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The Service Innovation Handbook
23,99 € *
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This is an action-oriented book for managers and entrepreneurs searching for ways to tackle issues they face in terms of developing and delivering services. The book focuses on service organizations, but has a broad interpretation of what services are. It is directed at the business world and combines inspirational text that is full of examples, with the features of a useful handbook of practical methods with associated templates.

Anbieter: buecher
Stand: 30.05.2020
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Animal Behavior
55,99 € *
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Animal Behavior, Second Edition, covers the broad sweep of animal behavior from its neurological underpinnings to the importance of behavior in conservation. The authors, Michael Breed and Janice Moore, bring almost 60 years of combined experience as university professors to this textbook, much of that teaching animal behavior. An entire chapter is devoted to the vibrant new field of behavior and conservation, including topics such as social behavior and the relationship between parasites, pathogens, and behavior. Thoughtful coverage has also been given to foraging behavior, mating and parenting behavior, anti-predator behavior, and learning. This text addresses the physiological foundations of behavior in a way that is both accessible and inviting, with each chapter beginning with learning objectives and ending with thought-provoking questions. Additionally, special terms and definitions are highlighted throughout. Animal Behavior provides a rich resource for students (and professors) from a wide range of life science disciplines. Provides a rich resource for students and professors from a wide range of life science disciplines Updated and revised chapters, with at least 50% new case studies and the addition of contemporary in-text examples Expanded and updated coverage of animal welfare topics Includes behavior and homeostatic mechanisms, behavior and conservation, and behavioral aspects of disease Available lab manual with fully developed and tested laboratory exercises Companion website includes newly developed slide sets/templates (PowerPoints) coordinated with the book

Anbieter: buecher
Stand: 30.05.2020
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Animal Behavior
55,99 € *
ggf. zzgl. Versand

Animal Behavior, Second Edition, covers the broad sweep of animal behavior from its neurological underpinnings to the importance of behavior in conservation. The authors, Michael Breed and Janice Moore, bring almost 60 years of combined experience as university professors to this textbook, much of that teaching animal behavior. An entire chapter is devoted to the vibrant new field of behavior and conservation, including topics such as social behavior and the relationship between parasites, pathogens, and behavior. Thoughtful coverage has also been given to foraging behavior, mating and parenting behavior, anti-predator behavior, and learning. This text addresses the physiological foundations of behavior in a way that is both accessible and inviting, with each chapter beginning with learning objectives and ending with thought-provoking questions. Additionally, special terms and definitions are highlighted throughout. Animal Behavior provides a rich resource for students (and professors) from a wide range of life science disciplines. Provides a rich resource for students and professors from a wide range of life science disciplines Updated and revised chapters, with at least 50% new case studies and the addition of contemporary in-text examples Expanded and updated coverage of animal welfare topics Includes behavior and homeostatic mechanisms, behavior and conservation, and behavioral aspects of disease Available lab manual with fully developed and tested laboratory exercises Companion website includes newly developed slide sets/templates (PowerPoints) coordinated with the book

Anbieter: buecher
Stand: 30.05.2020
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Exceptional C++
34,99 € *
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Exceptional C++ shows by example how to go about sound software engineering in standard C++. Do you enjoy solving thorny C++ problems and puzzles? Do you relish writing robust and extensible code? Then take a few minutes and challenge yourself with some tough C++ design and programming problems. The puzzles and problems in Exceptional C++ not only entertain, they will help you hone your skills to become the sharpest C++ programmer you can be. Many of these problems are culled from the famous Guru of the Week feature of the Internet newsgroup comp.lang.c++.moderated, expanded and updated to conform to the official ISO/ANSI C++ Standard. Each problem is rated according to difficulty and is designed to illustrate subtle programming mistakes or design considerations. After you've had a chance to attempt a solution yourself, the book then dissects the code, illustrates what went wrong, and shows how the problem can be fixed. Covering a broad range of C++ topics, the problems and solutions address critical issues such as: Generic programming and how to write reusable templates Exception safety issues and techniques Robust class design and inheritance Compiler firewalls and the Pimpl Idiom Name lookup, namespaces, and the Interface Principle Memory management issues and techniques Traps, pitfalls, and anti-idioms Optimization Try your skills against the C++ masters and come away with the insight and experience to create more efficient, effective, robust, and portable C++ code. 0201615622B04062001

