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Applied Machine Learning / / by David Forsyth
| Applied Machine Learning / / by David Forsyth |
| Autore | Forsyth David |
| Edizione | [1st ed. 2019.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019 |
| Descrizione fisica | 1 online resource (XXI, 494 p. 159 illus., 86 illus. in color.) |
| Disciplina | 006.31 |
| Soggetto topico |
Artificial intelligence
Mathematical statistics Artificial Intelligence Probability and Statistics in Computer Science |
| ISBN | 3-030-18114-6 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | 1. Learning to Classify -- 2. SVM’s and Random Forests -- 3. A Little Learning Theory -- 4. High-dimensional Data -- 5. Principal Component Analysis -- 6. Low Rank Approximations -- 7. Canonical Correlation Analysis -- 8. Clustering -- 9. Clustering using Probability Models -- 10. Regression -- 11. Regression: Choosing and Managing Models -- 12. Boosting -- 13. Hidden Markov Models -- 14. Learning Sequence Models Discriminatively -- 15. Mean Field Inference -- 16. Simple Neural Networks -- 17. Simple Image Classifiers -- 18. Classifying Images and Detecting Objects -- 19. Small Codes for Big Signals -- Index. |
| Record Nr. | UNINA-9910349292703321 |
Forsyth David
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| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Computer vision : a modern approach / / David A. Forsyth [and three others]
| Computer vision : a modern approach / / David A. Forsyth [and three others] |
| Autore | Forsyth David |
| Edizione | [Second edition.] |
| Pubbl/distr/stampa | Boston : , : Pearson, , [2012] |
| Descrizione fisica | 1 online resource (793 pages) : illustrations, tables |
| Disciplina | 006.37 |
| Collana | Always Learning |
| Soggetto topico | Computer vision |
| ISBN | 1-292-01408-3 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover -- Title Page -- Contents -- I IMAGE FORMATION -- 1 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 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 CALIBRATION -- 1.3.1 ALinear Approach to Camera Calibration -- 1.3.2 ANonlinear Approach to Camera Calibration -- 1.4 NOTES -- 2 Light and Shading -- 2.1 MODELLING PIXEL BRIGHTNESS -- 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 Calibration 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 NOTES -- 3 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 A MODEL 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 NOTES -- II EARLY VISION: JUST ONE IMAGE.
4 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 by Normalized Correlation -- 4.7 TECHNIQUE: SCALE AND IMAGE PYRAMIDS -- 4.7.1 The Gaussian Pyramid -- 4.7.2 Applications of Scaled Representations -- 4.8 NOTES -- 5 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 Neighborhood -- 5.4 DESCRIBING NEIGHBORHOODS WITH SIFT AND HOG FEATURES -- 5.4.1 SIFT Features -- 5.4.2 HOG Features -- 5.5 COMPUTING LOCAL FEATURES IN PRACTICE -- 5.6 NOTES -- 6 Texture -- 6.1 LOCAL TEXTURE REPRESENTATIONS USING FILTERS -- 6.1.1 Spots and Bars -- 6.1.2 From Filter Outputs to Texture Representation -- 6.1.3 Local Texture Representations in Practice -- 6.2 POOLED TEXTURE REPRESENTATIONS BY DISCOVERING TEXTONS -- 6.2.1 Vector Quantization and Textons -- 6.2.2 K-means Clustering for Vector Quantization -- 6.3 SYNTHESIZING TEXTURES AND FILLING HOLES IN IMAGES -- 6.3.1 Synthesis by Sampling Local Models -- 6.3.2 Filling in Holes in Images -- 6.4 IMAGE DENOISING -- 6.4.1 Non-local Means -- 6.4.2 Block Matching 3D (BM3D) -- 6.4.3 Learned Sparse Coding -- 6.4.4 Results. 