Computer Vision - Fall 2023

Computer Vision - Fall 2023

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Lecture 1 - Introduction to Computer Vision

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Lecture 1 - Introduction to Computer Vision

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Computer Vision - Fall 2023

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  1. 1 Lecture 1 - Introduction to Computer Vision
  2. 2 Lecture 1.1 - Introduction to Computer Vision [Basics]
  3. 3 Lecture 1.2 - Introduction to Computer Vision [Digital Image]
  4. 4 Lecture 1.3 - Introduction to Computer Vision [Motivation]
  5. 5 Lecture 1.4 - Introduction to Computer Vision [Object Detection]
  6. 6 Lecture 1.5 - Introduction to Computer Vision [Complex Computer Vision Tasks]
  7. 7 Lecture 2 - Linear Algebra Basics
  8. 8 Lecture 2.1 Linear Algebra Basics [Vectors]
  9. 9 Lecture 2.2 - Linear Algebra Basics [Matrix]
  10. 10 Lecture 2.3 - Linear Algebra Basics [Transformations]
  11. 11 Lecture 2.4 - Linear Algebra Basics [Single Value Decomposition]
  12. 12 Lecture 2.5 - Linear Algebra Basics [Q&A]
  13. 13 Lecture 3 - Image Filtering
  14. 14 Lecture 3.1 - Image Filtering [Digitization]
  15. 15 Lecture 3.2 - Image Filtering [Digitization of 1D, 2D, 3D, and Arc]
  16. 16 Lecture 3.3 Image Filtering [Definition, Sampling, Quantization, Examples]
  17. 17 Lecture 3.4 - Image Filtering [Histogram]
  18. 18 Lecture 3.5 - Image Filtering [Intensity Profiles]
  19. 19 Lecture 3.6 - Image Filtering [Image Noise]
  20. 20 Lecture 3.7 - Image Filtering [Why Image Filtering?]
  21. 21 Lecture 3.8 - Image Filtering [Image Filtering Operations using Kernel]
  22. 22 Lecture 3.9 - Image Filtering [Image Filtering Techniques]
  23. 23 Lecture 3.10 - Image Filtering [Gaussian Filter]
  24. 24 Lecture 3.11 - Image Filtering [Filtering Examples]
  25. 25 Lecture 3.12 - Image Filtering [Box Filter]
  26. 26 Lecture 3.13 - Image Filtering [Sobel Filter, and Image Filtering Properties]
  27. 27 Lecture 3.14 - Image Filtering [Median Filter and Kernel size used in Image Filtering]
  28. 28 Lecture 3.15 - Image Filtering [Introduction to Image Derivatives]
  29. 29 Lecture 4 - Edge Detection
  30. 30 Lecture 4.1- Edge Detection [Introduction to Edge Detection]
  31. 31 Lecture 4.2 - Edge Detection [Types of Edges]
  32. 32 Lecture 4.3 - Edge Detection [Characterizing Edges]
  33. 33 Lecture 4.4 - Edge Detection [Effects of Noise in Edge Detection]
  34. 34 Lecture 4.5 - Edge Detection [Smoothing in Edge Detection]
  35. 35 Lecture 4.6 - Edge Detection [Evaluate Edge Detection]
  36. 36 Lecture 4.7 - Edge Detection [Design Criteria of Edge Detection]
  37. 37 Lecture 4.8 - Edge Detection [Introduction to Prewitt and Sobel Edge Detection]
  38. 38 Lecture 4.9 - Edge Detection [Prewitt Edge Detector]
  39. 39 Lecture 4.10 - Edge Detection [Sobel Edge Detector]
  40. 40 Lecture 4.11 - Edge Detection [Marr Hildreth Edge Detector]
  41. 41 Lecture 4.12 - Edge Detection [Laplacian of Gaussian Filtering in Edge Detection]
  42. 42 Lecture 4.13 - Edge Detection [Canny Edge Detection]
  43. 43 Lecture 4.14 Edge Detection [Non Maximum Suppression]
  44. 44 Lecture 4.15 - Edge Detection [Hysteresis Thresholding]
  45. 45 Lecture 5 - Introduction to Neural Networks
  46. 46 Lecture 5.1 - Introduction to Neural Networks [Object Classification]
  47. 47 Lecture 5.2 - Introduction to Neural Networks [Pixel Representation of images]
  48. 48 Lecture 5.3 - Introduction to Neural Networks [Feature Representations]
  49. 49 Lecture 5.4 - Introduction to Neural Networks [Training and Testing Phases in Neural Networks​]
  50. 50 Lecture 5.5 - Introduction to Neural Networks [Features used in Neural Networks]
  51. 51 Lecture 5.6 - Introduction to Neural Networks [The Machine Learning Framework]
  52. 52 Lecture 5.7 - Introduction to Neural Networks [Neurons in the Brain 1]
  53. 53 Lecture 5.8 - Introduction to Neural Networks [Neural Activation Function]
  54. 54 Lecture 5.9 - Introduction to Neural Networks [Classifying an Image in Neural Networks]
  55. 55 Lecture 5.10 - Introduction to Neural Networks [Problems with all linear functions]
  56. 56 Lecture 5.11 - Introduction to Neural Networks [Introduction to Non linearities]
  57. 57 Lecture 5.12 - Introduction to Neural Networks [Perceptron]
  58. 58 Lecture 5.13 - Introduction to Neural Networks [Why CNN]
  59. 59 Lecture 6 - Introduction to Convolutional Neural Networks
  60. 60 Lecture 6.1 - Introduction to Convolutional Neural Networks [Motivation for CNN]
  61. 61 Lecture 6.2 - Introduction to Convolutional Neural Networks [Overview of CNN]
  62. 62 Lecture 6.3 - Introduction to Convolutional Neural Networks [Basics]
  63. 63 Lecture 6.4 - Introduction to Convolutional Neural Networks [Fundamental Operation]
  64. 64 Lecture 6.5 - Introduction to Convolutional Neural Networks [Convolution Recap]
  65. 65 Lecture 6.6 - Introduction to Convolutional Neural Networks [Parameters]
  66. 66 Lecture 6.7 - Introduction to Convolutional Neural Networks Convolution [Operation]
  67. 67 Lecture 6.8 - Intro to Convolutional Neural Networks [Understanding Convolution in Sobel edge d]
  68. 68 Lecture 6.9 - Introduction to Convolutional Neural Networks [Convolution Intuition]
  69. 69 Lecture 6.10 - Introduction to Convolutional Neural Networks [2D Convolution]
  70. 70 Lecture 6.11 - Introduction to Convolutional Neural Networks [Stride in 2D Convolution]
  71. 71 Lecture 6.12 - Introduction to Convolutional Neural Networks [Padding]
  72. 72 Lecture 6.13 - Introduction to Convolutional Neural Networks [Pooling]
  73. 73 Lecture 6.14 - Introduction to Convolutional Neural Networks [AlexNet Case Study]
  74. 74 Lecture 6.15 - Introduction to Convolutional Neural Networks [AlexNet Architecture]
  75. 75 Lecture 6.16 - Introduction to Convolutional Neural Networks [Visualizing CNN]
  76. 76 Lecture 7 - Training Neural Networks
  77. 77 Lecture 7.1 - Training Neural Networks [Basics Recap]
  78. 78 Lecture 7.2 Training Neural Networks [Loss Function]
  79. 79 Lecture 7.3 - Training Neural Networks [Gradient Descent in Neural Networks]
  80. 80 Lecture 7.4 - Training Neural Networks [Train CNN with Gradient Descent]
  81. 81 Lecture 7.5 - Training Neural Networks [Differentiability in Neural Networks]
  82. 82 Lecture 7.6 - Training Neural Networks [Backpropagation Chain Rule]
  83. 83 Lecture 7.7 - Training Neural Networks [Stochastic Gradient Descent]
  84. 84 Lecture 7.8 - Training Neural Networks [Stochastic Gradient Descent - Part 2]
  85. 85 Lecture 7.9 - Training Neural Networks [Gradient Descent Oscillations]
  86. 86 Lecture 7.10 - Training Neural Networks [Data Fitting Problem]
  87. 87 Lecture 7.11 - Training Neural Networks [Lowering Learning Rate]
  88. 88 Lecture 7.12 - Training Neural Networks [Regularization]
  89. 89 Lecture 7.13 - Training Neural Networks [Regularization Ensemble]
  90. 90 Lecture 7.14 - Training Neural Networks Dropout
  91. 91 Lecture 7.15 - Training Neural Networks [Training Steps]
  92. 92 Lecture 7.16 - Training Neural Networks [AlexNet Training]
  93. 93 Lecture 7.17 - Training Neural Networks [Residual Network]
  94. 94 Lecture 7.18 - Training Neural Networks [GoogleNet Inception]
  95. 95 Lecture 7.19 - Training Neural Networks [DenseNet]
  96. 96 Lecture 8 - PyTorch Tutorial
  97. 97 Lecture 8.1 - PyTorch Tutorial [PyTorch Tensor]
  98. 98 Lecture 8.2 - PyTorch Tutorial [PyTorch Operations]
  99. 99 Lecture 8.3 - PyTorch Tutorial [Torch Tensor vs Numpy Array]
  100. 100 Lecture 8.4 - PyTorch Tutorial [Matrix Multiplication in PyTorch]
  101. 101 Lecture 8.5 - PyTorch Tutorial [Computational Graphs and Automatic Gradient Computation]
  102. 102 Lecture 8.6 - PyTorch Tutorial [Training Procedure]
  103. 103 Lecture 8.7 - PyTorch Tutorial [Building Neural Network]
  104. 104 Lecture 8.8 - PyTorch Tutorial [Defining a Network class]
