Feature extraction and image processing
Whilst other books cover a broad range of topics, Feature Extraction and Image Processing takes one of the prime targets of applied computer vision, feature extraction, and uses it to provide an essential guide to the implementation of image processing and computer vision techniques. Acting as both...
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| Otros Autores: | |
| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
Amsterdam ; Boston :
Elsevier/Academic Press
2008.
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| Edición: | 2nd ed |
| Materias: | |
| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009627625806719 |
Tabla de Contenidos:
- Cover; Title page; Copyright Page; Table of Contents; Preface; Chapter 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 Mathcad, hello images!; 1.5.3 Hello Matlab!; 1.6 Associated literature; 1.6.1 Journals and magazines; 1.6.2 Textbooks; 1.6.3 The web; 1.7 Conclusions; 1.8 References
- Chapter 2 Images, sampling and frequency domain processing2.1 Overview; 2.2 Image formation; 2.3 The Fourier transform; 2.4 The sampling criterion; 2.5 The discrete Fourier transform; 2.5.1 One-dimensional transform; 2.5.2 Two-dimensional 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 wavelets: the Gabor wavelet; 2.7.4 Other transforms
- 2.8 Applications using frequency domain properties2.9 Further reading; 2.10 References; Chapter 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; 3.5 Other statistical operators; 3.5.1 More on averaging; 3.5.2 Median filter; 3.5.3 Mode filter; 3.5.4 Anisotropic diffusion; 3.5.5 Force field transform
- 3.5.6 Comparison of statistical operators3.6 Mathematical morphology; 3.6.1 Morphological operators; 3.6.2 Grey-level morphology; 3.6.3 Grey-level erosion and dilation; 3.6.4 Minkowski operators; 3.7 Further reading; 3.8 References; Chapter 4 Low-level feature extraction (including edge detection); 4.1 Overview; 4.2 First order edge detection operators; 4.2.1 Basic operators; 4.2.2 Analysis of the basic operators; 4.2.3 Prewitt edge detection operator; 4.2.4 Sobel edge detection operator; 4.2.5 Canny edge detection operator; 4.3 Second order edge detection operators; 4.3.1 Motivation
- 4.3.2 Basic operators: the Laplacian4.3.3 Marr-Hildreth operator; 4.4 Other edge detection operators; 4.5 Comparison of edge detection operators; 4.6 Further reading on edge detection; 4.7 Phase congruency; 4.8 Localized feature extraction; 4.8.1 Detecting image curvature (corner extraction); 4.8.1.1 Definition of curvature; 4.8.1.2 Computing differences in edge direction; 4.8.1.3 Measuring curvature by changes in intensity (differentiation); 4.8.1.4 Moravec and Harris detectors; 4.8.1.5 Further reading on curvature; 4.8.2 Modern approaches: region/patch analysis
- 4.8.2.1 Scale invariant feature transform
