Singh Practical Machine Learning and Image Processing

For Facial Recognition, Object Detection, and Pattern Recognition Using Python
1. ed
ISBN: 978-1-4842-4149-3
Verlag: Apress
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 177 Seiten

Gain insights into image-processing methodologies and algorithms, using machine learning and neural networks in Python. This book begins with the environment setup, understanding basic image-processing terminology, and exploring Python concepts that will be useful for implementing the algorithms discussed in the book. You will then cover all the core image processing algorithms in detail before moving onto the biggest computer vision library: OpenCV. You'll see the OpenCV algorithms and how to use them for image processing. 
The next section looks at advanced machine learning and deep learning methods for image processing and classification. You'll work with concepts such as pulse coupled neural networks, AdaBoost, XG boost, and convolutional neural networks for image-specific applications. Later you'll explore how models are made in real time and then deployed using various DevOps tools. 
All the concepts in Practical Machine Learning and Image Processing are explained using real-life scenarios. After reading this book you will be able to apply image processing techniques and make machine learning models for customized application.
What You Will LearnDiscover image-processing algorithms and their applications using Python
Explore image processing using the OpenCV library
Use TensorFlow, scikit-learn, NumPy, and other libraries
Work with machine learning and deep learning algorithms for image processing
Apply image-processing techniques to five real-time projects

Who This Book Is For
Data scientists and software developers interested in image processing and computer vision.


Himanshu Singh has more than five years of experience as a data science professional. Currently he is senior data scientist at Unify Technologies Private Limited. He gives corporate training on data science, ML, and DL. He's also a visiting faculty for analytics at the Narsee Monjee Institute of Management Studies, considered one of the premium management institutes in India. He is founder of Black Feathers Analytics and Rise of Literati Clubs.

Autoren/Hrsg.

Singh, Himanshu

Inhaltsverzeichnis

1;Table of Contents;4
2;About the Author;9
3;About the Technical Reviewer;10
4;Acknowledgments;11
5;Introduction;12
6;Chapter 1: Setup Environment;13
6.1;Install Anaconda;13
6.1.1;Windows;14
6.1.2;macOS;16
6.1.3;Ubuntu;16
6.2;Install OpenCV;16
6.3;Install Keras;17
6.4;Test the Installations;17
6.5;Virtual Environments;18
7;Chapter 2: Introduction to Image Processing;19
7.1;Images;20
7.2;Pixels;20
7.3;Image Resolution;21
7.4;PPI and DPI;22
7.5;Bitmap Images;22
7.6;Lossless Compression;23
7.7;Lossy Compression;24
7.8;Image File Formats;24
7.9;Color Spaces;25
7.9.1;RGB;26
7.9.2;XYZ;27
7.9.3;HSV/HSL;29
7.9.4;LAB;30
7.9.5;LCH;30
7.9.6;YPbPr;31
7.9.7;YUV;32
7.9.8;YIQ;33
7.10;Advanced Image Concepts;33
7.10.1;Bezier Curve;34
7.10.2;Ellipsoid;35
7.10.3;Gamma Correction;36
7.10.4;Structural Similarity Index;37
7.10.5;Deconvolution;37
7.10.6;Homography;38
7.10.7;Convolution;39
8;Chapter 3: Basics of Python and Scikit Image;40
8.1;Basics of Python;41
8.1.1;Variables and Data Types;41
8.1.2;Data Structures;44
8.1.2.1;Lists;44
8.1.2.2;Dictionaries;44
8.1.2.3;Tuples;45
8.1.3;Control Flow Statements;45
8.1.4;Conditional Statements;48
8.1.5;Functions;49
8.2;Scikit Image;51
8.2.1;Uploading and Viewing an Image;52
8.2.2;Getting Image Resolution;53
8.2.3;Looking at Pixel Values;54
8.2.4;Converting Color Space;54
8.2.4.1;RGB to HSV and Vice Versa;55
8.2.4.2;RGB to XYZ and Vice Versa;56
8.2.4.3;RGB to LAB and Vice Versa;58
8.2.4.4;RGB to YUV and Vice Versa;59
8.2.4.5;RGB to YIQ and Vice Versa;61
8.2.4.6;RGB to YPbPr and Vice Versa;62
8.2.5;Saving an Image;64
8.2.6;Creating Basic Drawings;64
8.2.6.1;Lines;64
8.2.6.2;Rectangles;65
8.2.6.3;Circles;66
8.2.6.4;Bezier Curve;67
8.2.7;Doing Gamma Correction;68
8.3;Rotating, Shifting, and Scaling Images;70
8.4;Determining Structural Similarity;71
9;Chapter 4: Advanced Image Processing Using OpenCV;73
9.1;Blending Two Images;74
9.2;Changing Contrast and Brightness;76
9.3;Adding Text to Images;78
9.4;Smoothing Images;81
9.4.1;Median Filter;81
9.4.2;Gaussian Filter;81
9.4.3;Bilateral Filter;82
9.5;Changing the Shape of Images;85
9.6;Effecting Image Thresholding;90
9.7;Calculating Gradients;94
9.8;Performing Histogram Equalization;97
10;Chapter 5: Image Processing Using Machine Learning;99
10.1;Feature Mapping Using the SIFT Algorithm;100
10.1.1;Step 1: Space Construction;101
10.1.2;Step 2: Difference between the Gaussians;101
10.1.3;Step 3: Important Points;102
10.1.4;Step 4: Unimportant Key Points;102
10.1.5;Step 5: Orientation of Key Points;102
10.1.6;Step 6: Key Features;103
10.2;Image Registration Using the RANSAC Algorithm;108
10.2.1;estimate_affine;115
10.2.2;residual_lengths;115
10.2.3;Processing the Images;116
10.2.4;The Complete Code;116
10.3;Image Classification Using Artificial Neural Networks;120
10.4;Image Classification Using CNNs;128
10.5;Image Classification Using Machine Learning Approaches;135
10.5.1;Decision Trees;136
10.5.2;Support Vector Machines;137
10.5.3;Logistic Regression;137
10.5.4;Code;137
10.6;Important Terms;140
11;Chapter 6: Real-time Use Cases;143
11.1;Finding Palm Lines;143
11.2;Detecting Faces;145
11.3;Recognizing Faces;148
11.4;Tracking Movements;151
11.5;Detecting Lanes;153
12;Appendix: Important Concepts and Terminology;160
12.1;Adaboost;160
12.2;XGBoost;161
12.3;Pulse-coupled Neural Networks;162
12.4;Gradient Descent;163
12.5;Stochastic Gradient Descent;164
12.6;AdaDelta;165
12.7;Canny Edge Detector;165
12.8;Sobel Transformation;166
12.9;Haar Cascade;167
12.10;LBPH Face Recognition;167
12.11;Image Moments;167
12.12;Image Contours;168
12.13;Chessboard Corners Function;169
12.14;Calibrate Camera Function;170
12.15;Perspective Transformation Function;171
13;Index;173