Anbieter: buecher
Stand: 30.05.2020
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Exceptional C++
34,99 € *
ggf. zzgl. Versand

Exceptional C++ shows by example how to go about sound software engineering in standard C++. Do you enjoy solving thorny C++ problems and puzzles? Do you relish writing robust and extensible code? Then take a few minutes and challenge yourself with some tough C++ design and programming problems. The puzzles and problems in Exceptional C++ not only entertain, they will help you hone your skills to become the sharpest C++ programmer you can be. Many of these problems are culled from the famous Guru of the Week feature of the Internet newsgroup comp.lang.c++.moderated, expanded and updated to conform to the official ISO/ANSI C++ Standard. Each problem is rated according to difficulty and is designed to illustrate subtle programming mistakes or design considerations. After you've had a chance to attempt a solution yourself, the book then dissects the code, illustrates what went wrong, and shows how the problem can be fixed. Covering a broad range of C++ topics, the problems and solutions address critical issues such as: Generic programming and how to write reusable templates Exception safety issues and techniques Robust class design and inheritance Compiler firewalls and the Pimpl Idiom Name lookup, namespaces, and the Interface Principle Memory management issues and techniques Traps, pitfalls, and anti-idioms Optimization Try your skills against the C++ masters and come away with the insight and experience to create more efficient, effective, robust, and portable C++ code. 0201615622B04062001

Anbieter: buecher
Stand: 30.05.2020
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Writing GNU Emacs Extensions (eBook, PDF)
24,95 € *
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Yes, it is possible to be all things to all people, if you're talking about the Emacs editor. As a user, you can make any kind of customization you want, from choosing the keystrokes that invoke your favorite commands to creating a whole new work environment that looks like nothing ever developed before. It's all in Emacs Lisp -- and in this short but fast-paced book.GNU Emacs is more than an editor; it's a programming environment, a communications package, and many other things. To provide such a broad range of functions, it offers a full version of the Lisp programming language -- something much more powerful than the little macro languages provided in other editors (including older versions of Emacs). GNU Emacs is a framework in which you can create whole new kinds of editors or just alter aspects of the many functions it already provides.In this book, Bob Glickstein delves deep into the features that permit far-reaching Emacs customizations. He teaches you the Lisp language and discusses Emacs topics (such as syntax tables and macro templates) in easy-to-digest portions. Examples progress in complexity from simple customizations to extensive major modes.You will learn how to write interactive commands, use hooks and advice, perform error recovery, manipulate windows, buffers, and keymaps, exploit and alter Emacs's main loop, and more. Each topic is explored through realistic examples and a series of successive refinements that illustrate not only the Emacs Lisp language, but the development process as well, making learning pleasant and natural.

Anbieter: buecher
Stand: 30.05.2020
Zum Angebot
Writing GNU Emacs Extensions (eBook, PDF)
24,95 € *
ggf. zzgl. Versand

Yes, it is possible to be all things to all people, if you're talking about the Emacs editor. As a user, you can make any kind of customization you want, from choosing the keystrokes that invoke your favorite commands to creating a whole new work environment that looks like nothing ever developed before. It's all in Emacs Lisp -- and in this short but fast-paced book.GNU Emacs is more than an editor; it's a programming environment, a communications package, and many other things. To provide such a broad range of functions, it offers a full version of the Lisp programming language -- something much more powerful than the little macro languages provided in other editors (including older versions of Emacs). GNU Emacs is a framework in which you can create whole new kinds of editors or just alter aspects of the many functions it already provides.In this book, Bob Glickstein delves deep into the features that permit far-reaching Emacs customizations. He teaches you the Lisp language and discusses Emacs topics (such as syntax tables and macro templates) in easy-to-digest portions. Examples progress in complexity from simple customizations to extensive major modes.You will learn how to write interactive commands, use hooks and advice, perform error recovery, manipulate windows, buffers, and keymaps, exploit and alter Emacs's main loop, and more. Each topic is explored through realistic examples and a series of successive refinements that illustrate not only the Emacs Lisp language, but the development process as well, making learning pleasant and natural.

