The Living Thing / Notebooks : Machine vision

Practical tips, tricks and algorithms.

See also artificial neural network, Markov random fields <{filename}graphical_models.rst>`_and `synestizer, random forests.

Software

Refs

BaFB94
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.
FlWe06
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
GKTN08
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.
GSKP11
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.
Kawa07
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.
KhBD04
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.
LNCS16
Lopez-Paz, D., Nishihara, R., Chintala, S., Schölkopf, B., & Bottou, L. (2016) Discovering Causal Signals in Images. arXiv:1605.08179 [Cs, Stat].
MKGK16
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].
MeSK13
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.
Ning05
Ning, F. (2005) Toward automatic phenotyping of developing embryos from videos. IEEE Trans. Image Process., 14, 1360–1371. DOI.
NoLB04
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.
NuKV03
Nummiaro, K., Koller-Meierb, E., & Van Gool, L. (2003) An adaptive color-based particle filter. Image and Vision Computing, 21(1), 99–110.
SáMS13
Sánchez Pérez, J., Monzón López, N., & Salgado de la Nuez, A. (2013) Robust Optical Flow Estimation. Image Processing On Line, 3, 252–270. DOI.
WiBö15
Wiatowski, T., & Bölcskei, H. (2015) A Mathematical Theory of Deep Convolutional Neural Networks for Feature Extraction. arXiv:1512.06293 [Cs, Math, Stat].