  105. 105 Lecture 8.9 - PyTorch Tutorial [Defining a Network class part 2]
  106. 106 Lecture 8.10 - PyTorch Tutorial [Defining a CNN Network - Example]
  107. 107 Lecture 8.11 - PyTorch Tutorial [Iterate over a Dataset of Inputs]
  108. 108 Lecture 8.12 - PyTorch Tutorial [Process input through the Network​]
  109. 109 Lecture 8.13 - PyTorch Tutorial [Loss Function]
  110. 110 Lecture 8.14 - PyTorch Tutorial [Gradient Computation]
  111. 111 Lecture 8.15 - PyTorch Tutorial [Update Parameters]
  112. 112 Lecture 8.16 - PyTorch Tutorial [Model Training - Full Procedure]
  113. 113 Lecture 9 - Features
  114. 114 Lecture 9.1 - Features [Introduction to Features]
  115. 115 Lecture 9.2 - Features [Types of Features]
  116. 116 Lecture 9.3 - Features [Uses of Features]
  117. 117 Lecture 9.4 - Features [Finding Features in videos]
  118. 118 Lecture 9.5 - Features [Characteristics of good Features]
  119. 119 Lecture 9.6 - Features [Key Points]
  120. 120 Lecture 9.7 - Features [Properties of Interest Points]
  121. 121 Lecture 9.8 - Features [Corner Detection]
  122. 122 Lecture 9.9 - Features [Corner Detection by Auto correlation]
  123. 123 Lecture 9.10 - Features [Corner Detection strategy]
  124. 124 Lecture 9.11 - Features [Harris Corner Detection]
  125. 125 Lecture 9.12 - Features [Histogram of Gradients Edges]
  126. 126 Lecture 9.13 - Features [HOG - Human Detection]
  127. 127 Lecture 9.14 - Features [Histogram of Oriented Gradients]
  128. 128 Lecture 9.15 - Features [Histogram of Oriented Gradients Example]
  129. 129 Lecture 9.16 - Features [Summary of HOG Computation]
  130. 130 Lecture 9.17 - Features [Introduction to SIFT with an Example]
  131. 131 Lecture 9.18 - Features [Overall Procedure of SIFT at a High Level]
  132. 132 Lecture 9.19 - Features [Automatic scale selection]
  133. 133 Lecture 9.20 - Features [Understanding of useful signature function​]
  134. 134 Lecture 9.21 - Features [Finding local maxima in given image]
  135. 135 Lecture 9.22 - Features [Optimizing LoG filter]
  136. 136 Lecture 9.23 - Features [SIFT Recap]
  137. 137 Lecture 9.24 - Features [Implementing Difference of Gaussian]
  138. 138 Lecture 9.25 - Features Extracting Feature Orientation
  139. 139 Lecture 9.26 - Features [Normalization of Orientation]
  140. 140 Lecture 9.27 - Features [Understanding formation of local features in window]
  141. 141 Lecture 9.28 - Features [Summary of SIFT Descriptor]
  142. 142 Lecture 10 - Autoencoder
  143. 143 Lecture 10.1 - Autoencoders [Introduction to Autoencoder]
  144. 144 Lecture 10.2 - Autoencoders [Introduction to Autoencoder - CNN]
  145. 145 Lecture 10.3 - Autoencoders [How Autoencoders work - Examples]
  146. 146 Lecture 10.4 - Autoencoders [Feature Learning using Autoencoder]
  147. 147 Lecture 10.5 - Autoencoders [Applications, and Properties of Autoencoder]
  148. 148 Lecture 11 - Classification I
  149. 149 Lecture 11.1 Classification I [Introduction to classification]
  150. 150 Lecture 11.2 - Classification I [Types of classification]
  151. 151 Lecture 11.3 - Classification I [Understanding how to Perform classification for the Given Data]
  152. 152 Lecture 11.4 - Classification I [General Framework, Decision Boundaries for classification]
  153. 153 Lecture 11.5 - Classification I [Nearest Neighbor Classifier]
  154. 154 Lecture 11.6 - Classification I [K-Nearest Neighbor]
  155. 155 Lecture 11.7 - Classification I [Maximum Margin Classifier - SVM]
  156. 156 Lecture 11.8 - Classification I [Understanding Non separable case]
  157. 157 Lecture 11.9 - Classification I [Support Vector Classifier - Separable case]
  158. 158 Lecture 11.10 - Classification I [Support Vector Classifier - Soft Margin]
  159. 159 Lecture 11.11 - Classification I [Support Vector Classifier Example]
  160. 160 Lecture 11.12 - Classification I [Disadvantages of linear boundary]
  161. 161 Lecture 11.13 - Classification I [Advantages of non linear boundary]
