OpenCV

Learn to use cv.kmeans() function in OpenCV for data clustering. We cover single feature, multi-feature data and color quantization of images using K-Means.

Learn the concepts of K-Means Clustering algorithm. We go through the step-by-step algorithm using a T-shirt size problem to explain how iterative centroid updates work.

Learn to use SVM with HOG (Histogram of Oriented Gradients) features for OCR of handwritten digits. This approach achieves ~94% accuracy, better than kNN with raw pixels.

Learn an intuitive understanding of Support Vector Machines (SVM). We cover decision boundaries, support vectors, margin maximization, and kernel trick for non-linearly separable data.

Learn to build a basic OCR application using kNN knowledge. We use OpenCV's digits.png dataset to recognize handwritten digits achieving ~91% accuracy.

Learn the concepts of the k-Nearest Neighbour (kNN) algorithm. We cover classification, feature space, weighted kNN, and implement a simple example with OpenCV.

Learn to create a depth map from stereo images. We cover cv.StereoBM.create() for disparity map computation and the parameters for tuning the results.

Learn the basics of multiview geometry. We cover epipole, epipolar lines, epipolar constraint, Fundamental Matrix and Essential Matrix, with OpenCV code to find and draw epilines.

Learn to exploit the calib3d module to create 3D effects in images. We cover cv.solvePnP() and cv.projectPoints() to draw 3D coordinate axes and a cube on a chessboard.