Anbieter: buecher
Stand: 30.05.2020
Zum Angebot
Computer Vision: A Modern Approach
50,99 € *
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Appropriate for upper-division undergraduate- and graduate-level courses in computer vision found in departments of Computer Science, Computer Engineering and Electrical Engineering. This textbook provides the most complete treatment of modern computer vision methods by two of the leading authorities in the field. This accessible presentation gives both a general view of the entire computer vision enterprise and also offers sufficient detail for students to be able to build useful applications. Students will learn techniques that have proven to be useful by first-hand experience and a wide range of mathematical methods. Features + Benefits Broad coverage—Coverage of a wide range of topics allows customization to fit instructor, student, and course needs. Allows instructors to select the most relevant topics for their students and encourages students to enrich their coursework by reading information on other computer vision topics. Most comprehensive and up-to-date text on computer vision—Includes essential topics that either reflect practical significance or are of theoretical importance. Provides students with the most coherent synthesis of current views and teaches them successful techniques for building applications. Depth of the material accessible to various levels of students—Topics are discussed in substantial and increasing depth. While the first half of each chapter is accessible to undergraduates, a good grasp of each chapter provides students with a professional level of skill and knowledge. Application surveys—Describe numerous important application areas such as image based rendering and digital libraries. Teaches students about practical use of techniques and helps them gain insight into the demands of applications. Many important algorithms broken down and illustrated in pseudo code. Enables students to build working systems easily as they can understand the construction of the final application. Excellent pedagogy throughout the text—Includes numerous worked examples, exercises, programming assignments, and extensive illustrations. Provides students with ample opportunity to apply the concepts in the text. I IMAGE FORMATION 1 1 Geometric Camera Models 3 1.1 Image Formation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 1.1.1 Pinhole Perspective . . . . . . . . . . . . . . . . . . . . . . . 4 1.1.2 Weak Perspective . . . . . . . . . . . . . . . . . . . . . . . . . 6 1.1.3 Cameras with Lenses . . . . . . . . . . . . . . . . . . . . . . . 8 1.1.4 The Human Eye . . . . . . . . . . . . . . . . . . . . . . . . . 12 1.2 Intrinsic and Extrinsic Parameters . . . . . . . . . . . . . . . . . . . 14 1.2.1 Rigid Transformations and Homogeneous Coordinates . . . . 14 1.2.2 Intrinsic Parameters . . . . . . . . . . . . . . . . . . . . . . . 16 1.2.3 Extrinsic Parameters . . . . . . . . . . . . . . . . . . . . . . . 18 1.2.4 Perspective Projection Matrices . . . . . . . . . . . . . . . . . 19 1.2.5 Weak-Perspective Projection Matrices . . . . . . . . . . . . . 20 1.3 Geometric Camera Calibration . . . . . . . . . . . . . . . . . . . . . 22 1.3.1 ALinear Approach to Camera Calibration . . . . . . . . . . . 23 1.3.2 ANonlinear Approach to Camera Calibration . . . . . . . . . 27 1.4 Notes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 2 Light and Shading 32 2.1 Modelling Pixel Brightness . . . . . . . . . . . . . . . . . . . . . . . 32 2.1.1 Reflection at Surfaces . . . . . . . . . . . . . . . . . . . . . . 33 2.1.2 Sources and Their Effects . . . . . . . . . . . . . . . . . . . . 34 2.1.3 The Lambertian+Specular Model . . . . . . . . . . . . . . . . 36 2.1.4 Area Sources . . . . . . . . . . . . . . . . . . . . . . . . . . . 36 2.2 Inference from Shading . . . . . . . . . . . . . . . . . . . . . . . . . . 37 2.2.1 Radiometric Calibration and High Dynamic Range Images . . 38 2.2.2 The Shape of Specularities . . . . . . . . . . . . . . . . . . . 40 2.2.3 Inferring Lightness and Illumination . . . . . . . . . . . . . . 43 2.2.4 Photometric Stereo: Shape from Multiple Shaded Images . . 46 2.3 Modelling Interreflection . . . . . . . . . . . . . . . . . . . . . . . . . 52 2.3.1 The Illumination at a Patch Due to an Area Source . . . . . 52 2.3.2 Radiosity and Exitance . . . . . . . . . . . . . . . . . . . . . 54 2.3.3 An Interreflection Model . . . . . . . . . . . . . . . . . . . . . 55 2.3.4 Qualitative Properties of Interreflections . . . . . . . . . . . . 56 2.4 Shape from One Shaded Image . . . . . . . . . . . . . . . . . . . . . 59 2.5 Notes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61 3 Color 68 3.1 Human Color Perception . . . . . . . . . . . . . . . . . . . . . . . . . 68 3.1.1 Color Matching . . . . . . . . . . . . . . . . . . . . . . . . . . 68 3.1.2 Color Receptors . . . . . . . . . . . . . . . . . . . . . . . . . 71 3.2 The Physics of Color . . . . . . . . . . . . . . . . . . . . . . . . . . . 73 3.2.1 The Color of Light Sources . . . . . . . . . . . . . . . . . . . 73 3.2.2 The Color of Surfaces . . . . . . . . . . . . . . . . . . . . . . 76 3.3 Representing Color . