Programming Computer Vision with Python: Tools and algorithms for analyzing imagesIf you want a basic understanding of computer vision’s underlying theory and algorithms, this hands-on introduction is the ideal place to start. You’ll learn techniques for object recognition, 3D reconstruction, stereo imaging, augmented reality, and other computer vision applications as you follow clear examples written in Python. Programming Computer Vision with Python explains computer vision in broad terms that won’t bog you down in theory. You get complete code samples with explanations on how to reproduce and build upon each example, along with exercises to help you apply what you’ve learned. This book is ideal for students, researchers, and enthusiasts with basic programming and standard mathematical skills.
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Contents
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Local Image Descriptors | 29 |
Image to Image Mappings | 53 |
Camera Models and Augmented Reality | 79 |
Multiple View Geometry | 99 |
Clustering Images | 127 |
Searching Images | 147 |
Classifying Image Content | 167 |
Image Segmentation | 191 |
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3D points add the following affine transform algorithm aligned augmented reality axis('off Bayes classifier binary camera center camera matrix chapter CherryPy class_2 clustering color computer vision contains create data set database de-noising distance edges eigenvectors epipole equation estimated example feature vector filenames filters following function fundamental matrix Gaussian gradient graph cut graylevel grayscale Here’s histogram homogeneous coordinates homography imlist imname implementation inliers input install JSON k-means labels load look matches Matplotlib method module nodes NumPy NumPy arrays object OpenCV OpenGL optical flow original image package parameters pickle pickle.load(f pixel plane plot point correspondences projection PyGame PyLab Python query RANSAC regions resize rotation sample scale SciPy script segmentation SIFT features Spectral clustering SQLite stereo Sudoku threshold tracking training data triangulation values visual vocabulary word