This video lecture introduces Supervised and Unsupervised classification methods and details the K- Nearest Neighbour Classifier and K - means clustering methods, as respective examples for them.
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CV Lecture 5 Histogram Equalization
CV Lecture 4: Image Enhancement
CV Lecture 3: Course Overview and Evolution of Computer Vision
CV Lecture 2b: Overview of the Course
CV Lecture 2a: Introduction to Computer Vision
CV Lecture 1 : Introduction to Computer Vision
DLCV Lecture 22 : Introduction to Neural Networks
DLCV Lecture 21: Disparity Estimation and Depth Extraction from Stereo Images
DLCV Lecture 20: 3D Reconstruction and Depth from Stereo
DLCV Lecture 19: Image Segmentation
DLCV Lecture 18: Bag of Visual Words
DLCV Lecture 16: SIFT features and Introduction to Bag of Visual Words
DLCV Lecture -17 : Supervised and Unsupervised Classification
DLCV L-15: SIFT Key point description & orientation assignment
Lecture 14: SIFT Key point localization
Lecture 13 : Scale Invariant Feature Transform
Lecture 12: Viola Jones Face Detection
Lecture 11 : HoG Feature Extraction and Image Stitching
Lecture 9: Interest point localization by Harris Corner Detection
Lecture 10: Harris Detector and HoG for Feature Extraction
Lecture 8: Convolution - Correlation for Feature Extraction
Lecture 7 : Importance and Overview of Feature Extraction in CV tasks
Lecture 6 : Understanding Cross Entropy and KL Divergence loss functions with Examples
Lecture 5: Derivatives wrt Vector, Matrices and Cross Entropy