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77 3.3.1 Linear Color Spaces . . . . . . . . . . . . . . . . . . . . . . . 77 3.3.2 Non-linear Color Spaces . . . . . . . . . . . . . . . . . . . . . 83 3.4 AModel of Image Color . . . . . . . . . . . . . . . . . . . . . . . . . 86 3.4.1 The Diffuse Term . . . . . . . . . . . . . . . . . . . . . . . . . 88 3.4.2 The Specular Term . . . . . . . . . . . . . . . . . . . . . . . . 90 3.5 Inference from Color . . . . . . . . . . . . . . . . . . . . . . . . . . . 90 3.5.1 Finding Specularities Using Color . . . . . . . . . . . . . . . 90 3.5.2 Shadow Removal Using Color . . . . . . . . . . . . . . . . . . 92 3.5.3 Color Constancy: Surface Color from Image Color . . . . . . 95 3.6 Notes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99 II EARLY VISION: JUST ONE IMAGE 105 4 Linear Filters 107 4.1 Linear Filters and Convolution . . . . . . . . . . . . . . . . . . . . . 107 4.1.1 Convolution . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107 4.2 Shift Invariant Linear Systems . . . . . . . . . . . . . . . . . . . . . 112 4.2.1 Discrete Convolution . . . . . . . . . . . . . . . . . . . . . . . 113 4.2.2 Continuous Convolution . . . . . . . . . . . . . . . . . . . . . 115 4.2.3 Edge Effects in Discrete Convolutions . . . . . . . . . . . . . 118 4.3 Spatial Frequency and Fourier Transforms . . . . . . . . . . . . . . . 118 4.3.1 Fourier Transforms . . . . . . . . . . . . . . . . . . . . . . . . 119 4.4 Sampling and Aliasing . . . . . . . . . . . . . . . . . . . . . . . . . . 121 4.4.1 Sampling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122 4.4.2 Aliasing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125 4.4.3 Smoothing and Resampling . . . . . . . . . . . . . . . . . . . 126 4.5 Filters as Templates . . . . . . . . . . . . . . . . . . . . . . . . . . . 131 4.5.1 Convolution as a Dot Product . . . . . . . . . . . . . . . . . 131 4.5.2 Changing Basis . . . . . . . . . . . . . . . . . . . . . . . . . . 132 4.6 Technique: Normalized Correlation and Finding Patterns . . . . . . 132 4.6.1 Controlling the Television by Finding Hands by Normalized Correlation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133 4.7 Technique: Scale and Image Pyramids . . . . . . . . . . . . . . . . . 134 4.7.1 The Gaussian Pyramid . . . . . . . . . . . . . . . . . . . . . 135 4.7.2 Applications of Scaled Representations . . . . . . . . . . . . . 136 4.8 Notes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 137 5 Local Image Features 141 5.1 Computing the Image Gradient . . . . . . . . . . . . . . . . . . . . . 141 5.1.1 Derivative of Gaussian Filters . . . . . . . . . . . . . . . . . . 142 5.2 Representing the Image Gradient . . . . . . . . . . . . . . . . . . . . 144 5.2.1 Gradient-Based Edge Detectors . . . . . . . . . . . . . . . . . 145 5.2.2 Orientations . . . . . . . . . . . . . . . . . . . . . . . . . . . . 147 5.3 Finding Corners and Building Neighborhoods . . . . . . . . . . . . . 148 5.3.1 Finding Corners . . . . . . . . . . . . . . . . . . . . . . . . . 149 5.3.2 Using Scale and Orientation to Build a Neighborhood . . . . 151 5.4 Describing Neighborhoods with SIFT and HOG Features . . . . . . 155 5.4.1 SIFT Features . . . . . . . . . . . . . . . . . . . . . . . . . . 157 5.4.2 HOG Features . . . . . . . . . . . . . . . . . . . . . . . . . . 159 5.5 Computing Local Features in Practice . . . . . . . . . . . . . . . . . 160 5.6 Notes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 160 6 Texture 164 6.1 Local Texture Representations Using Filters . . . . . . . . . . . . . . 166 6.1.1 Spots and Bars . . . . . . . . . . . . . . . . . . . . . . . . . . 167 6.1.2 From Filter Outputs to Texture Representation . . . . . . . . 168 6.1.3 Local Texture Representations in Practice . . . . . . . . . . . 170 6.2 Pooled Texture Representations by Discovering Textons . . . . . . . 171 6.2.1 Vector Quantization and Textons . . . . . . . . . . . . . . . . 172 6.2.2 K-means Clustering for Vector Quantization . . . . . . . . . . 172 6.3 Synthesizing Textures and Filling Holes in Images . . . . . . . . . . 176 6.3.1 Synthesis by Sampling Local Models . . . . . . . . . . . . . . 176 6.3.2 Filling in Holes in Images . . . . . . . . . . . . . . . . . . . . 179 6.4 Image Denoising . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 182 6.4.1 Non-local Means . . . . . . . . . . . . . . . . . . . . . . . . . 183 6.4.2 Block Matching 3D (BM3D) . . . . . . . . . . . . . . . . . . 183 6.4.3 Learned Sparse Coding . . . . . . . . . . . . . . . . . . . . . 184 6.4.4 Results . . . . . . . . . . . . . . . . . . . . .Appropriate for upper-division undergraduate- and graduate-level courses in computer vision found in departments of Computer Science, Computer Engineering and Electrical Engineering. This textbook provides the most complete treatment of modern computer vision methods by two of the leading authorities in the field. This accessible presentation gives both a general view of the entire computer vision enterprise and also offers sufficient detail for students to be able to build useful applications. Students will learn techniques that have proven to be useful by first-hand experience and a wide range of mathematical methods.

