, professionals outside of the field will also find the work an excellent overview of the basics.
45Hands-On GPU-Accelerated Computer Vision with OpenCV and CUDA: Effective techniques for processing complex image data in real time using GPUs
46Adobe Photoshop Lightroom Classic CC Classroom in a Book (2018 Release)
Adobe Photoshop Lightroom Classic CC Classroom in a Book(R) New ways to assemble collections and more search filters make it easier than ever to organize your growing library and find the photos you need. Streamline your editing workflow using intelligent tools trained with thousands of professionally corrected images, and make more selective local adjustments with sophisticated color and tone range masking. Work on your photos on any device; then, create stylish books and print layouts, dynamic slideshows, and interactive web galleries to showcase your images-or publish them online with just a click. The fastest, easiest, most comprehensive way to learn Adobe Photoshop Lightroom Classic CC! Classroom in a Book(R), the best-selling series of hands-on software training workbooks, offers what no other book or training program does-an official training series from Adobe, developed with the support of Adobe product experts. Adobe Photoshop Lightroom Classic CC Classroom in a Book (2018 release) contains 11 lessons that cover the basics and beyond, providing countless tips and techniques to help you become more productive with the program. You can follow the book from start to finish or choose only those lessons that interest you. Purchase of this book includes valuable online features. Follow the instructions in the book's Getting Started section to unlock access to: - Downloadable lesson files you need to work through the projects in the book - Web Edition containing the complete text of the book, interactive quizzes, and videos that walk you through the lessons step by step, and updated material covering new feature releases from Adobe What you need to use this book: Adobe Photoshop Lightroom Classic CC (2018 release) software, for either Windows or macOS. (Software not included.) Note: Classroom in a Book does not replace the documentation, support, updates, or any other benefits of being a registered owner of Adobe Photoshop Lightroom Classic CC software.
47DSP First: A Multimedia Approach. Solids and Structural Materials
This hands-on, multimedia package provides a motivating introduction to fundamental concepts, specifically discrete-time systems, for beginning engineering students. It is suitable for introduction to electrical engineering, undergraduate digital signal processing, and survey courses in electrical engineering for non-majors. This class-tested learning package can also be used as a self-teaching tool for anyone eager to discover more about DSP applications, multimedia signals, and MATLAB. Unique features such as visual learning demonstrations, MATLAB laboratories, and a bank of solved homework problems are just a few things that make this an essential learning tool for mastering fundamental concepts in today's electrical and computer engineering curricula.
48Feature Extraction and Image Processing
This text focuses on feature extraction while also encompassing issues and techniques such as image acquisition, sampling theory, point operations and low-level feature extraction.
49Learn OpenCV 4 by Building Projects: Build real-world computer vision and image processing applications with OpenCV and C++, 2nd Edition
50Two-Dimensional Signal and Image Processing
New to P-H Signal Processing Series (Alan Oppenheim, Series Ed) this text covers the principles and applications of "multidimensional" and "image" digital signal processing. For Sr/grad level courses in image processing in EE departments.
51Fundamentals of Electronic Image Processing
This book is directed to engineers and scientists who need to understand the fundamentals of image processing theory and algorithms to perform specific image processing tasks. It is intended to fill the gap between existing high-level texts and the need for a more practical and fundamental text on image processing. Throughout, the author has included a large number of examples to give the reader a better understanding of how particular image processing algorithms work.
52Hands-On Image Processing with Python: Expert techniques for advanced image analysis and effective interpretation of image data
53Remote Sensing Digital Image Analysis: An Introduction
Revised and enlarged to reflect new developments in the field, the fourth edition of this well-established text provides an introduction to quantitative evaluation of satellite- and aircraft-derived remotely retrieved data. Each chapter covers the pros and cons of digital remotely sensed data, without detailed mathematical treatment of computer based algorithms, but in a manner conductive to an understanding of their capabilities and limitations.
54Image Processing and Acquisition using Python (Chapman & Hall/CRC Mathematical and Computational Imaging Sciences Series)
55Digital Picture Processing, Volume 1
The rapid rate at which the field of digital picture processing has grown in the past five years had necessitated extensive revisions and the introduction of topics not found in the original edition.
56Beginning Digital Image Processing: Using Free Tools for Photographers
Since the advent of digital photography, we have been able to post-process our pictures. However, to do it properly, we have to become digital art apprentices. Sebastian Montabone is a computer vision expert who wants us to use our cameras and image processing software to come up with works of art. In this book, he teaches image processing techniques of ascending difficulty based on freely available tools. The book teaches you to use the best tools for the job, and it focuses on the techniques, not the environments or toolchains in which they run. Also in this book, you'll learn about the Canon Hack Development Kit (CHDK), which expands the features of some cameras.