  162. 162 Lecture 11.14 - Classification I [Non linear SVM]
  163. 163 Lecture 11.15 - Classification I [Multi class SVM]
  164. 164 Lecture 11.16 - Classification I [Pros and Cons of SVM]
  165. 165 Lecture 12 - Classification II
  166. 166 Lecture 12.1 - Classification II [Classification I Recap]
  167. 167 Lecture 12.2 - Classification II [Fully Convolutional Network]
  168. 168 Lecture 12.3 - Classification II [Converting Fully Convolution to Convolution]
  169. 169 Lecture 12.4 - Classification II [Introducing Non linearities]
  170. 170 Lecture 12.5 - Classification II [Activation Functions]
  171. 171 Lecture 12.6 - Classification II [Softmax Classification]
  172. 172 Lecture 12.7 - Classification II [Loss Function, Visualizing Convolution]
  173. 173 Lecture 13 - Object Detection
  174. 174 Lecture 13.1 - Object Detection {Introduction to Object Detection]
  175. 175 Lecture 13.2 - Object Detection [Introduction to Sliding Window, Template Matching]
  176. 176 Lecture 13.3 - Object Detection [Example of Sliding Window Approach]
  177. 177 Lecture 13.4 - Object Detection [Gaussian pre-filtering]
  178. 178 Lecture 13.5 - Object Detection [Gaussian Pyramid Construction and Challenges]
  179. 179 Lecture 13.6 - Object Detection [Extract Features, Classify Features, Post Processing]
  180. 180 Lecture 13.7 - Object Detection [Non maximum suppression]
  181. 181 Lecture 13.8 - Object Detection [Evaluation using IOU]
  182. 182 Lecture 13.9 - Object Detection [Precision and Recall]
  183. 183 Lecture 13.10 - Object Detection [Mean Average Precision]
  184. 184 Lecture 13.11 - Object Detection [CNN Based Approach - A Simple Solution]
  185. 185 Lecture 13.12 - Object Detection [CNN based Approach RCNN]
  186. 186 Lecture 13.13 - Object Detection [CNN based Approach Fast R-CNN]
  187. 187 Lecture 13.14 - Object Detection [Per class or class agnostic regression]
  188. 188 Lecture 14 - Image Segmentation
  189. 189 Lecture 14.2 - Image Segmentation [Image Binarization]
  190. 190 Lecture 14.3 - Image Segmentation [Thresholding Examples]
  191. 191 Lecture 14.4 - Image Segmentation [Otsu Example]
  192. 192 Lecture 14.5 - Image Segmentation [Region Growing Algorithm]
  193. 193 Lecture 14.6 - Image Segmentation [Region Splitting and Merging 1]
  194. 194 Lecture 14.7 - Image Segmentation [Introduction to Clustering]
  195. 195 Lecture 14.8 - Image Segmentation [Natural Grouping and Distance Metrics]
  196. 196 Lecture 14.9 - Image Segmentation [K-means clustering algorithm]
  197. 197 Lecture 14.10 - Image Segmentation [K-means Loss Function]
  198. 198 Lecture 14.11 - Image Segmentation [SLIC Algorithm]
  199. 199 Lecture 14.12 - Image Segmentation [Mean Shift Segmentation]
  200. 200 Lecture 14.13 - Image Segmentation [Mean Shift Algorithm]
  201. 201 Lecture 14.14 - Image Segmentation [Mean Shift Clustering]
  202. 202 Lecture 15 - Semantic Segmentation
  203. 203 Lecture 15.1 - Semantic Segmentation [Introduction to Semantic Segmentation]
  204. 204 Lecture 15.2 - Semantic Segmentation [Segmentation tasks]
  205. 205 Lecture 15.3 - Semantic Segmentation [Fully Convolutional Networks]
  206. 206 Lecture 15.4 - Semantic Segmentation [Skip Connections in Fully Convolutional Networks]
  207. 207 Lecture 15.5 - Semantic Segmentation [Upsampling]
  208. 208 Lecture 15.6 - Semantic Segmentation [Deconvolution network for Semantic Segmentation​]
  209. 209 Lecture 15.7 - Semantic Segmentation [U-Net Sampling]
  210. 210 Lecture 16 - Instance Segmentation
  211. 211 Lecture 16.1 - Instance Segmentation [Introduction to Instance Segmentation]
  212. 212 Lecture 16.2 - Instance Segmentation [Mask - RCNN]
  213. 213 Lecture 16.3 - Instance Segmentation [RoIAlign vs RoIPool]
  214. 214 Lecture 16.4 - Instance Segmentation [RoI-Align vs RoI-Pooling Example]
  215. 215 Lecture 16.5 - Instance Segmentation [Mask - RCNN Architecture]

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