【Computer Vision】計算機視覺相關課程和書籍
阿新 • • 發佈：2019-01-03
Table of Contents
Books
Computer Vision
OpenCV Programming
- OpenCV Essentials - Oscar Deniz Suarez, Mª del Milagro Fernandez Carrobles, Noelia Vallez Enano, Gloria Bueno Garcia, Ismael Serrano Gracia
Machine Learning
Fundamentals
Courses
Computer Vision
Computational Photography
Machine Learning and Statistical Learning
Optimization
Papers
Conference papers on the web
Survey Papers
Tutorials and talks
Computer Vision
Recent Conference Talks
3D Computer Vision
Internet Vision
- The Distributed Camera - Noah Snavely (Cornell University) 2011
- A Trillion Photos - Steve Seitz (University of Washington) 2013
Computational Photography
Learning and Vision
Object Recognition
Graphical Models
Machine Learning
Optimization
Deep Learning
Software
External Resource Links
General Purpose Computer Vision Library
Multiple-view Computer Vision
Feature Detection and Extraction
- SIFT
- David G. Lowe, "Distinctive image features from scale-invariant keypoints," International Journal of Computer Vision, 60, 2 (2004), pp. 91-110.
- BRISK
- Stefan Leutenegger, Margarita Chli and Roland Siegwart, "BRISK: Binary Robust Invariant Scalable Keypoints", ICCV 2011
- SURF
- Herbert Bay, Andreas Ess, Tinne Tuytelaars, Luc Van Gool, "SURF: Speeded Up Robust Features", Computer Vision and Image Understanding (CVIU), Vol. 110, No. 3, pp. 346--359, 2008
- FREAK
- A. Alahi, R. Ortiz, and P. Vandergheynst, "FREAK: Fast Retina Keypoint", CVPR 2012
- AKAZE
- Pablo F. Alcantarilla, Adrien Bartoli and Andrew J. Davison, "KAZE Features", ECCV 2012
Low-level Vision
Stereo Vision
Optical Flow
Image Denoising
BM3D, KSVD,
Super-resolution
- Multi-frame image super-resolution
- Pickup, L. C. Machine Learning in Multi-frame Image Super-resolution, PhD thesis 2008
- Markov Random Fields
for Super-Resolution
- W. T Freeman and C. Liu. Markov Random Fields for Super-resolution and Texture Synthesis. In A. Blake, P. Kohli, and C. Rother, eds., Advances in Markov Random Fields for Vision and Image Processing, Chapter 10. MIT Press, 2011
- Sparse regression and natural image prior
- K. I. Kim and Y. Kwon, "Single-image super-resolution using sparse regression and natural image prior", IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 32, no. 6, pp. 1127-1133, 2010.
- Single-Image Super Resolution via a Statistical Model
- T. Peleg and M. Elad, A Statistical Prediction Model Based on Sparse Representations for Single Image Super-Resolution, IEEE Transactions on Image Processing, Vol. 23, No. 6, Pages 2569-2582, June 2014
- Sparse Coding for Super-Resolution
- R. Zeyde, M. Elad, and M. Protter On Single Image Scale-Up using Sparse-Representations, Curves & Surfaces, Avignon-France, June 24-30, 2010 (appears also in Lecture-Notes-on-Computer-Science - LNCS).
- Patch-wise Sparse Recovery
- Jianchao Yang, John Wright, Thomas Huang, and Yi Ma. Image super-resolution via sparse representation. IEEE Transactions on Image Processing (TIP), vol. 19, issue 11, 2010.
- Neighbor embedding
- H. Chang, D.Y. Yeung, Y. Xiong. Super-resolution through neighbor embedding. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), vol.1, pp.275-282, Washington, DC, USA, 27 June - 2 July 2004.
- Deformable Patches
- Yu Zhu, Yanning Zhang and Alan Yuille, Single Image Super-resolution using Deformable Patches, CVPR 2014
- SRCNN
- Chao Dong, Chen Change Loy, Kaiming He, Xiaoou Tang, Learning a Deep Convolutional Network for Image Super-Resolution, in ECCV 2014
- A+: Adjusted Anchored Neighborhood Regression
- R. Timofte, V. De Smet, and L. Van Gool. A+: Adjusted Anchored Neighborhood Regression for Fast Super-Resolution, ACCV 2014
- Transformed Self-Exemplars
- Jia-Bin Huang, Abhishek Singh, and Narendra Ahuja, Single Image Super-Resolution using Transformed Self-Exemplars, IEEE Conference on Computer Vision and Pattern Recognition, 2015
Image Deblurring
Non-blind deconvolution
Blind deconvolution
Non-uniform Deblurring
Image Completion
Image Retargeting
Alpha Matting
Image Pyramid
Edge-preserving image processing
Intrinsic Images
Contour Detection and Image Segmentation
Interactive Image Segmentation
Video Segmentation
Camera calibration
Simultaneous localization and mapping
SLAM community:
Tracking/Odometry:
Graph Optimization:
Loop Closure:
Localization & Mapping:
Single-view Spatial Understanding
Object Detection
Nearest Neighbor Search
General purpose nearest neighbor search
Nearest Neighbor Field Estimation
Visual Tracking
Saliency Detection
Attributes
Action Reconition
Egocentric cameras
Human-in-the-loop systems
Image Captioning
Optimization
- Ceres Solver - Nonlinear least-square problem and unconstrained optimization solver
- NLopt- Nonlinear least-square problem and unconstrained optimization solver
- OpenGM - Factor graph based discrete optimization and inference solver
- GTSAM - Factor graph based lease-square optimization solver
Deep Learning
Machine Learning
Datasets
External Dataset Link Collection
Low-level Vision
Stereo Vision
Optical Flow
Image Super-resolutions
Intrinsic Images
Material Recognition
Multi-view Reconsturction
Saliency Detection
Visual Tracking
Visual Surveillance
Saliency Detection
Change detection
Visual Recognition
Image Classification
Scene Recognition
Object Detection
Semantic labeling
Multi-view Object Detection
Fine-grained Visual Recognition
Pedestrian Detection
Action Recognition
Image-based
Video-based
Image Deblurring
Image Captioning
Scene Understanding
# SUN RGB-D - A RGB-D Scene Understanding Benchmark Suite # NYU depth v2 - Indoor Segmentation and Support Inference from RGBD Images
Resources for students
Resource link collection
- Graduate Skills Seminars - Yashar Ganjali, Aaron Hertzmann (University of Toronto)
- Research Skills - Simon Peyton Jones (Microsoft Research)
- Resource collection - Tao Xie (UIUC) and Yuan Xie (UCSB)
Writing
Presentation
- How to give a good talk - David Fleet (University of Toronto) and Aaron Hertzmann (Adobe Research)