Based on the idea proposed in [Informative websites related to OpenCV](http://answers.opencv.org/question/69691/informative-websites-related-to-opencv/) by Sturkmen, I think that it would also be useful to create a list about implementations based on papers and publications:
1. implemented with OpenCV and/or
2. that can be used/integrated easily with OpenCV
***(maybe one toy example with each one would be fantastic)***
As OpenCV is under the open-source BSD license, it would also be interesting that these algorithms would be BSD or similar. So, I am going to put my list (Maybe some algorithms should not be in this list, so do it together!).
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**TRACKING**
- **Object tracking**
- *Real-time Compressive Tracking*
> (http://www4.comp.polyu.edu.hk/~cslzhang/CT/CT.htm) implementation integrated with opencv> Zhang, K., Zhang, L., & Yang, M. H.> (2012). Real-time compressive> tracking. In Computer Vision–ECCV 2012
> (pp. 864-877). Springer Berlin> Heidelberg.
- *Accurate scale estimation for robust visual tracking*
> Implemented in DLIB library http://dlib.net/ > Danelljan, M., Häger, G., Khan, F., &> Felsberg, M. (2014). Accurate scale> estimation for robust visual> tracking. In British Machine Vision> Conference, Nottingham, September 1-5,> 2014. BMVA Press. (winning algorithm from last year's Visual Object> Tracking Challenge. )
**FACE PROCESSING**
- **Face pre-processing**
- *Tan&Triggs processing*> A efficient image pre-processing> normalization algorithm to deal with> difficult lighting conditions: Tan,> X., & Triggs, B. (2010). Enhanced> local texture feature sets for face> recognition under difficult lighting> conditions. Image Processing, IEEE> Transactions on, 19(6), 1635-1650.> implementation: https://github.com/bytefish/opencv/blob/master/misc/tan_triggs.cpp (BSD license)
- *Real-Time Face Pose Estimation*
> One Millisecond Face Alignment with an> Ensemble of Regression Trees Kazemi,> V., & Sullivan, J. (2014, June). One> millisecond face alignment with an> ensemble of regression trees. In> Computer Vision and Pattern> Recognition (CVPR), 2014 IEEE> Conference on (pp. 1867-1874). IEEE.> Implemented in DLIB library http://dlib.net/> demo snippet: https://gist.github.com/berak/b23262a9cb08a9d0a6d3#file-dlib-landmarks-example
- *Face landmarks detector (face alignment)*
> Cao X, Wei Y, Wen F, et al. Face> alignment by explicit shape> regression[J]. International Journal> of Computer Vision, 2014, 107(2):> 177-190.> implementation: https://github.com/delphifirst/FaceX/> demo snippet: https://gist.github.com/berak/79aeb39b59222917c558#file-facex-example
- *Eye localization: Average of Synthetic Exact Filters*
> Bolme, D. S., Draper, B., & Beveridge,> J. R. (2009, June). Average of> synthetic exact filters. In Computer> Vision and Pattern Recognition, 2009.> CVPR 2009. IEEE Conference on (pp.> 2105-2112). IEEE.> implementation: https://github.com/laoyang/ASEF
- *Eye localization: Accurate eye centre localisation by means of gradient*
> Timm, F., & Barth, E. (2011, March).> Accurate Eye Centre Localisation by> Means of Gradients. In VISAPP (pp.> 125-130).> implementation: https://github.com/trishume/eyeLike
**FACE DETECTION**
- *PICO Face detection*
> N. Markus, M. Frljak, I. S. Pandzic,> J. Ahlberg and R. Forchheimer, "Object> Detection with Pixel Intensity> Comparisons Organized in Decision> Trees", http://arxiv.org/abs/1305.4537> implementation: https://github.com/nenadmarkus/pico> license: https://github.com/nenadmarkus/pico/blob/master/LICENSE
**FRAMEWORKS**
- **Deep learning: CAFFE**
> Jia, Y., Shelhamer, E., Donahue, J.,> Karayev, S., Long, J., Girshick, R.,> ... & Darrell, T. (2014, November). > Caffe: Convolutional architecture for> fast feature embedding. In Proceedings> of the ACM International Conference> on Multimedia (pp. 675-678). ACM.> Caffe is released under the BSD 2-Clause license.> http://caffe.berkeleyvision.org/> CAFFE & Opencv: http://answers.opencv.org/question/72321/how-can-caffe-be-interfaced-using-opencv/
- **Machine learning framework: mlpack**
> mlpack: a scalable C++ machine learning library http://mlpack.org/> Curtin, R. R., Cline, J. R., Slagle,> N. P., March, W. B., Ram, P., Mehta,> N. A., & Gray, A. G. (2013). MLPACK:> A scalable C++ machine learning> library. The Journal of Machine> Learning Research, 14(1), 801-805.> implementation: https://github.com/mlpack/mlpack (BSD License)
- **Machine learning framework: LIBSVM**
> LIBSVM -- A Library for Support Vector Machines> https://www.csie.ntu.edu.tw/~cjlin/libsvm/> Chang, C. C., & Lin, C. J. (2011).> LIBSVM: A library for support vector> machines. ACM Transactions on> Intelligent Systems and Technology> (TIST), 2(3), 27.
