Low-light enhancement
Ke Xu, 2020, Learning to Restore Low-Light Images via Decomposition-and-Enhancement [pdf]
Unsupervised Learning
Ghahramani, 2004, Unsupervised learning [pdf]
Karhunen, 2015, Unsupervised Deep Learning: A Short Review [pdf]
Langkvist, 2014, A Review of Unsupervised Feature Learning and Deep Learning for Time-Series Modeling [pdf]
Bengio, 2012, Unsupervised Feature Learning and Deep Learning: A Review and New Perspectives [pdf]
Bengio, 2012, Deep Learning of Representations for Unsupervised and Transfer Learning [pdf]
Bengio, 2009, Learning Deep Architectures for AI [pdf]
Salakhutdinov, 2009, Deep Boltzmann Machines [pdf]
Convolutional Neural Network (CNN) - Deep Learning
- VGG, Oxford [site]
- CNN basic with Matlab: VGG Convolutional Neural Networks Practical [site]
- Alex Krizhevsky, 2012, ImageNet Classification with Deep Convolutional Neural Networks [pdf]
- Mairal, 2014, Convolutional Kernel Network [pdf]
- Bo, 2011 nips, Efficient Match Kernels between Sets of Features for Visual Recognition [pdf]
- Bo, 2011 cvpr, Object Recognition with Hierarchical Kernel Descriptors [pdf]
- Schmidhuber, 2014, Deep learning in neural networks:an overview [pdf]
- LeCun, 2015, Deep learning: Nature
- Mnih, 2014, Recurrent Models of Visual Attention [pdf]
- Tang, 2014, Learning Generative Models with Visual Attention [pdf]
- Xu, 2015, Show, Attend and Tell: Neural Image Caption Generation with Visual Attention [pdf]
- Jiang, 2014, Deep Salience: Visual Salience Modeling via Deep Belief Propagation [pdf]
- Tseng, 2013, Deep learning on natural viewing behaviors to differentiate children with fetal alcohol spectrum disorder [pdf]
- Zhao, 2015, Saliency Detection by Multi-Context Deep Learning [pdf]
- Pan & Yang, 1999, A survey on transfer learning [pdf]
- S. Mallat, 2016, Understanding deep convolutional networks [pdf]
- Anden & Mallat, 2014, Deep Scattering spectrum [pdf]
- Aubry, 2015, Understanding deep features with computer-generated imagery [pdf]
- Zeiler, 2014, Visualizing and Understanding Convolutional Networks [pdf]
- Mallat, 2013, Course on High Dimensional Classification with Deep Scattering Networks [Slide]
- Srivastava, 2014, Dropout: A Simple Way to Prevent Neural Networks from Overfitting [pdf]
- Hasan, 2016, Learning Temporal Regularity in Video Sequences [pdf]
- Duchi, 2011, Adaptive Subgradient Methods for Online Learning and Stochastic Optimization [pdf]
- Kingma, 2015, ADAM: A METHOD FOR STOCHASTIC OPTIMIZATION [pdf]
- Wang, 2015, Action Recognition with Trajectory-Pooled Deep-Convolutional Descriptors [pdf, code]
- Feichtenhofer, 2016, Convolutional Two-Stream Network Fusion for Video Action Recognition [pdf, code]
- Zhang, 2016, Real-time Action Recognition with Enhanced Motion Vector CNNs [pdf]
- Chellappa, 2016, The changing fortunes of pattern recognition and computer vision [pdf]
- Kuo, 2016, Understanding Convolutional Neural Networks with A Mathematical Model [pdf]
- Simonyan, 2014, Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps [pdf]
- Erhan, 2009, Visualizing Higher-Layer Features of a Deep Network [pdf]
- Yosinski, 2015, Understanding Neural Networks Through Deep Visualization [pdf, demo]
- Makhzani, 2014, Winner-Take-All Autoencoders [pdf]
- Slides explaining Variational Autoencoder [pdf]
- Lin, 2013, Network in network [pdf]
- Goodfellow, 2013, Maxout networks [pdf]
- Baccouche, 2011, Sequential Deep Learning for Human Action Recognition [pdf]
- LeCun, 2005, Off-Road Obstacle Avoidance through End-to-End Learning [pdf]
- Socher, 2012, Convolutional-Recursive Deep Learning for 3D Object Classification [pdf, code]
- Long, 2014, Fully Convolutional Networks for Semantic Segmentation [pdf]
- Chen, 2016, DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs [pdf]
- Hochreiter & Schmidhuber, 1997, Long short term memory [pdf]
- Bau, 2017, Network Dissection: Quantifying Interpretability of Deep Visual Representations [pdf]
