| Description |
1 online resource (419 pages) |
| Contents |
Intro; Preface; Acknowledgements; Contents; Introduction; 1 Computer Vision and Machine Learning; 2 Python and Mathematica; References; Chapter 1: Dimension Reduction; 1.1 Principal Component Analysis; Basic theory; 1.1.1 Principal Component; 1.1.2 Singular Value Decomposition; 1.1.3 Karhunen-Loeve Decomposition; 1.1.4 PCA and Total Least Square; 1.1.5 Image Compression; 1.1.6 Color Image Compression; 1.1.7 Image Compression in Python; 1.2 Independent Component Analysis; Basic theory; 1.2.1 Independent Component Analysis; 1.2.2 Image Compression via ICA; Mathematica; Python |
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1.3 Discrete Fourier TransformBasic theory; 1.3.1 Data Compression via DFT; 1.3.2 DFT Image Compression; 1.4 Discrete Wavelet Transform; Basic theory; 1.4.1 Concept of Discrete Wavelet Transform; 1.4.2 2D Discrete Wavelet Transform; 1.4.3 DWT Image Compression; 1.5 Radial Basis Function; Basic theory; 1.5.1 RBF Approximation; 1.5.2 RBF Image Compression; 1.6 AutoEncoding; Basic theory; 1.6.1 Concept of AutoEncoding; 1.6.2 Simple Example; 1.6.3 Compression of Image; 1.7 Fractal Compression; Basic theory; 1.7.1 Concept of Fractal Compression; 1.7.2 Illustrative Example; Mathematica |
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1.7.3 Image Compression with Python1.7.4 Accelerating Fractal Code Book Computation; 1.8 Comparison of Dimension Reduction Methods; 1.8.1 Measure of Image Quality; 1.8.2 Comparing Different Images; 1.8.3 Compression of Mandala; PCA method; Discrete Wavelet Transform; References; Chapter 2: Classification; 2.1 KNearest Neighbors Classification; Basic theory; 2.1.1 Small Data Set; Mathematica; Python; Mathematica; Python; 2.1.2 Vacant and Residential Lands; Vacant Land; Mathematica; Python; 2.2 Logistic Regression; Basic theory; 2.2.1 Iris Data Set; Python; Mathematica; 2.2.2 Digit Recognition |
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MathematicaPython; 2.3 Tree Based Methods; Basic theory; 2.3.1 Playing Tennis Today?; Mathematica; 2.3.2 Snowmen and Dice; Mathematica; Python; 2.4 Support Vector Classification; Basic theory; 2.4.1 Margin maximization; Mathematica; Python; 2.4.2 Feature Space Mapping; Mathematica; Python; 2.4.3 Learning Chess Board Fields; Mathematica; Python; 2.5 Naive Bayes Classifier; Basic theory; 2.5.1 Playing Tennis Today?; Mathematica; Python; 2.5.2 Zebra, Gorilla, Horse and Penguin; Mathematica; Python; 2.6 Comparison of Classification Methods; References; Chapter 3: Clustering; 3.1 KMeans Clustering |
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Basic theory3.1.1 Small Data Set; Python; Mathematica; 3.1.2 Clustering Images; Mathematica; 3.2 Hierarchical Clustering; Basic theory; 3.2.1 Dendrogram for Small Data Set; Python; 3.2.2 Image Segmentation; Mathematica; Python; 3.3 Density-Based Spatial Clustering of Applications with Noise; Basic theory; 3.3.1 Data Set Moons; Mathematica; Python; 3.3.2 Segmentation of MRI of Brain; 3.4 Spectral Clustering; Basic theory; 3.4.1 Nonlinear Data Set Moons; Mathematica; Python; 3.4.2 Image Coloring; 3.5 Comparison of Clustering Methods; 3.5.1 Measurement of Quality of Cluster Analysis |
| Summary |
The book introduces the latest methods and algorithms developed in machine and deep learning (hybrid symbolic-numeric computations, robust statistical techniques for clustering and eliminating data as well as convolutional neural networks) dealing not only with images and the use of computers, but also their applications to visualization tasks generalized by up-to-date points of view. Associated algorithms are deposited on iCloud |
| Notes |
3.5.2 Optimal Number of Clusters |
| Bibliography |
Includes bibliographical references |
| Notes |
Print version record |
| Subject |
Computer vision.
|
|
Computer vision
|
| Form |
Electronic book
|
| Author |
Paláncz, Béla, 1944-
|
|
Völgyesi, Lajos.
|
| ISBN |
9783030261535 |
|
3030261530 |
|
