Info
- Utilizzare la checkbox di selezione a fianco di ciascun documento per attivare le funzionalità di stampa, invio email, download nei formati disponibili del (i) record.
Feature extraction & image processing for computer vision [[electronic resource] /] / Mark S. Nixon, Alberto S. Aguado
| Feature extraction & image processing for computer vision [[electronic resource] /] / Mark S. Nixon, Alberto S. Aguado |
| Autore | Nixon Mark S |
| Edizione | [3rd ed.] |
| Pubbl/distr/stampa | Oxford, : Academic, 2012 |
| Descrizione fisica | 1 online resource (628 p.) |
| Disciplina | 006.37 |
| Altri autori (Persone) |
AguadoAlberto S
NixonMark S |
| Soggetto topico |
Computer vision
Computer vision - Mathematics Pattern recognition systems Image processing - Digital techniques |
| Soggetto genere / forma | Electronic books. |
| ISBN | 0-12-397824-6 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Front Cover; Feature Extraction & Image Processing for Computer Vision; Copyright page; Contents; Preface; What is new in the third edition?; Why did we write this book?; The book and its support; In gratitude; Final message; About the authors; 1 Introduction; 1.1 Overview; 1.2 Human and computer vision; 1.3 The human vision system; 1.3.1 The eye; 1.3.2 The neural system; 1.3.3 Processing; 1.4 Computer vision systems; 1.4.1 Cameras; 1.4.2 Computer interfaces; 1.4.3 Processing an image; 1.5 Mathematical systems; 1.5.1 Mathematical tools; 1.5.2 Hello Matlab, hello images!; 1.5.3 Hello Mathcad!
1.6 Associated literature1.6.1 Journals, magazines, and conferences; 1.6.2 Textbooks; 1.6.3 The Web; 1.7 Conclusions; 1.8 References; 2 Images, sampling, and frequency domain processing; 2.1 Overview; 2.2 Image formation; 2.3 The Fourier transform; 2.4 The sampling criterion; 2.5 The discrete Fourier transform; 2.5.1 1D transform; 2.5.2 2D transform; 2.6 Other properties of the Fourier transform; 2.6.1 Shift invariance; 2.6.2 Rotation; 2.6.3 Frequency scaling; 2.6.4 Superposition (linearity); 2.7 Transforms other than Fourier; 2.7.1 Discrete cosine transform; 2.7.2 Discrete Hartley transform 2.7.3 Introductory wavelets2.7.3.1 Gabor wavelet; 2.7.3.2 Haar wavelet; 2.7.4 Other transforms; 2.8 Applications using frequency domain properties; 2.9 Further reading; 2.10 References; 3 Basic image processing operations; 3.1 Overview; 3.2 Histograms; 3.3 Point operators; 3.3.1 Basic point operations; 3.3.2 Histogram normalization; 3.3.3 Histogram equalization; 3.3.4 Thresholding; 3.4 Group operations; 3.4.1 Template convolution; 3.4.2 Averaging operator; 3.4.3 On different template size; 3.4.4 Gaussian averaging operator; 3.4.5 More on averaging; 3.5 Other statistical operators 3.5.1 Median filter3.5.2 Mode filter; 3.5.3 Anisotropic diffusion; 3.5.4 Force field transform; 3.5.5 Comparison of statistical operators; 3.6 Mathematical morphology; 3.6.1 Morphological operators; 3.6.2 Gray-level morphology; 3.6.3 Gray-level erosion and dilation; 3.6.4 Minkowski operators; 3.7 Further reading; 3.8 References; 4 Low-level feature extraction (including edge detection); 4.1 Overview; 4.2 Edge detection; 4.2.1 First-order edge-detection operators; 4.2.1.1 Basic operators; 4.2.1.2 Analysis of the basic operators; 4.2.1.3 Prewitt edge-detection operator 4.2.1.4 Sobel edge-detection operator4.2.1.5 The Canny edge detector; 4.2.2 Second-order edge-detection operators; 4.2.2.1 Motivation; 4.2.2.2 Basic operators: the Laplacian; 4.2.2.3 The Marr-Hildreth operator; 4.2.3 Other edge-detection operators; 4.2.4 Comparison of edge-detection operators; 4.2.5 Further reading on edge detection; 4.3 Phase congruency; 4.4 Localized feature extraction; 4.4.1 Detecting image curvature (corner extraction); 4.4.1.1 Definition of curvature; 4.4.1.2 Computing differences in edge direction; 4.4.1.3 Measuring curvature by changes in intensity (differentiation) 4.4.1.4 Moravec and Harris detectors |
