One of the most successful frameworks in computational neuroscience is modeling visual processing using the statistical structure of natural images. In this framework, the visual system of the brain constructs a model of the statistical regularities of the incoming visual data. This enables the visual system to perform efficient probabilistic inference. The same framework is also very useful in engineering applications such as image processing and computer vision. This book is the first comprehensive introduction to the multidisciplinary field of natural image statistics. The book starts with a review of background material in signal processing and neuroscience, which makes it accessible to a wide audience. The book then explains both the basic theory and the most recent advances in a coherent and user-friendly manner. This structure, together with the included exercises and computer assignments, also make it an excellent textbook. "Natural Image Statistics" is a timely and valuable resource for advanced students and researchers in any discipline related to vision, such as neuroscience, computer science, psychology, electrical engineering, cognitive science or statistics.
Cited By
- Nguyen T and Nguyen T (2021). A Comprehensive Taxonomy of Dynamic Texture Representation, ACM Computing Surveys, 55:1, (1-39), Online publication date: 31-Jan-2023.
- Laiadi O, Ouamane A, Boutellaa E, Benakcha A, Taleb-Ahmed A and Hadid A (2019). Kinship verification from face images in discriminative subspaces of color components, Multimedia Tools and Applications, 78:12, (16465-16487), Online publication date: 1-Jun-2019.
- Yi P and Ching S (2019). Multiple timescale online learning rules for information maximization with energetic constraints, Neural Computation, 31:5, (943-979), Online publication date: 1-May-2019.
- Zhang K and Zhang L (2018). Extracting hierarchical spatial and temporal features for human action recognition, Multimedia Tools and Applications, 77:13, (16053-16068), Online publication date: 1-Jul-2018.
- Arashloo S (2017). Multiscale binarised statistical image features for symmetric face matching using multiple descriptor fusion based on class-specific LDA, Pattern Analysis & Applications, 20:1, (113-126), Online publication date: 1-Feb-2017.
- Leboran V, Garcia-Diaz A, Fdez-Vidal X and Pardo X (2017). Dynamic Whitening Saliency, IEEE Transactions on Pattern Analysis and Machine Intelligence, 39:5, (893-907), Online publication date: 1-May-2017.
- Bai S (2017). Growing random forest on deep convolutional neural networks for scene categorization, Expert Systems with Applications: An International Journal, 71:C, (279-287), Online publication date: 1-Apr-2017.
- Kolesnikov A and Lampert C PixelCNN models with auxiliary variables for natural image modeling Proceedings of the 34th International Conference on Machine Learning - Volume 70, (1905-1914)
- Hirayama J, Hyvärinen A and Kawanabe M SPLICE Proceedings of the 34th International Conference on Machine Learning - Volume 70, (1491-1500)
- Henriques J and Vedaldi A Warped convolutions Proceedings of the 34th International Conference on Machine Learning - Volume 70, (1461-1469)
- Zhu F, Shao L, Xie J and Fang Y (2016). From handcrafted to learned representations for human action recognition, Image and Vision Computing, 55:P2, (42-52), Online publication date: 1-Nov-2016.
- Zhu H, Long M, Wang J and Cao Y Deep Hashing Network for efficient similarity retrieval Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, (2415-2421)
- Liu C, Xu W, Wu Q and Yang G (2016). Learning motion and content-dependent features with convolutions for action recognition, Multimedia Tools and Applications, 75:21, (13023-13039), Online publication date: 1-Nov-2016.
- Pang Y, Khor E and Ooi S Biometric Access Control with High Dimensional Facial Features Proceedings, Part II, of the 21st Australasian Conference on Information Security and Privacy - Volume 9723, (437-445)
- McIntosh L, Maheswaranathan N, Nayebi A, Ganguli S and Baccus S Deep learning models of the retinal response to natural scenes Proceedings of the 30th International Conference on Neural Information Processing Systems, (1369-1377)
- Faridul H, Pouli T, Chamaret C, Stauder J, Reinhard E, Kuzovkin D and Tremeau A (2016). Colour Mapping, Computer Graphics Forum, 35:1, (59-88), Online publication date: 1-Feb-2016.
- Chen G, Clarke D, Giuliani M, Gaschler A and Knoll A (2015). Combining unsupervised learning and discrimination for 3D action recognition, Signal Processing, 110:C, (67-81), Online publication date: 1-May-2015.
- Borgolte K, Kruegel C and Vigna G Meerkat Proceedings of the 24th USENIX Conference on Security Symposium, (595-610)
- Feige U Why are Images Smooth? Proceedings of the 2015 Conference on Innovations in Theoretical Computer Science, (229-236)
- Mairal J, Bach F and Ponce J (2014). Sparse Modeling for Image and Vision Processing, Foundations and Trends® in Computer Graphics and Vision, 8:2-3, (85-283), Online publication date: 1-Dec-2014.
- Nasri M, Saryazdi S and Nezamabadi-Pour H (2013). A Fast Adaptive Salt and Pepper Noise Reduction Method in Images, Circuits, Systems, and Signal Processing, 32:4, (1839-1857), Online publication date: 1-Aug-2013.
- Main L, Cowley B, Kneller A and Thornton J Evaluating Sparse Codes on Handwritten Digits Proceedings of the 26th Australasian Joint Conference on AI 2013: Advances in Artificial Intelligence - Volume 8272, (396-407)
- Chen G, Zhang F, Giuliani M, Buckl C and Knoll A Unsupervised Learning Spatio-temporal Features for Human Activity Recognition from RGB-D Video Data Proceedings of the 5th International Conference on Social Robotics - Volume 8239, (341-350)
- Mohammed R, Mohammed S and Schwabe L BatGaze Proceedings of the 2012 international conference on Brain Informatics, (85-96)
- Thornton J, Main L and Srbic A Fixed frame temporal pooling Proceedings of the 25th Australasian joint conference on Advances in Artificial Intelligence, (707-718)
- García-Manso A, García-Orellana C, Gallardo-Caballero R, Lanconelli N, González-Velasco H and Macías-Macías M Robustness of a CAD system on digitized mammograms Proceedings of the 5th INNS IAPR TC 3 GIRPR conference on Artificial Neural Networks in Pattern Recognition, (127-138)
- Ma L and Xu K Antialiasing recovery for edit propagation Proceedings of the 10th International Conference on Virtual Reality Continuum and Its Applications in Industry, (125-130)
- Yang L, Sander P, Lawrence J and Hoppe H (2011). Antialiasing recovery, ACM Transactions on Graphics, 30:3, (1-9), Online publication date: 1-May-2011.
- Matsuda Y and Yamaguchi K Partial extraction of edge filters by cumulant-based ICA under highly overcomplete model Proceedings of the 17th international conference on Neural information processing: models and applications - Volume Part II, (633-640)
- Druckmann S and Chklovskii D Over-complete representations on recurrent neural networks can support persistent percepts Proceedings of the 23rd International Conference on Neural Information Processing Systems - Volume 1, (541-549)
- Lyu S Estimating vignetting function from a single image for image authentication Proceedings of the 12th ACM workshop on Multimedia and security, (3-12)
Index Terms
- Natural Image Statistics: A Probabilistic Approach to Early Computational Vision.