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Lecture 1.1: Introduction to Computer Vision [Basics]
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Classroom Contents
Computer Vision - Fall 2022
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- 1 Lecture 1.1: Introduction to Computer Vision [Basics]
- 2 Lecture 1.2: Introduction to Computer Vision [Digital Image]
- 3 Lecture 1.3: Introduction to Computer Vision [Motivation and Applications]
- 4 Lecture 1.4: Introduction to Computer Vision [Q&A]
- 5 Lecture 2.1: Linear Algebra Basics
- 6 Lecture 2.2: Linear Algebra Basics [Transformations]
- 7 Lecture 2: Part 2 Q&A 1
- 8 Lecture 3.1: Image Filtering [Histogram]
- 9 Lecture 3.2: Image FIltering [DIgitization]
- 10 Lecture 3.3: Image Filtering [Noise]
- 11 Lecture 3.4: Image Filtering [Filtering]
- 12 Lecture 3.5: Image Filtering [Image Derivates]
- 13 Lecture 4.1: Edge Detection
- 14 Lecture 4.2: Edge Detection [Prewitt and Sobel Edge Detection]
- 15 Lecture 4.3: Edge Detection [Marr Hilreth Edge Detection]
- 16 Lecture 4.4: Edge Detection [Canny Edge Detection]
- 17 Lecture 3.6: Image Filtering [Q&A]
- 18 Lecture 5.1: Introduction to Neural Networks
- 19 Lecture 5.2: Introduction to Neural Networks [Neural Network basics]
- 20 Lecture 5.3: Introduction to Neural Networks [Non-Linearity]
- 21 Lecture 4.5: Edge Detection [Q&A]
- 22 Lecture 5.4: Introduction to Neural Networks [Q&A]
- 23 Lecture 6.1: Introduction to Convolutional Neural Networks
- 24 Lecture 6.2: Introduction to Convolutional Neural Networks Fundamental Operation
- 25 Lecture 6.3: Introduction to Convolutional Neural Networks Fundamental Operation
- 26 Lecture 6.4: Introduction to Convolutional Neural Networks Practical Considerations
- 27 Lecture 6.5: Introduction to Convolutional Neural Networks Case Study
- 28 Lecture 6: Q & A
- 29 Lecture 6 Q&A
- 30 Lecture 7.1: Training Neural Networks
- 31 Lecture 7.2: Training Neural Networks [Backpropagation]
- 32 Lecture 7.3: Training Neural Networks [Practical Aspects]
- 33 Lecture 7.4: Training Neural Networks [CNN Variants]
- 34 Lecture 7: Q&A (Part 1)
- 35 Lecture 7.5: Training Neural Networks [Q&A]
- 36 Lecture 8.1: Pytorch Tutorial
- 37 Lecture 8.2: Pytorch Tutorial [Model Training]
- 38 Lecture 8: Q&A
- 39 Lecture 9.1: Features
- 40 Lecture 9.2: Features [Key Points]
- 41 Lecture 9.3: Features [Histogram of Gradients] [HOG]
- 42 Lecture 9.4: Features [SIFT]
- 43 Lecture 9.4: Features [SIFT Part 2]
- 44 Lecture 9: Features Q&A
- 45 Lecture 9: Features Q&A (Part 2)
- 46 Lecture 10.1: Autoencoder
- 47 Lecture 10.2: Autoencoder [Q&A]
- 48 Lecture 11.1: Classification - I
- 49 Lecture 11.2: Classification - I [Nearest Neighbor Classifier]
- 50 Lecture 11.3: Maximum Margin Classifier
- 51 Lecture 11.4: Classification - I [Support Vector Machine]
- 52 Lecture 11: Classification - I Q&A
- 53 Lecture 12.1: Classification - II
- 54 Lecture 11, 12: Q&A - [Classification - I], [Classification - II]
- 55 Lecture 13.1: Object Detection
- 56 Lecture 13.2: Object Detection [Sliding Window Approach]
- 57 Lecture 13.3: Object Detection [Evaluation]
- 58 Lecture 13: Object Detection Q&A
- 59 Lecture 14.4: Image Segmentation [Clustering Based]
- 60 Lecture 14: Q&A - Image Segmentation [Clustering Based]
- 61 Lecture 15: Semantic Segmentation
- 62 Lecture 15: Semantic Segmentation [Q&A]
- 63 Lecture 16: Instance Segmentation
