Paperback. Pub Date :2012-05-01 Publisher: Electronic Industry Press Only genuine brand new book No pictures. Baidu check title ISBN Pricing Press; only puerile - can not counter-offer can not free open textbook amount of the invoice is not taxed. books dedicated machine-printed invoices.
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Book Description paperback. Book Condition: New. Paperback. Pub Date :2012-05-01 Publisher: Electronic Industry Press Only genuine brand new book No pictures. Baidu check title ISBN Pricing Press; only puerile - can not counter-offer can not free open textbook amount of the invoice is not taxed. books dedicated machine-printed invoices. Bookseller Inventory # CA069086
Book Description paperback. Book Condition: New. Ship out in 2 business day, And Fast shipping, Free Tracking number will be provided after the shipment.Paperback. Pub Date: Unknown Pages: 761 in Publisher: Publishing House of Electronics Industry List Price: 95.00 yuan Author: Forsyth (United States) waiting Publisher: Electronic Industry Press ISBN: 9787121168307 Pages: 761 Edition: 1 Binding: Paperback: 16 Published :2012 -5-1 printing time: Words: 1268000 Item ID: 22788402 contents Introduction to computer vision is to study how to make artificial system from the image or cube perception of science. This book is a classic textbook of the field of computer vision. the content involving geometric camera model. light and coloring. color. linear filtering. local image features. texture. three-dimensional relative movement structure. clustering segmentation. combined with the model fitting track. registration. smooth surface and skeleton. the distance data. image classification. object detection and identification. optimization technology and content-based modeling and rendering of images. humanoid research. image search and retrieval. Compared with the previous edition. this book has simplified some of the themes. application examples. rewrite modern characteristics. details of modern image editing technology with object recognition technology. About the author catalog I the IMAGE FORMATION1 Geometric Camera Models 1.1 Image Formation 1.1.1 Pinhole Perspective 1.1.2 Weak Perspective 1.1.3 Cameras with Lenses 1.1.4 The Human Eye 1.2 Intrinsic and for Extrinsic Parameters 1.2.1 Rigid Transformations and Homogeneous Coordinates 1.2.2 Intrinsic Parameters 1.2.3 Extrinsic Parameters 1.2.4 Perspective Projection Matrices 1.2.5 Weak-Perspective Projection Matrices 1.3 Geometric Camera Caliation 1.3.1 ALinear Approach to Camera Caliation 1.3.2 ANonlinear Approach to Camera Caliation 1.4 Notes2 Light and Shading 2.1 Modelling Pixel ightness 2.1.1 Reflection at Surfaces 2.1.2 Sources and Their Effects 2.1.3 The Lambertian Specular Model 2.1.4 Area Sources 2.2 Inference from Shading 2.2.1 Radiometric Caliation and High Dynamic Range Images 2.2.2 The Shape of Specularities 2.2.3 Inferring Lightness and Illumination 2.2.4 Photometric Stereo: Shape from Multiple Shaded Images 2.3 Modelling Interreflection 2.3.1 The Illumination at a Patch Due to an Area Source 2.3.2 Radiosity and Exitance 2.3.3 An Interreflection Model 2.3.4 Qualitative Properties of Interreflections 2.4 Shape from One Shaded Image 2.5 Notes3 Color 3.1 Human Color Perception 3.1.1 Color Matching 3.1.2 Color Receptors 3.2 The Physics of Color 3.2.1 The Color of Light Sources 3.2.2 The Color of Surfaces 3.3 Representing Color 3.3.1 Linear Color Spaces 3.3.2 Non-linear Color Spaces 3.4 AModel of Image Color 3.4.1 The Diffuse Term 3.4.2 The Specular Term 3.5 Inference from Color 3.5.1 Finding Specularities Using Color 3.5.2 Shadow Removal Using Color 3.5.3 Color Constancy: Surface Color from Image Color 3.6 NotesII EARLY VISION: JUST ONE IMAGE4 Linear Filters 4.1 Linear Filters and Convolution 4.1.1 Convolution 4.2 Shift Invariant Linear Systems 4.2.1 Discrete Convolution 4.2.2 Continuous Convolution 4.2.3 Edge Effects in Discrete Convolutions 4.3 Spatial Frequency and Fourier Transforms 4.3.1 Fourier Transforms 4.4 Sampling and Aliasing 4.4.1 Sampling 4.4.2 Aliasing 4.4.3 Smoothing and Resampling 4.5 Filters as Templates 4.5.1 Convolution as a Dot Product 4.5.2 Changing Basis 4.6 Technique: Normalized Correlation and Finding Patterns 4.6.1 Controlling the Television by Finding Hands byNormalized Correlation 4.7 Technique: Scale and Image Pyramids 4.7.1 The Gaussian Pyramid 4.7.2 Applications of Scaled Representations 4.8 Notes5 Local Image Features 5.1 Computing the Image Gradient 5.1.1 Derivative of Gaussian Filters 5.2 Representing the Image Gradient 5.2.1 Gradient-Based Edge Detectors 5.2.2 Orientations 5.3 Finding Corners and Building Neighborhoods 5.3.1 Finding Corners 5.3.2 Using Scale and Orientation to Build a Neighb. Bookseller Inventory # EJ026637