Anbieter: buecher
Stand: 30.05.2020
Zum Angebot
Computer Vision: A Modern Approach
50,99 € *
ggf. zzgl. Versand

Appropriate for upper-division undergraduate- and graduate-level courses in computer vision found in departments of Computer Science, Computer Engineering and Electrical Engineering. This textbook provides the most complete treatment of modern computer vision methods by two of the leading authorities in the field. This accessible presentation gives both a general view of the entire computer vision enterprise and also offers sufficient detail for students to be able to build useful applications. Students will learn techniques that have proven to be useful by first-hand experience and a wide range of mathematical methods. Features + Benefits Broad coverage—Coverage of a wide range of topics allows customization to fit instructor, student, and course needs. Allows instructors to select the most relevant topics for their students and encourages students to enrich their coursework by reading information on other computer vision topics. Most comprehensive and up-to-date text on computer vision—Includes essential topics that either reflect practical significance or are of theoretical importance. Provides students with the most coherent synthesis of current views and teaches them successful techniques for building applications. Depth of the material accessible to various levels of students—Topics are discussed in substantial and increasing depth. While the first half of each chapter is accessible to undergraduates, a good grasp of each chapter provides students with a professional level of skill and knowledge. Application surveys—Describe numerous important application areas such as image based rendering and digital libraries. Teaches students about practical use of techniques and helps them gain insight into the demands of applications. Many important algorithms broken down and illustrated in pseudo code. Enables students to build working systems easily as they can understand the construction of the final application. Excellent pedagogy throughout the text—Includes numerous worked examples, exercises, programming assignments, and extensive illustrations. Provides students with ample opportunity to apply the concepts in the text. I IMAGE FORMATION 1 1 Geometric Camera Models 3 1.1 Image Formation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 1.1.1 Pinhole Perspective . . . . . . . . . . . . . . . . . . . . . . . 4 1.1.2 Weak Perspective . . . . . . . . . . . . . . . . . . . . . . . . . 6 1.1.3 Cameras with Lenses . . . . . . . . . . . . . . . . . . . . . . . 8 1.1.4 The Human Eye . . . . . . . . . . . . . . . . . . . . . . . . . 12 1.2 Intrinsic and Extrinsic Parameters . . . . . . . . . . . . . . . . . . . 14 1.2.1 Rigid Transformations and Homogeneous Coordinates . . . . 14 1.2.2 Intrinsic Parameters . . . . . . . . . . . . . . . . . . . . . . . 16 1.2.3 Extrinsic Parameters . . . . . . . . . . . . . . . . . . . . . . . 18 1.2.4 Perspective Projection Matrices . . . . . . . . . . . . . . . . . 19 1.2.5 Weak-Perspective Projection Matrices . . . . . . . . . . . . . 20 1.3 Geometric Camera Calibration . . . . . . . . . . . . . . . . . . . . . 22 1.3.1 ALinear Approach to Camera Calibration . . . . . . . . . . . 23 1.3.2 ANonlinear Approach to Camera Calibration . . . . . . . . . 27 1.4 Notes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 2 Light and Shading 32 2.1 Modelling Pixel Brightness . . . . . . . . . . . . . . . . . . . . . . . 