- Wang, 2017, Learning to Detect Salient Objects with Image-level Supervision [pdf]
- He, 2015, Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification [pdf]
- Ioffe, 2015, Batch Normalization: Accelerating Deep Network Training b y Reducing Internal Covariate Shift [pdf]
- Russakovsky, 2014, ImageNet Large Scale Visual Recognition Challenge [pdf]
- He, 2016, Deep Residual Learning for Image Recognition (ResNet) [pdf]
- Zagoruyko, 2016, Wide Residual Networks (WRN) [pdf]
- Xie, 2017, Aggregated Residual Transformations for Deep Neural Networks (ResNeXt) [pdf]
- Simonyan, 2015, VERY DEEP CONVOLUTIONAL NETWORKS FOR LARGE-SCALE IMAGE RECOGNITION (VGG) [pdf]
- Szegedy, 2015, Going Deeper with Convolutions (Inception/GoogLeNet) [pdf]
- Gastaldi, 2017, Shake-shake regularization (Shake-Shake) [pdf]
- DeVries, 2017, Improved Regularization of Convolutional Neural Networks with Cutout (CutOut) [pdf]
- Explain Network-in-Network [slide]
Person re-identification
- Zhao et al., 2013, Unsupervised salience learning for person re-identification [pdf, code]
- Loy et al., 2013, Person re-identification by manifold ranking [pdf, code]
- Roth et al., 2014, Mahalanobis Distance Learning for Person Re-Identification [pdf]
- Zheng et al., 2012, Person Re-identification by Probabilistic Relative Distance Comparison [pdf]
- Farenzena et al., 2010, Person Re-Identification by Symmetry-Driven Accumulation of Local Features [pdf, code]
- Bazzani et al., 2010, Multiple-shot Person Re-identification by HPE signature [pdf]
- Cho & Yoon, 2016, Improving Person Re-identification via Pose-aware Multi-shot Matching [pdf]
- Wu et al., 2016, PersonNet: Person Re-identification with Deep Convolutional Neural Networks [pdf]
- Ahmed et al., 2015, An Improved Deep Learning Architecture for Person Re-Identification [pdf]
- Saul et al., 2005, Spectral Methods for Dimensionality Reduction [pdf]
- Belkin, 2003, Laplacian Eigenmaps for Dimensionality Reduction and Data Representation [pdf]
- Li et al., 2014, DeepReID: Deep Filter Pairing Neural Network for Person Re-Identification [pdf]
- Weinberger, 2009, Distance Metric Learning for Large Margin Nearest Neighbor Classification [pdf]
Remote sensing
- PennState, Remotely sensed image data [chapter]
- Introduction to hyperspectral imaging [pdf]
- Li et al., Hyperspectral Image segmentation, deblurring, and spectral analysis for material identification [pdf]
- Luo & Chanussot, 2009, HYPERSPECTRAL IMAGE CLASSIFICATION BASED ON SPECTRAL AND
GEOMETRICAL FEATURES [pdf]
- Chen et al., 2011, Hyperspectral Image Classification Using Dictionary-Based Sparse Representation [pdf]
- Zhou & Camps-Valls, 2007, Semi-supervised graph-based hyperspectral image classification [code]
- Collection of remote sensing image processing [html]
- Villa et al., 2011, Hyperspectral Image Classification With Independent Component Discriminant Analysis [pdf]
- Keshava & Mustard, 2002, Spectral unmixing [pdf]
- Iordache et al., 2012, Total Variation Spatial Regularization for Sparse Hyperspectral Unmixing [pdf, code]
- Iordache 2011, Sparse unmixing of hyperspectral data [pdf]
- MIT, Multispectral image segmentation [pdf]
- Mitra et al., 2004, Segmentation of multispectral remote sensing images using active support vector machines [pdf]
- Jordan & Angelopoulou, 2012, SUPERVISED MULTISPECTRAL IMAGE SEGMENTATION WITH POWER WATERSHEDS [pdf, data]
- Li et al., 2006, Spatial Kernel K-Harmonic Means Clustering for Multi-spectral Image Segmentation [pdf]
- Adams et al., 1995, Classification of Multispectral Images Based on Fractions of Endmembers: Application to Land-Cover Change in the Brazilian Amazon [pdf]
- Xu & Gong, 2004, Land-use/Land-cover Classification with Multispectral and Hyperspectral EO-1 Data [pdf]
- Multispectral image processing [html]
- Gabriel Peyre: Multispectral imaging: compression, denoising [html]
- Shippert, 2003, Introduction to Hyperspectral Image Analysis [pdf]
