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Python 3.x for Computer Vision
Introduction to Image Processing
The Course Overview (3:18)
Image Processing and Its Applications (2:56)
Image Processing Libraries - Pillow (6:11)
Geometrical Transformation - Pillow (3:45)
Introduction to scikit-image (6:42)
Filters and Features
Image Derivatives (3:06)
Understanding Image Filters (4:06)
Custom Filters and Image Thresholding (2:45)
Edge Detection (4:31)
Object Detection
Harris Corner Detection (2:14)
Local Binary Patterns (3:47)
Oriented FAST and Rotated BRIEF (ORB) (3:30)
Image Stitching (3:32)
Segmentation – Understanding Images Better
Contour Detection and the Watershed Algorithm (4:57)
Superpixels and Normalized Graph Cut (5:19)
ntegrating Machine Learning with Computer Vision
Introduction to Machine Learning (4:55)
Logistic Regression (4:27)
Support Vector Machines (4:01)
K-means Clustering (3:14)
Image Classification Using Neural Networks
Introduction to Neural Network (2:17)
MNIST Digit Classification Using Neural Networks (5:10)
Convolutional Neural Networks (5:01)
Image Derivatives
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