The Living Thing / Notebooks :

Machine vision

Practical tips, tricks and algorithms.

See also artificial neural network, Markov random fields, synestizer and random forests.



Barron, J. L., Fleet, D. J., & Beauchemin, S. S.(1994) Performance of optical flow techniques. International Journal of Computer Vision, 12(1), 43–77. DOI.
Fleet, D. J., & Weiss, Y. (2006) Optical Flow Estimation. In N. Paragios, Y. Chen, & O. Faugeras (Eds.), Handbook of mathematical models in computer vision. New York: Springer
Glocker, B., Komodakis, N., Tziritas, G., Navab, N., & Paragios, N. (2008) Dense image registration through MRFs and efficient linear programmingq. Medical Image Analysis, 12(6), 731–741. DOI.
Glocker, B., Sotiras, A., Komodakis, N., & Paragios, N. (2011) Deformable Medical Image Registration: Setting the State of the Art with Discrete Methods. Annual Review of Biomedical Engineering, 13(1), 219–244. DOI.
Kawamoto, K. (2007) Optical Flow–Driven Motion Model with Automatic Variance Adjustment for Adaptive Tracking. In Y. Yagi, S. B. Kang, I. S. Kweon, & H. Zha (Eds.), Computer Vision – ACCV 2007 (pp. 555–564). Springer Berlin Heidelberg DOI.
Khan, Z., Balch, T., & Dellaert, F. (2004) An MCMC-Based Particle Filter for Tracking Multiple Interacting Targets. In T. Pajdla & J. Matas (Eds.), Computer Vision - ECCV 2004 (pp. 279–290). Springer Berlin Heidelberg DOI.
Lopez-Paz, D., Nishihara, R., Chintala, S., Schölkopf, B., & Bottou, L. (2016) Discovering Causal Signals in Images. arXiv:1605.08179 [Cs, Stat].
Mehri, S., Kumar, K., Gulrajani, I., Kumar, R., Jain, S., Sotelo, J., … Bengio, Y. (2016) SampleRNN: An Unconditional End-to-End Neural Audio Generation Model. arXiv:1612.07837 [Cs].
Meinhardt-Llopis, E., Sánchez Pérez, J., & Kondermann, D. (2013) Horn-Schunck Optical Flow with a Multi-Scale Strategy. Image Processing On Line, 3, 151–172. DOI.
Ning, F. (2005) Toward automatic phenotyping of developing embryos from videos. IEEE Trans. Image Process., 14, 1360–1371. DOI.
Noyer, J. C., Lanvin, P., & Benjelloun, M. (2004) Model-based tracking of 3D objects based on a sequential Monte-Carlo method. In Conference Record of the Thirty-Eighth Asilomar Conference on Signals, Systems and Computers, 2004 (Vol. 2, p. 1744–1748 Vol.2). DOI.
Nummiaro, K., Koller-Meierb, E., & Van Gool, L. (2003) An adaptive color-based particle filter. Image and Vision Computing, 21(1), 99–110.
Sánchez Pérez, J., Monzón López, N., & Salgado de la Nuez, A. (2013) Robust Optical Flow Estimation. Image Processing On Line, 3, 252–270. DOI.
Wiatowski, T., & Bölcskei, H. (2015) A Mathematical Theory of Deep Convolutional Neural Networks for Feature Extraction. arXiv:1512.06293 [Cs, Math, Stat].