| Record Nr. | UNINA-9910462068503321 |
Nixon Mark S
|
||
| Oxford, : Academic, 2012 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Feature extraction & image processing for computer vision [[electronic resource] /] / Mark S. Nixon, Alberto S. Aguado
| Feature extraction & image processing for computer vision [[electronic resource] /] / Mark S. Nixon, Alberto S. Aguado |
| Autore | Nixon Mark S |
| Edizione | [3rd ed.] |
| Pubbl/distr/stampa | Oxford, : Academic, 2012 |
| Descrizione fisica | 1 online resource (628 p.) |
| Disciplina | 006.37 |
| Altri autori (Persone) |
AguadoAlberto S
NixonMark S |
| Soggetto topico |
Computer vision
Computer vision - Mathematics Pattern recognition systems Image processing - Digital techniques |
| ISBN | 0-12-397824-6 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Front Cover; Feature Extraction & Image Processing for Computer Vision; Copyright page; Contents; Preface; What is new in the third edition?; Why did we write this book?; The book and its support; In gratitude; Final message; About the authors; 1 Introduction; 1.1 Overview; 1.2 Human and computer vision; 1.3 The human vision system; 1.3.1 The eye; 1.3.2 The neural system; 1.3.3 Processing; 1.4 Computer vision systems; 1.4.1 Cameras; 1.4.2 Computer interfaces; 1.4.3 Processing an image; 1.5 Mathematical systems; 1.5.1 Mathematical tools; 1.5.2 Hello Matlab, hello images!; 1.5.3 Hello Mathcad!
1.6 Associated literature1.6.1 Journals, magazines, and conferences; 1.6.2 Textbooks; 1.6.3 The Web; 1.7 Conclusions; 1.8 References; 2 Images, sampling, and frequency domain processing; 2.1 Overview; 2.2 Image formation; 2.3 The Fourier transform; 2.4 The sampling criterion; 2.5 The discrete Fourier transform; 2.5.1 1D transform; 2.5.2 2D transform; 2.6 Other properties of the Fourier transform; 2.6.1 Shift invariance; 2.6.2 Rotation; 2.6.3 Frequency scaling; 2.6.4 Superposition (linearity); 2.7 Transforms other than Fourier; 2.7.1 Discrete cosine transform; 2.7.2 Discrete Hartley transform 2.7.3 Introductory wavelets2.7.3.1 Gabor wavelet; 2.7.3.2 Haar wavelet; 2.7.4 Other transforms; 2.8 Applications using frequency domain properties; 2.9 Further reading; 2.10 References; 3 Basic image processing operations; 3.1 Overview; 3.2 Histograms; 3.3 Point operators; 3.3.1 Basic point operations; 3.3.2 Histogram normalization; 3.3.3 Histogram equalization; 3.3.4 Thresholding; 3.4 Group operations; 3.4.1 Template convolution; 3.4.2 Averaging operator; 3.4.3 On different template size; 3.4.4 Gaussian averaging operator; 3.4.5 More on averaging; 3.5 Other statistical operators 3.5.1 Median filter3.5.2 Mode filter; 3.5.3 Anisotropic diffusion; 3.5.4 Force field transform; 3.5.5 Comparison of statistical operators; 3.6 Mathematical morphology; 3.6.1 Morphological operators; 3.6.2 Gray-level morphology; 3.6.3 Gray-level erosion and dilation; 3.6.4 Minkowski operators; 3.7 Further reading; 3.8 References; 4 Low-level feature extraction (including edge detection); 4.1 Overview; 4.2 Edge detection; 4.2.1 First-order edge-detection operators; 4.2.1.1 Basic operators; 4.2.1.2 Analysis of the basic operators; 4.2.1.3 Prewitt edge-detection operator 4.2.1.4 Sobel edge-detection operator4.2.1.5 The Canny edge detector; 4.2.2 Second-order edge-detection operators; 4.2.2.1 Motivation; 4.2.2.2 Basic operators: the Laplacian; 4.2.2.3 The Marr-Hildreth operator; 4.2.3 Other edge-detection operators; 4.2.4 Comparison of edge-detection operators; 4.2.5 Further reading on edge detection; 4.3 Phase congruency; 4.4 Localized feature extraction; 4.4.1 Detecting image curvature (corner extraction); 4.4.1.1 Definition of curvature; 4.4.1.2 Computing differences in edge direction; 4.4.1.3 Measuring curvature by changes in intensity (differentiation) 4.4.1.4 Moravec and Harris detectors |