- Zhao et al., 2011, Hyperspectral imagery super-resolution by sparse representation and spectral regularization [html], evaluation: PSNR, SSIM, FSIM
- Plaza et al., 2006, Advanced Processing of Hyperspectral Images [pdf]
- Unay (These), MULTISPECTRAL IMAGE PROCESSING AND PATTERN RECOGNITION TECHNIQUES FOR QUALITY
INSPECTION OF APPLE FRUITS [pdf]
- Cagnazzo, 2007, Region-Based Transform Coding of Multispectral Images [pdf]
- Cagnazzo, 2007, Improved Class-Based Coding of Multispectral Images With Shape-Adaptive Wavelet Transform [pdf]
- Landsat image gallery [html]
- Peng, 2014, PhD thesis, Automatic Denoising and Unmixing in Hyperspectral Image Processing [pdf]
- Zhang 2007, ADAPTIVE BILATERAL FILTER FOR SHARPNESS ENHANCEMENT AND NOISE [pdf]
- Zelinski, 2006, Denoising Hyperspectral Imagery and Recovering Junk Bands using Wavelets and Sparse Approximation [pdf]
- Lam 2012, Denoising Hyperspectral Images Using Spectral Domain Statistics [pdf]
Visual perception
- Hilbert, Color constancy and the complexity of color [pdf]
Visual attention / Saliency map
- Itti et al., 1998, A Model of Saliency-Based Visual Attention for Rapid Scene Analysis [pdf]
- Torralba et al., 2006, Contextual guidance of eye movements and attention in real-world scenes: The role of global features on object search [pdf]
- Perazzi et al., 2012, Saliency Filters: Contrast Based Filtering for Salient Region Detection [pdf, material]
- Cheng et al., 2011, Global contrast based salient region detection [pdf, html]
- Computational attention benchmark
- Coutrot & Guyader, 2014, How saliency, faces, and sound influence gaze in dynamic social scenes [pdf]
- Judd et al., 2009, Learning to Predict Where Humans Look [pdf]
- Zhang & Sclaroff, 2013, Saliency detection: A boolean map approach [pdf]
- Erdem, 2013, Visual saliency estimation by nonlinearly integrating features using region covariances [pdf]
- Harel et al., 2006, Graph-based visual saliency [pdf]
- Peters & Itti, 2008, Applying computational tools to predict gaze direction in interactive visual environments [pdf]
- Achanta 2009, Frequency-tuned Salient Region Detection (1000 images + object-based ground truth) [pdf, html]
- Frintrop 2015, Traditional Saliency Reloaded: A Good Old Model in New Shape [pdf]
- Margolin 2014, How to Evaluate Foreground Maps? (F-measure) [pdf]
- Li, 2014, The secrets of salient objects: dataset, evaluation, code [pdf, html]
- Goferman et al., 2012, Context-Aware Saliency Detection [pdf]
- Zhao et al., 2015, Saliency Detection by Multi-Context Deep Learning [pdf]
- Hou and Zhang, 2007, Saliency Detection: A Spectral Residual Approach [pdf]
- Kruger et al, 2013, Deep Hierarchies in the Primate Visual Cortex: What Can We Learn For Computer Vision? [pdf]
Super-resolution & Demosaicking
- Protter et al., 2009, Generalizing the NonLocal-Means to super resolution reconstruction [pdf]
- Takeda et al., 2009, Super-Resolution Without Explicit Subpixel Motion Estimation [pdf]
- Farsiu et al., 2006, Multiframe Demosaicing and Super-Resolution of Color Images [pdf]
- Elad and Hel-Or, 2001, A Fast Super-Resolution Reconstruction Algorithm for Pure Translational Motion and Common
Space-Invariant Blur [pdf]
- Other papers on image and signal processing from Michael Elad's site
- Shi et al., 2014, Sub-Pixel Layout for Super-Resolution with Images in the Octic Group [pdf]
- Yang et al., 2014, Single-Image Super-Resolution: A Benchmark [pdf]
- Software: bayesian [html], reproductible research [html]
- Babacan et al., 2011, Variational Bayesian Super resolution [pdf]
- Vandewalle et al., 2006, A frequency domain approach to Registration of Aliased Images with Application to Super-resolution [pdf]
- Yang et al., 2010, Image super-resolution via sparse representation
- Yang et al., 2008, Image super-resolution as sparse representation of raw image patches [pdf]
- Yang & Huang, Image super resolution: historical overview and future challenge [pdf]
- Park 2003, Super resolution image reconstruction: technical overview [pdf]
