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Practical Machine Learning and Image Processing.pdf

发布时间：2022-06-19 发布人：admin 分类：说明书 资料大小：4.78M 资料格式：pdf 举报 版权申诉
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    Table of Contents
    About the Author
    About the Technical Reviewer
    Acknowledgments
    Introduction
    Chapter 1: Setup Environment
    Install Anaconda
    Windows
    macOS
    Ubuntu
    Install OpenCV
    Install Keras
    Test the Installations
    Virtual Environments
    Chapter 2: Introduction to Image Processing
    Images
    Pixels
    Image Resolution
    PPI and DPI
    Bitmap Images
    Lossless Compression
    Lossy Compression
    Image File Formats
    Color Spaces
    RGB
    XYZ
    HSV/HSL
    LAB
    LCH
    YPbPr
    YUV
    YIQ
    Advanced Image Concepts
    Bezier Curve
    Ellipsoid
    Gamma Correction
    Structural Similarity Index
    Deconvolution
    Homography
    Convolution
    Chapter 3: Basics of Python and Scikit Image
    Basics of Python
    Variables and Data Types
    Data Structures
    Lists
    Dictionaries
    Tuples
    Control Flow Statements
    Conditional Statements
    Functions
    Scikit Image
    Uploading and Viewing an Image
    Getting Image Resolution
    Looking at Pixel Values
    Converting Color Space
    RGB to HSV and Vice Versa
    RGB to XYZ and Vice Versa
    RGB to LAB and Vice Versa
    RGB to YUV and Vice Versa
    RGB to YIQ and Vice Versa
    RGB to YPbPr and Vice Versa
    Saving an Image
    Creating Basic Drawings
    Lines
    Rectangles
    Circles
    Bezier Curve
    Doing Gamma Correction
    Rotating, Shifting, and Scaling Images
    Determining Structural Similarity
    Chapter 4: Advanced Image Processing Using OpenCV
    Blending Two Images
    Changing Contrast and Brightness
    Adding Text to Images
    Smoothing Images
    Median Filter
    Gaussian Filter
    Bilateral Filter
    Changing the Shape of Images
    Effecting Image Thresholding
    Calculating Gradients
    Performing Histogram Equalization
    Chapter 5: Image Processing Using Machine Learning
    Feature Mapping Using the SIFT Algorithm
    Step 1: Space Construction
    Step 2: Difference between the Gaussians
    Step 3: Important Points
    Step 4: Unimportant Key Points
    Step 5: Orientation of Key Points
    Step 6: Key Features
    Image Registration Using the RANSAC Algorithm
    estimate_affine
    residual_lengths
    Processing the Images
    The Complete Code
    Image Classification Using Artificial Neural Networks
    Image Classification Using CNNs
    Image Classification Using Machine Learning Approaches
    Decision Trees
    Support Vector Machines
    Logistic Regression
    Code
    Important Terms
    Chapter 6: Real-time Use Cases
    Finding Palm Lines
    Detecting Faces
    Recognizing Faces
    Tracking Movements
    Detecting Lanes
    Appendix: Important Concepts and Terminology
    Adaboost
    XGBoost
    Pulse-coupled Neural Networks
    Gradient Descent
    Stochastic Gradient Descent
    AdaDelta
    Canny Edge Detector
    Sobel Transformation
    Haar Cascade
    LBPH Face Recognition
    Image Moments
    Image Contours
    Chessboard Corners Function
    Calibrate Camera Function
    Perspective Transformation Function
    Index
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