| Record Nr. | UNINA-9910790326903321 |
|
Nixon Mark S
|
||
| Oxford, : Academic, 2012 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Feature extraction & image processing for computer vision / / Mark S. Nixon, Alberto S. Aguado
| Feature extraction & image processing for computer vision / / Mark S. Nixon, Alberto S. Aguado |
| Autore | Nixon Mark S |
| Edizione | [3rd ed.] |
| Pubbl/distr/stampa | Oxford, : Academic, 2012 |
| Descrizione fisica | 1 online resource (628 p.) |
| Disciplina | 006.37 |
| Altri autori (Persone) |
AguadoAlberto S
NixonMark S |
| Soggetto topico |
Computer vision
Computer vision - Mathematics Pattern recognition systems Image processing - Digital techniques |
| ISBN | 0-12-397824-6 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Front Cover; Feature Extraction & Image Processing for Computer Vision; Copyright page; Contents; Preface; What is new in the third edition?; Why did we write this book?; The book and its support; In gratitude; Final message; About the authors; 1 Introduction; 1.1 Overview; 1.2 Human and computer vision; 1.3 The human vision system; 1.3.1 The eye; 1.3.2 The neural system; 1.3.3 Processing; 1.4 Computer vision systems; 1.4.1 Cameras; 1.4.2 Computer interfaces; 1.4.3 Processing an image; 1.5 Mathematical systems; 1.5.1 Mathematical tools; 1.5.2 Hello Matlab, hello images!; 1.5.3 Hello Mathcad!
1.6 Associated literature1.6.1 Journals, magazines, and conferences; 1.6.2 Textbooks; 1.6.3 The Web; 1.7 Conclusions; 1.8 References; 2 Images, sampling, and frequency domain processing; 2.1 Overview; 2.2 Image formation; 2.3 The Fourier transform; 2.4 The sampling criterion; 2.5 The discrete Fourier transform; 2.5.1 1D transform; 2.5.2 2D transform; 2.6 Other properties of the Fourier transform; 2.6.1 Shift invariance; 2.6.2 Rotation; 2.6.3 Frequency scaling; 2.6.4 Superposition (linearity); 2.7 Transforms other than Fourier; 2.7.1 Discrete cosine transform; 2.7.2 Discrete Hartley transform 2.7.3 Introductory wavelets2.7.3.1 Gabor wavelet; 2.7.3.2 Haar wavelet; 2.7.4 Other transforms; 2.8 Applications using frequency domain properties; 2.9 Further reading; 2.10 References; 3 Basic image processing operations; 3.1 Overview; 3.2 Histograms; 3.3 Point operators; 3.3.1 Basic point operations; 3.3.2 Histogram normalization; 3.3.3 Histogram equalization; 3.3.4 Thresholding; 3.4 Group operations; 3.4.1 Template convolution; 3.4.2 Averaging operator; 3.4.3 On different template size; 3.4.4 Gaussian averaging operator; 3.4.5 More on averaging; 3.5 Other statistical operators 3.5.1 Median filter3.5.2 Mode filter; 3.5.3 Anisotropic diffusion; 3.5.4 Force field transform; 3.5.5 Comparison of statistical operators; 3.6 Mathematical morphology; 3.6.1 Morphological operators; 3.6.2 Gray-level morphology; 3.6.3 Gray-level erosion and dilation; 3.6.4 Minkowski operators; 3.7 Further reading; 3.8 References; 4 Low-level feature extraction (including edge detection); 4.1 Overview; 4.2 Edge detection; 4.2.1 First-order edge-detection operators; 4.2.1.1 Basic operators; 4.2.1.2 