32 2.1.1 Reflection at Surfaces . . . . . . . . . . . . . . . . . . . . . . 33 2.1.2 Sources and Their Effects . . . . . . . . . . . . . . . . . . . . 34 2.1.3 The Lambertian+Specular Model . . . . . . . . . . . . . . . . 36 2.1.4 Area Sources . . . . . . . . . . . . . . . . . . . . . . . . . . . 36 2.2 Inference from Shading . . . . . . . . . . . . . . . . . . . . . . . . . . 37 2.2.1 Radiometric Calibration and High Dynamic Range Images . . 38 2.2.2 The Shape of Specularities . . . . . . . . . . . . . . . . . . . 40 2.2.3 Inferring Lightness and Illumination . . . . . . . . . . . . . . 43 2.2.4 Photometric Stereo: Shape from Multiple Shaded Images . . 46 2.3 Modelling Interreflection . . . . . . . . . . . . . . . . . . . . . . . . . 52 2.3.1 The Illumination at a Patch Due to an Area Source . . . . . 52 2.3.2 Radiosity and Exitance . . . . . . . . . . . . . . . . . . . . . 54 2.3.3 An Interreflection Model . . . . . . . . . . . . . . . . . . . . . 55 2.3.4 Qualitative Properties of Interreflections . . . . . . . . . . . . 56 2.4 Shape from One Shaded Image . . . . . . . . . . . . . . . . . . . . . 59 2.5 Notes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61 3 Color 68 3.1 Human Color Perception . . . . . . . . . . . . . . . . . . . . . . . . . 68 3.1.1 Color Matching . . . . . . . . . . . . . . . . . . . . . . . . . . 68 3.1.2 Color Receptors . . . . . . . . . . . . . . . . . . . . . . . . . 71 3.2 The Physics of Color . . . . . . . . . . . . . . . . . . . . . . . . . . . 73 3.2.1 The Color of Light Sources . . . . . . . . . . . . . . . . . . . 73 3.2.2 The Color of Surfaces . . . . . . . . . . . . . . . . . . . . . . 76 3.3 Representing Color . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77 3.3.1 Linear Color Spaces . . . . . . . . . . . . . . . . . . . . . . . 77 3.3.2 Non-linear Color Spaces . . . . . . . . . . . . . . . . . . . . . 83 3.4 AModel of Image Color . . . . . . . . . . . . . . . . . . . . . . . . . 86 3.4.1 The Diffuse Term . . . . . . . . . . . . . . . . . . . . . . . . . 88 3.4.2 The Specular Term . . . . . . . . . . . . . . . . . . . . . . . . 90 3.5 Inference from Color . . . . . . . . . . . . . . . . . . . . . . . . . . . 90 3.5.1 Finding Specularities Using Color . . . . . . . . . . . . . . . 90 3.5.2 Shadow Removal Using Color . . . . . . . . . . . . . . . . . . 92 3.5.3 Color Constancy: Surface Color from Image Color . . . . . . 95 3.6 Notes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99 II EARLY VISION: JUST ONE IMAGE 105 4 Linear Filters 107 4.1 Linear Filters and Convolution . . . . . . . . . . . . . . . . . . . . . 107 4.1.1 Convolution . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107 4.2 Shift Invariant Linear Systems . . . . . . . . . . . . . . . . . . . . . 112 4.2.1 Discrete Convolution . . . . . . . . . . . . . . . . . . . . . . . 113 4.2.2 Continuous Convolution . . . . . . . . . . . . . . . . . . . . . 115 4.2.3 Edge Effects in Discrete Convolutions . . . . . . . . . . . . . 118 4.3 Spatial Frequency and Fourier Transforms . . . . . . . . . . . . . . . 118 4.3.1 Fourier Transforms . . . . . . . . . . . . . . . . . . . . . . . . 119 4.4 Sampling and Aliasing . . . . . . . . . . . . . . . . . . . . . . . . . . 121 4.4.1 Sampling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122 4.4.2 Aliasing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125 4.4.3 Smoothing and Resampling . . . . . . . . . . . . . . . . . . . 