6.5 SHAPE FROM TEXTURE -- 6.5.1 Shape from Texture for Planes -- 6.5.2 Shape from Texture for Curved Surfaces -- 6.6 NOTES -- III EARLY VISION: MULTIPLEIMAGES -- 7 Stereopsis -- 7.1 BINOCULAR CAMERA GEOMETRY AND THE EPIPOLAR CONSTRAINT -- 7.1.1 Epipolar Geometry -- 7.1.2 The Essential Matrix -- 7.1.3 The Fundamental Matrix -- 7.2 BINOCULAR RECONSTRUCTION -- 7.2.1 Image Rectification -- 7.3 HUMAN STEREOPSIS -- 7.4 LOCAL METHODS FOR BINOCULAR FUSION -- 7.4.1 Correlation -- 7.4.2 Multi-Scale Edge Matching -- 7.5 GLOBAL METHODS FOR BINOCULAR FUSION -- 7.5.1 Ordering Constraints and Dynamic Programming -- 7.5.2 Smoothness Constraints and Combinatorial Optimization over Graphs -- 7.6 USING MORE CAMERAS -- 7.7 APPLICATION: ROBOT NAVIGATION -- 7.8 NOTES -- 8 Structure from Motion -- 8.1 INTERNALLY CALIBRATED PERSPECTIVE CAMERAS -- 8.1.1 Natural Ambiguity of the Problem -- 8.1.2 Euclidean Structure and Motion from Two Images -- 8.1.3 Euclidean Structure and Motion from Multiple Images -- 8.2 UNCALIBRATED WEAK-PERSPECTIVE CAMERAS -- 8.2.1 Natural Ambiguity of the Problem -- 8.2.2 Affine Structure and Motion from Two Images -- 8.2.3 Affine Structure and Motion from Multiple Images -- 8.2.4 From Affine to Euclidean Shape -- 8.3 UNCALIBRATED PERSPECTIVE CAMERAS -- 8.3.1 Natural Ambiguity of the Problem -- 8.3.2 Projective Structure and Motion from Two Images -- 8.3.3 Projective Structure and Motion from Multiple Images -- 8.3.4 From Projective to Euclidean Shape -- 8.4 NOTES -- IV MID-LEVEL VISION -- 9 Segmentation by Clustering -- 9.1 HUMAN VISION: GROUPING AND GESTALT -- 9.2 IMPORTANT APPLICATIONS -- 9.2.1 Background Subtraction -- 9.2.2 Shot Boundary Detection -- 9.2.3 Interactive Segmentation -- 9.2.4 Forming Image Regions -- 9.3 IMAGE SEGMENTATION BY CLUSTERING PIXELS -- 9.3.1 Basic Clustering Methods -- 9.3.2 The Watershed Algorithm. 9.3.3 Segmentation Using K-means -- 9.3.4 Mean Shift: Finding Local Modes in Data -- 9.3.5 Clustering and Segmentation with Mean Shift -- 9.4 SEGMENTATION, CLUSTERING, AND GRAPHS -- 9.4.1 Terminology and Facts for Graphs -- 9.4.2 Agglomerative Clustering with a Graph -- 9.4.3 Divisive Clustering with a Graph -- 9.4.4 Normalized Cuts -- 9.5 IMAGE SEGMENTATION IN PRACTICE -- 9.5.1 Evaluating Segmenters -- 9.6 NOTES -- 10 Grouping and Model Fitting -- 10.1 THE HOUGH TRANSFORM -- 10.1.1 Fitting Lines with the Hough Transform -- 10.1.2 Using the Hough Transform -- 10.2 FITTING LINES AND PLANES -- 10.2.1 Fitting a Single Line -- 10.2.2 Fitting Planes -- 10.2.3 Fitting Multiple Lines -- 10.3 FITTING CURVED STRUCTURES -- 10.4 Robustness -- 10.4.1 M-Estimators -- 10.4.2 RANSAC: Searching for Good Points -- 10.5 FITTING USING PROBABILISTIC MODELS -- 10.5.1 Missing Data Problems -- 10.5.2 Mixture Models and Hidden Variables -- 10.5.3 The EM Algorithm for Mixture Models -- 10.5.4 Difficulties with the EM Algorithm -- 10.6 MOTION SEGMENTATION BY PARAMETER ESTIMATION -- 10.6.1 Optical Flow and Motion -- 10.6.2 Flow Models -- 10.6.3 Motion Segmentation with Layers -- 10.7 MODEL SELECTION: WHICH MODEL IS THE BEST FIT? -- 10.7.1 Model Selection Using Cross-Validation -- 10.8 NOTES -- 11 Tracking -- 11.1 SIMPLE TRACKING STRATEGIES -- 11.1.1 Tracking by Detection -- 11.1.2 Tracking Translations by Matching -- 11.1.3 Using Affine Transformations to Confirm a Match -- 11.2 TRACKING USING MATCHING -- 11.2.1 Matching Summary Representations -- 11.2.2 Tracking Using Flow -- 11.3 TRACKING LINEAR DYNAMICAL MODELS WITH KALMAN FILTERS -- 11.3.1 Linear Measurements and Linear Dynamics -- 11.3.2 The Kalman Filter -- 11.3.3 Forward-backward Smoothing -- 11.4 DATA ASSOCIATION -- 11.4.1 Linking Kalman Filters with Detection Methods -- 11.4.2 Key Methods of Data Association. 