- Source code: [epfl], [Milanfar]
- Slide: Introduction to image super-resolution [pdf], Intoduction to image filtering, image denoising, image interpolation [pdf]
- Liu 2014, On Bayesian Adaptive Video Super Resolution [pdf]
- Elad 1997, Restoration of a Single Superresolution Image from Several Blurred, Noisy, and Undersampled Measured Images [pdf] (using POCS)
- Joshi 2008, PSF Estimation using Sharp Edge Prediction [pdf]
- Timofte, 2015, Semantic super resolution: When and where is it useful? [pdf]
- Lu et al., 2010, Demosaicking by Alternating Projections: Theory and Fast One-Step Implementation [pdf, code]
- Zhang's site [html],
- Zhang's Demosaicing code [html]
- Daisuke Kiku et al., 2013, Residual interpolation for color image demosaicking [pdf, code]
- Alleysson et al., Color demosaicing by estimating luminance and opponent chromatic signals in the Fourier domain [pdf]
- Li et al., Image demosaicking: a systematic survey [pdf]
- Color interpolation [pdf]
- False color and zipper effect: Mirko Guarnera et al., 2010,Adaptive color demosaicing and false color removal [pdf]
- Gunturk et al., 2005, Demosaicking CFA Interpolation [pdf]
- Chung & Chan, Color Demosaicing Using Variance of Color Differences [pdf]
- Farsiu et al., 2006, Multiframe Demosaicing and Super-Resolution of Color Images [pdf]
- Gotoh, Color super resolution from a single CCD [pdf]
- Dubois 2005, Frequency-Domain Methods for Demosaicking of Bayer-Sampled Color Images [pdf]
- Adams 1997, Design of practical color filter array interpolation algorithms for digital cameras [pdf]
- Su et al., Effective False Color Suppression of Demosaicing Using Direction Inversion and Bidirectional Signal Correlation [pdf]
- Mukherje et al., False Color Suppression in Demosaiced Color Images [pdf]
- Tomaselli et al., False-colors removal on the YCrCb color space [pdf]
- Huang et al., 2014, Dictionary Learning based Color Demosaicing for Plenoptic Cameras [pdf]
- Buades et al., 2009, Self-similarity driven color demosaicking [pdf] (explain zipper effect and its evaluation)
- Zhang & Wandell, S-CIELAB [pdf, code]
- Kaiming, 2010, Guided Image Filtering [pdf, code]
- Wu et al., 2015, EFFICIENT REGRESSION PRIORS FOR POST-PROCESSING DEMOSAICED IMAGES [pdf]
- Pekkucuksen & Altunbasak, 2010, Gradient based threshold free color filter array interpolation (GBTF) [code]
- Zhang & Wu, 2005, Color demosaicking via directional linear minimum mean square-error estimation (DLMMSE) [pdf]
Zhu & Milanfar, 2010, Automatic Parameter Selection for Denoising Algorithms Using a No-Reference Measure of Image Content (Q metric) [pdf]
Detection & Recognition
- Viola & Jones, 2004, Robust Real-Time Face Detection [pdf]
- Viola & Jones, 2001, Rapid Object Detection using a Boosted Cascade of Simple Features [pdf]
- Simple explanation of Viola-Jones face detection [html]
- Freund & Schapire, 1997, A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting [pdf]
- Freund & Schapire, 1999, A Short Introduction to Boosting [pdf]
- Schapire, Explaining AdaBoost [pdf]
- Stauffer & Grimson, 1998, Adaptive background mixture models for real-time tracking [pdf]
- Bouwmans et al., 2008, Background Modeling using Mixture of Gaussians for Foreground Detection - A Survey [pdf]
- Piccardi, 2004, Background subtraction techniques: a review [pdf]
- Haines & Xiang, 2012, Background Subtraction with Dirichlet Processes [pdf]
- Sheikh & Shah, 2005, Bayesian Object Detection in Dynamic Scenes [pdf, code]
- Narayana: Motion segmentation/Background subtraction - pixelwise kernel variance, joint domaine-range, optical flow [pdf&code]
- Seth Benton, 2008, EE Times, Simple explanation of Background subtraction, Part1: Matlab models [pdf & code]
- Lacassagne & Manzanera, 2009, Motion detection: fast and robust algorithms for embedded systems [pdf]
- Pintea et al., 2014, Deja Vu: Motion Prediction in Static Images [pdf]