Analysis of the basic operators; 4.2.1.3 Prewitt edge-detection operator 4.2.1.4 Sobel edge-detection operator4.2.1.5 The Canny edge detector; 4.2.2 Second-order edge-detection operators; 4.2.2.1 Motivation; 4.2.2.2 Basic operators: the Laplacian; 4.2.2.3 The Marr-Hildreth operator; 4.2.3 Other edge-detection operators; 4.2.4 Comparison of edge-detection operators; 4.2.5 Further reading on edge detection; 4.3 Phase congruency; 4.4 Localized feature extraction; 4.4.1 Detecting image curvature (corner extraction); 4.4.1.1 Definition of curvature; 4.4.1.2 Computing differences in edge direction; 4.4.1.3 Measuring curvature by changes in intensity (differentiation) 4.4.1.4 Moravec and Harris detectors |
| Altri titoli varianti | Feature extraction and image processing for computer vision |
| Record Nr. | UNINA-9910969419303321 |
|
Nixon Mark S
|
||
| Oxford, : Academic, 2012 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Markov random fields for vision and image processing [[electronic resource] /] / edited by Andrew Blake, Pushmeet Kohli, and Carsten Rother
| Markov random fields for vision and image processing [[electronic resource] /] / edited by Andrew Blake, Pushmeet Kohli, and Carsten Rother |
| Pubbl/distr/stampa | Cambridge, Mass., : MIT Press, c2011 |
| Descrizione fisica | 1 online resource (472 p.) |
| Disciplina | 006.3/70151 |
| Altri autori (Persone) |
BlakeAndrew <1956->
KohliPushmeet RotherCarsten |
| Soggetto topico |
Image processing - Mathematics
Computer graphics - Mathematics Computer vision - Mathematics Markov random fields |
| Soggetto genere / forma | Electronic books. |
| ISBN |
1-283-25865-X
9786613258656 0-262-29835-X |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover ; Contents; 1 Introduction to Markov Random Fields; I Algorithms for Inference of MAP Estimates for MRFs; 2 Basic Graph Cut Algorithms; 3 Optimizing Multilabel MRFs Using Move-Making Algorithms; 4 Optimizing Multilabel MRFs with Convex and Truncated Convex Priors; 5 Loopy Belief Propagation, Mean Field Theory, and Bethe Approximations; 6 Linear Programming and Variants of Belief Propagation; II Applications of MRFs, including Segmentation; 7 Interactive Foreground Extraction; 8 Continuous-Valued MRF for Image Segmentation; 9 Bilayer Segmentation of Video
10 MRFs for Superresolution and Texture Synthesis11 A Comparative Study of Energy Minimization Methods for MRFs; III Further Topics: Inference, Parameter Learning, and Continuous Models; 12 Convex Relaxation Techniques for Segmentation, Stereo, and Multiview Reconstruction; 13 Learning Parameters in Continuous-Valued Markov Random Fields; 14 Message Passing with Continuous Latent Variables; 15 Learning Large-Margin Random Fields Using Graph Cuts; 16 Analyzing Convex Relaxations for MAP Estimation; 17 MAP Inference by Fast Primal-Dual Linear Programming 18 Fusion-Move Optimization for MRFs with an Extensive Label SpaceIV Higher-Order MRFs and Global Constraints; 19 Field of Experts; 20 Enforcing Label Consistency Using Higher-Order Potentials; 21 Exact Optimization for Markov Random Fields with Nonlocal Parameters; 22 Graph Cut-Based Image Segmentation with Connectivity Priors; V Advanced Applications of MRFs; 23 Symmetric Stereo Matching for Occlusion Handling; 24 Steerable Random Fields for Image Restoration; 25 Markov Random Fields for Object Detection; 26 SIFT Flow; 27 Unwrap Mosaics; Bibliography; Contributors; Index |
| Record Nr. | UNINA-9910456890503321 |
| Cambridge, Mass., : MIT Press, c2011 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Markov random fields for vision and image processing / / edited by Andrew Blake, Pushmeet Kohli, and Carsten Rother