- **Framework for face processing and recognition**
> Open Source Biometric Recognition http://openbiometrics.org/ License: Apache 2.0 (requires Qt and OpenCV).> Klontz, J. C., Klare, B. F., Klum, S.,> Jain, A. K., & Burge, M. J. (2013,> September). Open source biometric> recognition. In Biometrics: Theory,> Applications and Systems (BTAS), 2013> IEEE Sixth International Conference on> (pp. 1-8). IEEE.
- **general purpose library**
> Dlib is a general purpose cross-platform C++ library designed using contract programming and modern C++ techniques. It is open source software and licensed under the Boost Software License. http://dlib.net/> OpenCV image objects can be converted into a form usable by dlib routines by using cv_image. > You can also convert from a dlib matrix or image to an OpenCV Mat using dlib::toMat().
- **human action recognition**
> https://github.com/DAIGroup/BagOfKeyPoses> License: Apache 2.0 License> Chaaraoui, A. A., Climent-Pérez, P., &> Flórez-Revuelta, F. (2013).
> Silhouette-based human action> recognition using sequences of key> poses. Pattern Recognition Letters,> 34(15), 1799-1807.> http://dx.doi.org/10.1016/j.patrec.2013.01.021
**TEXTURE DESCRIPTORS**
- **LBP Modification: High-Dimensional-LBP**
> implementation of high dimensional lbp feature for face recognition based on> Chen, D., Cao, X., Wen, F., & Sun, J. (2013, June). Blessing of dimensionality: High-dimensional > feature and its efficient compression for face verification. In Computer Vision and Pattern Recognition > (CVPR), 2013 IEEE Conference on (pp. 3025-3032). IEEE.> Chen, B. C., Chen, C. S., & Hsu, W.> (2014). Review and Implementation of> High-Dimensional Local Binary > Patterns and Its Application to Face> Recognition. Inst. Inf. Sci., Academia> Sinica, Taipei, Taiwan, Tech. Rep.> TR-IIS-14-003.> implementation: https://github.com/bcsiriuschen/High-Dimensional-LBP
**BACKGROUND SUBTRACTION**
- **Background subtraction: BGSLibrary**
> implementation: https://github.com/andrewssobral/bgslibrary> Sobral, A. (2013, June). BGSLibrary:> An opencv c++ background subtraction> library. In IX Workshop de Visao> Computacional (WVC’2013), Rio de
> Janeiro, Brazil.
**Vehicle Detection, Tracking and Counting**
- **Vehicle Detection, Tracking and Counting**
> web page: https://www.behance.net/gallery/Vehicle-Detection-Tracking-and-Counting/4057777> Vehicle tracking using Haar Cascades or Background Subtraction (BS)
**AUGMENTED REALITY**
- **Marker Detection for AR Applications**
> https://infi.nl/nieuws/marker-detection-for-augmented-reality-applications/> Hirzer, M. (2008, October). Marker> detection for augmented reality> applications. In Seminar/Project Image> Analysis> Graz.(http://studierstube.icg.tugraz.at/thesis/marker_detection.pdf)> Source code: https://infi.nl/files/overig/MarkerDetectionSource.zip
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