实例介绍
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Table of Contents Preface What this book covers What you need for this book Who this book is for Conventions Reader feedback Customer support Downloading the example code Downloading the color images of this book Errata Piracy Questions 1. Introduction to Image Processing Image processing - its applications Image processing libraries Pillow Installation Getting started with pillow Reading an image Writing or saving an image Cropping an image Changing between color spaces Geometrical transformation Image enhancement Introduction to scikit-image Installation Getting started with scikit-image Summary 2. Filters and Features Image derivatives Kernels Convolution Understanding image filters Gaussian blur Median filter Dilation and erosion Erosion Dilation Custom filters Image thresholding Edge detection Sobel edge detector Why have pixels with large gradient values? Canny edge detector Hough line Hough circle Summary 3. Drilling Deeper into Features - Object Detection Revisiting image features Harris corner detection Local Binary Patterns Oriented FAST and Rotated BRIEF (ORB) oFAST – FAST keypoint orientation FAST detector Orientation by intensity centroid rBRIEF – Rotation-aware BRIEF Steered BRIEF Variance and correlation Image stitching Summary 4. Segmentation - Understanding Images Better Introduction to segmentation Contour detection The Watershed algorithm Superpixels Normalized graph cut Summary 5. Integrating Machine Learning with Computer Vision Introduction to machine learning Data preprocessing Image translation through random cropping Image rotation and scaling Scikit-learn (sklearn) Applications of machine learning for computer vision Logistic regression Support vector machines K-means clustering Summary 6. Image Classification Using Neural Networks Introduction to neural networks Design of a basic neural network Training a network MNIST digit classification using neural networks Playing with hidden layers Convolutional neural networks Challenges in machine learning Summary 7. Introduction to Computer Vision using OpenCV Installation macOS Windows Linux OpenCV APIs Reading an image Writing/saving the image Changing the color space Scaling Cropping the image Translation Rotation Thresholding Filters Gaussian blur Median blur Morphological operations Erosion Dilation Edge detection Sobel edge detection Canny edge detector Contour detection Template matching Summary 8. Object Detection Using OpenCV Haar Cascades Integral images Scale Invariant Feature Transformation (SIFT) Algorithm behind SIFT Scale-space extrema detection Keypoint localization Orientation assignment Keypoint descriptor Speeded up robust features Detecting SURF keypoints SURF keypoint descriptors Orientation assignment Descriptor based on Haar wavelet response Summary 9. Video Processing Using OpenCV Reading/writing videos Reading a video Writing a video Basic operations on videos Converting to grayscale Color tracking Object tracking Kernelized Correlation Filter (KCF) Lucas Kanade Tracker (LK Tracker) Summary 10. Computer Vision as a Service Computer vision as a service – architecture overview Environment setup http-server virtualenv flask Developing a server-client model Client Server Computer vision engine Putting it all together Client Server Summary
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