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2D vision: 1. Course introduction, Computer vision overview 2. Linear Algebra for Computer Vision 3. Pixels and image representation 4. Image filters and edge detection 5. Local features and fitting (Local features, Harris corner detection, Scale invariant feature transform, Image stitching and RANSAC) 6. Local features and fitting (Local features, Harris corner detection, Scale invariant feature transform, Image stitching and RANSAC) (cont’d) 7. Python for Computer Vision 8. Segmentation: tree-based segmentation, spectral clustering, other superpixel methods. 9. Segmentation: tree-based segmentation, spectral clustering, other superpixel methods (cont’d) 10. Image classification overview and Bag of Features 11. Nearest neighbor and logistic regression (for image search and image classification) 12. Image classification and image search using advanced feature coding (First and second order local feature aggreg
ation, sparse coding). 13. Image classification and image search using advanced feature coding (First and second order local feature aggregation, sparse coding) (cont’d) 14. Object detection (e.g., face and pedestrian) with sliding window approach, object detection using part-based models. 15. Object detection (e.g., face and pedestrian) with sliding window approach, object detection using part-based models. (cont’d) 16. Object detection (e.g., face and pedestrian) with sliding window approach, object detection using part-based models. (cont’d) 17. Motion and tracking 18. Motion and tracking (cont’d) 3D vision 19. Image formulation, camera calibration 20. Image formulation, camera calibration (cont’d) 21. Multi-view geometry 22. Multi-view geometry (cont’d) 23. Multi-view geometry (cont’d) Computer Vision in the era of deep learning 24. Neural Networks (perceptro
n, multi-layer perceptron, activation functions, loss functions, gradient descent, back-propagation). 25. Neural Networks (perceptron, multi-layer perceptron, activation functions, loss functions, gradient descent, back-propagation). (cont’d) 26. Deep learning (principles, convolutional neural network, auto-encoder neural network, recurrent neural network) and state-of-the-art deep network architectures for image classification. 27. Deep learning (principles, convolutional neural network, auto-encoder neural network, recurrent neural network) and state-of-the-art deep network architectures for image classification. (cont’d) 28. Deep learning (principles, convolutional neural network, auto-encoder neural network, recurrent neural network) and state-of-the-art deep network architectures for image classification. (cont’d) 29. Deep learning for object detection and semantic segmentation 30. Deep learning for objec
t detection and semantic segmentation (cont’d)
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