- Chen, 2013, Blessing of Dimensionality: High-dimensional Feature and Its Efficient Compression for Face Verification [pdf]
- Schwartz, 2009, Human Detection Using Partial Least Squares Analysis [pdf]
- Zhu & Ramanan 2012, Face detection, pose estimation and landmark localization in the wild [pdf, code]
- Girshick, 2015, Fast R-CNN [pdf]
- Lu, 2015, Surpassing human-level face verification performance on LFW with gaussian face [pdf]
Evaluation & Dataset
- Caltech Pedestrian dataset and others [html]
- Dollar et al., 2012, Pedestrian Detection: An Evaluation of the State of the Art [pdf]
- Mariano et al., 2002, Performance Evaluation of Object Detection Algorithms [pdf]
- Nascimento et al., 2006, Performance evaluation of object detection algorithms for video surveillance [pdf]
Facial Landmark Detection
- Facial landmark detection [html]
- Wu & Ji, 2015, Robust Facial Landmark Detection under Significant Head Poses and Occlusion [pdf]
Segmentation
- Li et al., 2010, Distance Regularized Level Set Evolution and Its Application to Image Segmentation [pdf, code]
- Shi & Malik, 2000, Normalized Cuts and Image Segmentation [pdf, code, code]
- Achanta et al., 2011, SLIC Superpixels Compared to State-of-the-art Superpixel Methods [pdf, pdf, code]
Liu (UW), A Review of Computer Vision Segmentation Algorithms [pdf]
Tracking
- Comaniciu et al., 2000, Real-Time Tracking of Non-Rigid Objects using Mean Shift [pdf]
- Comaniciu & Meer, 2002, .Mean Shift: a robust approach toward feature space analysis [pdf].
- Comaniciu & Meer, Mean shift analysis and applications [pdf]
- Matlab Code for mean shift video tracking [code]
- Arulampalam et al., 2002, A Tutorial on Particle Filters for Online Nonlinear/Non-Gaussian Bayesian Tracking [pdf]
- Mihaylova et al., Object Tracking by Particle Filtering Techniques in Video Sequences [pdf]
- Hue et al., 2002, Tracking Multiple Objects with Particle Filtering [pdf]
- Hue et al., 2002, Sequential Monte Carlo Methods for Multiple Target Tracking and Data Fusion [pdf, code]
- Okuma et al., 2004, A Boosted Particle Filter: Multitarget Detection and Tracking [pdf, code requires OpenSUSE 32bit for using the built-in hockey player detector]
- Breitenstein et al., 2009, Robust Tracking-by-Detection using a Detector Confidence Particle Filter [pdf]
- Yilmaz et al., 2006, Object tracking:a survey [pdf]
- Li et al., 2013, A Survey of Appearance Models in Visual Object Tracking [pdf]
- Ross et al., 2011, Incremental learning for visual tracking [pdf, code]
Feature extraction/ image analysis
- Burt & Adelson, 1983, The Laplacian Pyramid as a Compact Image Code [pdf]
- Simoncelli, the steerable pyramid [html]
- Mallat, A wavelet tour of signal processing [html]
- Freeman & Adelson, 1991, The design and use of steerable filters [pdf]
- Oliva 2013, The Art of Hybrid Images: Two for the View of One [pdf]
Watermarking
- Provos & Honeyman, 2003, Hide and Seek: An Introduction to Steganography [pdf]
- Shatnawi, 2012, A New Method in Image Steganography with Improved Image Quality [pdf]
SITES
- Zhang's homepage: papers & codes according topics
- Image database: CMU - CFA base (Image Analysis Lab), CVonline
- Supervised learning and optimization [site]
BOOKS
- Code, lectures for the book "Image processing, analysis and machine vision" by Sonka et al., 2007.
- Ramesh Jain, Rangachar Kasturi, Brian G. Schunck, Machine vision, 1995 [pdf]
- Goodfellow, Deep learning [html]
- Nielsen, Neural Networks and Deep Learning [html]
- Shawe-Taylor, Kernel methods for Pattern Analysis [pdf]
- Christopher Bishop, 2006, Pattern recognition and Machine Learning [pdf, code]
- Other matlab codes for machine learning (Kmeans, HMM, regression, EM, GMM,...)
- Kelleher, Mac Namee, and D'Arcy, Fundamentals of Machine Learning for Predictive Data Analytics, Case study: Galaxy classification
Note: This site compiles links for books, papers, codes, and datasets in the field of vision. Full copyright remains with the original authors.