11.5 PARTICLE FILTERING -- 11.5.1 Sampled Representations of Probability Distributions -- 11.5.2 The Simplest Particle Filter -- 11.5.3 The Tracking Algorithm -- 11.5.4 A Workable Particle Filter -- 11.5.5 Practical Issues in Building Particle Filters -- 11.6 NOTES -- V HIGH-LEVEL VISION -- 12 Registration -- 12.1 REGISTERING RIGID OBJECTS -- 12.1.1 Iterated Closest Points -- 12.1.2 Searching for Transformations via Correspondences -- 12.1.3 Application: Building Image Mosaics -- 12.2 MODEL-BASED VISION: REGISTERING RIGID OBJECTS WITH PROJECTION -- 12.2.1 Verification: Comparing Transformed and Rendered Source to Target -- 12.3 REGISTERING DEFORMABLE OBJECTS -- 12.3.1 Deforming Texture with Active Appearance Models -- 12.3.2 Active Appearance Models in Practice -- 12.3.3 Application: Registration in Medical Imaging Systems -- 12.4 NOTES -- 13 Smooth Surfaces and Their Outlines -- 13.1 ELEMENTS OF DIFFERENTIAL GEOMETRY -- 13.1.1 Curves -- 13.1.2 Surfaces -- 13.2 CONTOUR GEOMETRY -- 13.2.1 The Occluding Contour and the Image Contour -- 13.2.2 The Cusps and Inflections of the Image Contour -- 13.2.3 Koenderink's Theorem -- 13.3 VISUAL EVENTS: MORE DIFFERENTIAL GEOMETRY -- 13.3.1 The Geometry of the Gauss Map -- 13.3.2 Asymptotic Curves -- 13.3.3 The Asymptotic Spherical Map -- 13.3.4 Local Visual Events -- 13.3.5 The Bitangent Ray Manifold -- 13.3.6 Multilocal Visual Events -- 13.3.7 The Aspect Graph -- 13.4 NOTES -- 14 Range Data -- 14.1 ACTIVE RANGE SENSORS -- 14.2 RANGE DATA SEGMENTATION -- 14.2.1 Elements of Analytical Differential Geometry -- 14.2.2 Finding Step and Roof Edges in Range Images -- 14.2.3 Segmenting Range Images into Planar Regions -- 14.3 RANGE IMAGE REGISTRATION AND MODEL ACQUISITION -- 14.3.1 Quaternions -- 14.3.2 Registering Range Images -- 14.3.3 Fusing Multiple Range Images -- 14.4 OBJECT RECOGNITION. 14.4.1 Matching Using Interpretation Trees. |
| Record Nr. | UNINA-9910151650803321 |
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Forsyth David
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| Boston : , : Pearson, , [2012] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Probability and statistics for computer science / / by David Forsyth
| Probability and statistics for computer science / / by David Forsyth |
| Autore | Forsyth David |
| Edizione | [1st ed. 2018.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2018 |
| Descrizione fisica | 1 online resource (XXIV, 367 p. 124 illus., 84 illus. in color.) |
| Disciplina | 004.015114 |
| Soggetto topico |
Estadística matemàtica - Informàtica
Simulació per ordinador Mathematical statistics Computer simulation Statistics Probability and Statistics in Computer Science Simulation and Modeling Statistics and Computing/Statistics Programs |
| ISBN |
9783319644103
3-319-64410-6 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | 1 Notation and conventions -- 2 First Tools for Looking at Data -- 3 Looking at Relationships -- 4 Basic ideas in probability -- 5 Random Variables and Expectations -- 6 Useful Probability Distributions -- 7 Samples and Populations -- 8 The Significance of Evidence -- 9 Experiments -- 10 Inferring Probability Models from Data -- 11 Extracting Important Relationships in High Dimensions -- 12 Learning to Classify -- 13 Clustering: Models of High Dimensional Data -- 14 Regression -- 15 Markov Chains and Hidden Markov Models -- 16 Resources. |
| Record Nr. | UNINA-9910299288703321 |
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Forsyth David
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| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2018 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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