| Markov random fields for vision and image processing / / edited by Andrew Blake, Pushmeet Kohli, and Carsten Rother |
| Pubbl/distr/stampa | Cambridge, Mass., : MIT Press, ©2011 |
| Descrizione fisica | 1 online resource (472 p.) |
| Disciplina | 006.3/70151 |
| Altri autori (Persone) |
BlakeAndrew <1956->
KohliPushmeet RotherCarsten |
| Soggetto topico |
Image processing - Mathematics
Computer graphics - Mathematics Computer vision - Mathematics Markov random fields |
| Soggetto non controllato |
NEUROSCIENCE/Visual Neuroscience
COMPUTER SCIENCE/General |
| ISBN |
1-283-25865-X
9786613258656 0-262-29835-X |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover ; Contents; 1 Introduction to Markov Random Fields; I Algorithms for Inference of MAP Estimates for MRFs; 2 Basic Graph Cut Algorithms; 3 Optimizing Multilabel MRFs Using Move-Making Algorithms; 4 Optimizing Multilabel MRFs with Convex and Truncated Convex Priors; 5 Loopy Belief Propagation, Mean Field Theory, and Bethe Approximations; 6 Linear Programming and Variants of Belief Propagation; II Applications of MRFs, including Segmentation; 7 Interactive Foreground Extraction; 8 Continuous-Valued MRF for Image Segmentation; 9 Bilayer Segmentation of Video
10 MRFs for Superresolution and Texture Synthesis11 A Comparative Study of Energy Minimization Methods for MRFs; III Further Topics: Inference, Parameter Learning, and Continuous Models; 12 Convex Relaxation Techniques for Segmentation, Stereo, and Multiview Reconstruction; 13 Learning Parameters in Continuous-Valued Markov Random Fields; 14 Message Passing with Continuous Latent Variables; 15 Learning Large-Margin Random Fields Using Graph Cuts; 16 Analyzing Convex Relaxations for MAP Estimation; 17 MAP Inference by Fast Primal-Dual Linear Programming 18 Fusion-Move Optimization for MRFs with an Extensive Label SpaceIV Higher-Order MRFs and Global Constraints; 19 Field of Experts; 20 Enforcing Label Consistency Using Higher-Order Potentials; 21 Exact Optimization for Markov Random Fields with Nonlocal Parameters; 22 Graph Cut-Based Image Segmentation with Connectivity Priors; V Advanced Applications of MRFs; 23 Symmetric Stereo Matching for Occlusion Handling; 24 Steerable Random Fields for Image Restoration; 25 Markov Random Fields for Object Detection; 26 SIFT Flow; 27 Unwrap Mosaics; Bibliography; Contributors; Index |
| Record Nr. | UNINA-9910781666803321 |
| Cambridge, Mass., : MIT Press, ©2011 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Optimization for computer vision : an introduction to core concepts and methods / / Marco Alexander Treiber
| Optimization for computer vision : an introduction to core concepts and methods / / Marco Alexander Treiber |
| Autore | Treiber Marco Alexander |
| Edizione | [1st ed. 2013.] |
| Pubbl/distr/stampa | New York, : Springer, 2013 |
| Descrizione fisica | 1 online resource (xi, 257 pages) : illustrations (some color) |
| Disciplina | 006.37 |
| Collana | Advances in computer vision and pattern recognition |
| Soggetto topico |
Computer vision - Mathematics
Mathematical optimization |
| ISBN | 1-4471-5283-2 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Introduction -- Continuous Optimization -- Linear Programming and the Simplex Method -- Variational Methods -- Correspondence Problems -- Graph Cuts -- Dynamic Programming. |
| Record Nr. | UNINA-9910437579303321 |
|
Treiber Marco Alexander
|
||
| New York, : Springer, 2013 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