Book Description paperback. Book Condition: New. Ship out in 2 business day, And Fast shipping, Free Tracking number will be provided after the shipment.Paperback.Pub Date:2012-05-01 Pages:761 Publisher: QQ11408523441. 2.48 3.EMS 4. 5.6.:95.0076.00.19.0080:2012-5-1ISBN97871211683071268000761116I IMAGE FORMATION1 Geometric Camera Models1.1 Image Formation1.1.1 Pinhole Perspective1.1.2 Weak Perspective1.1.3 Cameras with Lenses1.1.4 The Human Eye1.2 Intrinsic and Extrinsic Parameters1.2.1 Rigid Transformations and Homogeneous Coordinates1.2.2 Intrinsic Parameters1.2.3 Extrinsic Parameters1.2.4 Perspective Projection Matrices1.2.5 Weak-Perspective Projection Matrices1.3 Geometric Camera Calibration1.3.1 ALinear Approach to Camera Calibration1.3.2 ANonlinear Approach to Camera Calibration1.4 Notes2 Light and Shading2.1 Modelling Pixel Brightness2.1.1 Reflection at Surfaces2.1.2 Sources and Their Effects2.1.3 The Lambertian Specular Model2.1.4 Area Sources2.2 Inference from Shading2.2.1 Radiometric Calibration and High Dynamic Range Images2.2.2 The Shape of Specularities2.2.3 Inferring Lightness and Illumination2.2.4 Photometric Stereo: Shape from Multiple Shaded Images2.3 Modelling Interreflection2.3.1 The Illumination at a Patch Due to an Area Source2.3.2 Radiosity and Exitance2.3.3 An Interreflection Model2.3.4 Qualitative Properties of Interreflections2.4 Shape from One Shaded Image2.5 Notes3 Color3.1 Human Color Perception3.1.1 Color Matching3.1.2 Color Receptors3.2 The Physics of Color3.2.1 The Color of Light Sources3.2.2 The Color of Surfaces3.3 Representing Color3.3.1 Linear Color Spaces3.3.2 Non-linear Color Spaces3.4 AModel of Image Color3.4.1 The Diffuse Term3.4.2 The Specular Term3.5 Inference from Color3.5.1 Finding Specularities Using Color3.5.2 Shadow Removal Using Color3.5.3 Color Constancy: Surface Color from Image Color3.6 NotesII EARLY VISION: JUST ONE IMAGE4 Linear Filters4.1 Linear Filters and Convolution4.1.1 Convolution4.2 Shift Invariant Linear Systems4.2.1 Discrete Convolution4.2.2 Continuous Convolution4.2.3 Edge Effects in Discrete Convolutions4.3 Spatial Frequency and Fourier Transforms4.3.1 Fourier Transforms4.4 Sampling and Aliasing4.4.1 Sampling4.4.2 Aliasing4.4.3 Smoothing and Resampling4.5 Filters as Templates4.5.1 Convolution as a Dot Product4.5.2 Changing Basis4.6 Technique: Normalized Correlation and Finding Patterns4.6.1 Controlling the Television by Finding Hands byNormalizedCorrelation4.7 Technique: Scale and Image Pyramids4.7.1 The Gaussian Pyramid4.7.2 Applications of Scaled Representations4.8 Notes5 Local Image Features5.1 Computing the Image Gradient5.1.1 Derivative of Gaussian Filters5.2 Representing the Image Gradient5.2.1 Gradient-Based Edge Detectors5.2.2 Orientations5.3 Finding Corners and Building Neighborhoods5.3.1 Finding Corners5.3.2 Using Scale and Orientation to Build a Neighborhood5.4 Describing Neighborhoods with SIFT and HOG Features5.4.1 SIFT Features5.4.2 HOG Features5.5 Computing Local Features in Practice5.6 Notes6 Texture6.1 Local Texture Representations Using Filters6.1.1 Spots and Bars6.1.2 From Filter Outputs to Texture Representation6.1.3 Local Texture Representations in Practice6.2 Pooled Texture Representations by Discovering Textons6.2.1 Vector Quantization and Textons6.2.2 K-means Clustering for Vector Quantization6.3 Synthesizing Textures and Filling Holes in Images6.3.1 Synthesis by Sampling Local Models6.3.2 Filling in Holes in Images6.4 Image Denoising6.4.1 Non-local Means6.4.2 Block Matching 3D (BM3D)6.4.3 Learned Sparse Coding6.4.4 Results6.5 Shape from Texture6.5.1 Shape from Texture for Planes6.5.2 Shape from Texture for Curved Surfaces6.6 NotesIII EARLY VISION: MULTIPLE IMAGES7 Stereopsis7.1 Binocular Camera Geometry and the Epipolar Constraint7.1.1 Epipolar Geometry7.1.2 The Essential Matrix7.1.3 The Fundamental Matrix7.2 Binocular Reconstruction7.2.1 Image Rectification7.3 Human Stereopsis7.4 Local Methods for Binocular Fusion7.4.1 Correlation7.4.2 Multi-Scale Edge Matching7.5 Global Methods for Binoc. Bookseller Inventory # FQ033666