126 4.5 Filters as Templates . . . . . . . . . . . . . . . . . . . . . . . . . . . 131 4.5.1 Convolution as a Dot Product . . . . . . . . . . . . . . . . . 131 4.5.2 Changing Basis . . . . . . . . . . . . . . . . . . . . . . . . . . 132 4.6 Technique: Normalized Correlation and Finding Patterns . . . . . . 132 4.6.1 Controlling the Television by Finding Hands by Normalized Correlation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133 4.7 Technique: Scale and Image Pyramids . . . . . . . . . . . . . . . . . 134 4.7.1 The Gaussian Pyramid . . . . . . . . . . . . . . . . . . . . . 135 4.7.2 Applications of Scaled Representations . . . . . . . . . . . . . 136 4.8 Notes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 137 5 Local Image Features 141 5.1 Computing the Image Gradient . . . . . . . . . . . . . . . . . . . . . 141 5.1.1 Derivative of Gaussian Filters . . . . . . . . . . . . . . . . . . 142 5.2 Representing the Image Gradient . . . . . . . . . . . . . . . . . . . . 144 5.2.1 Gradient-Based Edge Detectors . . . . . . . . . . . . . . . . . 145 5.2.2 Orientations . . . . . . . . . . . . . . . . . . . . . . . . . . . . 147 5.3 Finding Corners and Building Neighborhoods . . . . . . . . . . . . . 148 5.3.1 Finding Corners . . . . . . . . . . . . . . . . . . . . . . . . . 149 5.3.2 Using Scale and Orientation to Build a Neighborhood . . . . 151 5.4 Describing Neighborhoods with SIFT and HOG Features . . . . . . 155 5.4.1 SIFT Features . . . . . . . . . . . . . . . . . . . . . . . . . . 157 5.4.2 HOG Features . . . . . . . . . . . . . . . . . . . . . . . . . . 159 5.5 Computing Local Features in Practice . . . . . . . . . . . . . . . . . 160 5.6 Notes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 160 6 Texture 164 6.1 Local Texture Representations Using Filters . . . . . . . . . . . . . . 166 6.1.1 Spots and Bars . . . . . . . . . . . . . . . . . . . . . . . . . . 167 6.1.2 From Filter Outputs to Texture Representation . . . . . . . . 168 6.1.3 Local Texture Representations in Practice . . . . . . . . . . . 170 6.2 Pooled Texture Representations by Discovering Textons . . . . . . . 171 6.2.1 Vector Quantization and Textons . . . . . . . . . . . . . . . . 172 6.2.2 K-means Clustering for Vector Quantization . . . . . . . . . . 172 6.3 Synthesizing Textures and Filling Holes in Images . . . . . . . . . . 176 6.3.1 Synthesis by Sampling Local Models . . . . . . . . . . . . . . 176 6.3.2 Filling in Holes in Images . . . . . . . . . . . . . . . . . . . . 179 6.4 Image Denoising . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 182 6.4.1 Non-local Means . . . . . . . . . . . . . . . . . . . . . . . . . 183 6.4.2 Block Matching 3D (BM3D) . . . . . . . . . . . . . . . . . . 183 6.4.3 Learned Sparse Coding . . . . . . . . . . . . . . . . . . . . . 184 6.4.4 Results . . . . . . . . . . . . . . . . . . . . .Appropriate for upper-division undergraduate- and graduate-level courses in computer vision found in departments of Computer Science, Computer Engineering and Electrical Engineering. This textbook provides the most complete treatment of modern computer vision methods by two of the leading authorities in the field. This accessible presentation gives both a general view of the entire computer vision enterprise and also offers sufficient detail for students to be able to build useful applications. Students will learn techniques that have proven to be useful by first-hand experience and a wide range of mathematical methods.

Anbieter: buecher
Stand: 30.05.2020
Zum Angebot