CMP717: Image Processing
Spring 2018
Detailed Syllabus and Lectures
May 10: Deep Generative Models Lecture slides
Topics:
Generative models, autoregressive models, variational autoencoders, generative adversarial networks
Paper presentations:
Required Reading:
- Auto-Encoding Variational Bayes, D. P. Kingma and M. Welling, In Proc. ICLR, 2014
- Generative Adversarial Networks, I. J. Goodfellow et al., In Proc. NIPS, 2014
- Conditional Image Generation with PixelCNN Decoders, A. van den Oord et al., In Proc. NIPS, 2016
- Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks, A. Radford et al., In Proc. ICLR, 2016
- Learning to Generate Images of Outdoor Scenes from Attributes and Semantic Layouts, L. Karacan et al., arXiv preprint arXiv:1612.00215, 2016
- Image-to-Image Translation with Conditional Adversarial Networks, P. Isola et al., In Proc. CVPR, 2017
- Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks, J.-Y. Zhu et al., In Proc. ICCV, 2017
Additional Reading:
- Pixel Recurrent Neural Networks, A. van den Oord et al., In Proc. ICML, 2016
- Wasserstein Generative Adversarial Networks, M. Arjovsky et al., In Proc. ICML, 2017
- Progressive Growing of GANs for Improved Quality, Stability, and Variation, T. Karras et al., In Proc. ICLR, 2018
Topics:
Visual attention, visual saliency prediction, eye fixations, salient object, top-down saliency
Paper presentations:
Required Reading:
Additional Reading:
Topics:
Image deblurring, blind deconvolution, non-blind deconvolution, MAP based formulations, variational Bayesian based models, edge based methods
Paper presentation:
- Mask R-CNN, K. He et al., In Proc. ICCV, 2017
Slides by Guler Koc and Halil Ibrahim Ozturk
Required Reading:
- Removing camera shake from a single image, R. Fergus et al., In Proc. SIGGRAPH, 2006
- Fast motion deblurring, S. Cho and S. Lee, ACM Trans. Graph., 2009
- Understanding and evaluating blind deconvolution algorithms, A. Levin et al., In Proc. CVPR, 2009
- Two-Phase Kernel Estimation for Robust Motion Deblurring, L. Xu and J. Jia, In Proc. ECCV, 2010
- Image Deblurring with Blurred/Noisy Image Pairs, L. Yuan et al., SIGGRAPH, 2007
Additional Reading:
Apr 12: Semantic Segmentation Lecture slides
Topics:
Semantic segmentation, instance segmentation
Paper presentation:
Required Reading:
- TextonBoost for Image Understanding: Multi-Class Object Recognition and Segmentation by Jointly Modeling Texture, Layout, and Context, J. Shotton et al., Int. Journal Comp. Vis., 2007
- Nonparametric Scene Parsing via Label Transfer, Liu et al., IEEE Trans. Pattern Anal. Mach. Intell., 2011
- Simultaneous Detection and Segmentation, B. Hariharan et al., In Proc. ECCV, 2014
- Fully Convolutional Networks for Semantic Segmentation, J. Long et al., In Proc. CVPR, 2015
- Hypercolumns for Object Segmentation and Fine-grained Localization, B. Hariharan et al., In Proc. CVPR, 2015
Additional Reading:
Topics:
Convolutional neural networks
Required Reading:
Additional Reading:
Mar 29: Deep learning basics Lecture slides
Topics:
Deep learning, perceptron, multi layer perceptron, backprogation, activation functions
Paper presentation:
Required Reading:
Additional Reading:
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Topics:
Graphical models, Markov random fields, conditional random fields, graph-cut
Paper presentation:
Required Reading:
Additional Reading:
Topics:
Sparse coding, dictionary learning, K-SVD algorithm, L0-smoothing
Paper presentation:
Required Reading:
Mar 7: Modern Image Smoothing Lecture slides
Topics:
Bilateral filtering, non-local means denoising, LARK filter, image smoothing via region covariance (RegCov smoothing), rolling guidance filter
Required Reading:
- Bilateral Filtering for Gray and Color Images, C. Tomasi and R. Manduchi, In Proc. ICCV, 1998
- A non-local algorithm for image denoising, A. Buades et al., In Proc. CVPR, 2005
- Kernel Regression for Image Processing and Reconstruction, H. Takeda et al., IEEE Trans. Image Processing, 2007
- Structure Preserving Image Smoothing via Region Covariances, L. Karacan et al., ACM Trans. Graph., 2013
- Rolling Guidance Filter, Q. Zhang et al., In Proc. ECCV, 2014
Additional Reading:
Mar 1: Nonlinear Filtering, Snakes, Variational Segmentation Models Lecture slides
Topics:
Nonlinear filtering, Perona-Malik diffusion, total variation, context-guided filtering, active contours, snakes, Mumford-Shah model and its approximations
Required Material:
- Notes on nonlinear diffusion
- Notes on Mumford-Shah formulation
- Variational Methods: A Short Intro by Daniel Cremers
- Scale-Space and Edge Detection Using Anisotropic Diffusion, P. Perona and J. Malik, IEEE Trans. Pattern Anal. Mach. Intell., 1990
- Nonlinear Total Variation Based Noise Removal Algorithms, L. Rudin et al., Phys. D., 1992
- Snakes: Active contour models, M. Kass et al., Int. Journal Comp. Vis., 1988
- Active contours without edges, T. Chan and L. Vese, IEEE Trans. Image Processing, 2001
- Mumford-Shah Regularizer with Contextual Feedback, E. Erdem and S. Tari, J. Math. Imaging and Vision, 2009
Additional Reading:
Feb 22: Linear Filtering, Edge/Boundary Detection, Image Segmentation Lecture slides
Topics:
Linear filtering, linear diffusion, derivative filters, Laplacian of Gaussian, Canny edge detector, pb detector, sketch tokens, k-means, normalized cut
Required Reading:
- Notes on linear diffusion
- Scale-space filtering: A New Approach to Multi-Scale Description, A. P. Witkin, In Proc. IJCAI, 1983
- Theory of Edge Detection, D. Marr and E. Hildreth
. Proc. R. Soc. Lond. B, 1980.
- Normalized Cuts and Image Segmentation, J. Shi and J. Malik, IEEE Trans. Pattern Anal. Mach. Intell., 2000
- Contour Detection and Hierarchical Image Segmentation, P. Arbelaez et al., IEEE Trans. Pattern Anal. Mach. Intell., 2011
- Sketch Tokens: A Learned Mid-level Representation for Contour and Object Detection, J. J. Lim et al., In Proc. CVPR, 2013
Additional Reading:
- Chapter 4 (Edges) and Chapter 5 (Segmentation) from R. Szeliski's book
Feb 15: Introduction to Image Processing Lecture slides
Topics:
Course information, what is image processing
Required Reading:
- Chapter 3 (Image processing) from R. Szeliski's book
Additional Reading: