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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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Computer Vision: A Modern Appr
Seller: World of Books (was SecondSale), Montgomery, IL, U.S.A.
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Condition: Good. Item in good condition. Textbooks may not include supplemental items i.e. CDs, access codes etc. Seller Inventory # 00086272583
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Computer Vision: A Modern Appr
Seller: World of Books (was SecondSale), Montgomery, IL, U.S.A.
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Condition: Very Good. Item in very good condition! Textbooks may not include supplemental items i.e. CDs, access codes etc. Seller Inventory # 00081023736
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(Chinese Edition)
ISBN 10: 7121168308
ISBN 13: 9787121168307
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Seller: liu xing, Nanjing, JS, China
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paperback. 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. Seller Inventory # FQ033666
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