Bibliography

[Ahonen04] Ahonen, Timo, Abdenour Hadid, and Matti Pietikäinen. “Face recognition with local binary patterns.” European conference on computer vision. Springer Berlin Heidelberg, 2004.

[Acharya05] Acharya, T., and A. Ray. Image Processing: Principles and Applications. New York: Wiley, 2005.

[Adelson84] Adelson, E. H., C. H. Anderson, J. R. Bergen, P. J. Burt, and J. M. Ogden. “Pyramid methods in image processing,” RCA Engineer 29 (1984): 33–41.

[Agarwal08] Agrawal, M., K. Konolige, and M. R. Blas. “CenSurE: Center Surround Extremas for Realtime Feature Detection and Matching,” European Conference on Computer Vision, 2008.

[Ahmed74] Ahmed, N., T. Natarajan, and K. R. Rao. “Discrete cosine transform,” IEEE Transactions on Computers 23 (1974): 90–93.

[Alahi12] Alahi, Alexandre, Raphael Ortiz, and Pierre Vandergheynst. “Freak: Fast retina keypoint.” Computer vision and pattern recognition (CVPR), 2012 IEEE conference on. IEEE, 2012.

[Arfken85] Arfken, G. “Convolution theorem,” in Mathematical Methods for Physicists, 3rd ed. (pp. 810–814), Orlando, FL: Academic Press, 1985.

[Arraiy] Arraiy Corporation, http://www.arraiy.ai.

[Arthur07] Arthur, D., and S. Vassilvitskii., “k-means++: the advantages of careful seeding.” Proceedings of the eighteenth annual ACM-SIAM symposium on discrete algorithms. (2007) pp. 1027–1035.

[Bajaj97] Bajaj, C. L., V. Pascucci, and D. R. Schikore. “The contour spectrum,” Proceedings of IEEE Visualization 1997 (pp. 167–173), 1997.

[Ballard81] Ballard, D. H. “Generalizing the Hough transform to detect arbitrary shapes,” Pattern Recognition 13 (1981): 111–122.

[Ballard82] Ballard, D., and C. Brown. Computer Vision. Englewood Cliffs, NJ: Prentice-Hall, 1982.

[Bardyn84] Bardyn, J. J. et al. “Une architecture VLSI pour un operateur de filtrage median,” Congres reconnaissance des formes et intelligence artificielle (vol. 1, pp. 557–566), Paris, 25–27, January 1984.

[Bay06] Bay, H., T. Tuytelaars, and L. V. Gool. “SURF: Speeded up robust features,” Proceedings of the Ninth European Conference on Computer Vision (pp. 404–417), May 2006.

[Bay08] Bay, Herbert, Andreas Ess, Tinne Tuytelaars, and Luc J. Van Gool. “SURF: Speeded up robust features,” Computer Vision and Image Understanding (CVIU) 110 (3) (2008) 346–359.

[Bayes1763] Bayes, T. “An essay towards solving a problem in the doctrine of chances. By the late Rev. Mr. Bayes, F.R.S. communicated by Mr. Price, in a letter to John Canton, A.M.F.R.S.,” Philosophical Transactions, Giving Some Account of the Present Undertakings, Studies and Labours of the Ingenious in Many Considerable Parts of the World 53 (1763): 370–418.

[Bazargani15] Bazargani, Hamid, Olexa Bilaniuk, and Robert Laganiere. “A fast and robust homography scheme for real-time planar target detection.” Journal of Real-Time Image Processing (2015): 1–20.

[Belongie02] Belongie, S., J. Malik, and J. Puzicha. “Shape matching and object recognition using shape contexts,” in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.24, no. 4, pp. 509–522, Apr 2002.

[Bhattacharyya43] Bhattacharyya, A. “On a measure of divergence between two statistical populations defined by probability distributions,” Bulletin of the Calcutta Mathematical Society 35 (1943): 99–109.

[BirchfieldTomasi99] Birchfield, Stan, and Carlo Tomasi. “Depth discontinuities by pixel-to-pixel stereo.” International Journal of Computer Vision 35.3 (1999): 269–293.

[Bishop07] Bishop, C. M. Pattern Recognition and Machine Learning. New York: Springer-Verlag, 2007.

[Black92] Black, M. J. “Robust incremental optical flow” (YALEU-DCS-RR-923), Ph.D. thesis, Department of Computer Science, Yale University, New Haven, CT, 1992.

[Black93] Black, M. J., and P. Anandan. “A framework for the robust estimation of optical flow,” Fourth International Conference on Computer Vision (pp. 231–236), May 1993.

[Black96] Black, M. J., and P. Anandan. “The robust estimation of multiple motions: Parametric and piecewise-smooth flow fields,” Computer Vision and Image Understanding 63 (1996): 75–104.

[Bobick96] Bobick, A., and J. Davis. “Real-time recognition of activity using temporal templates,” IEEE Workshop on Applications of Computer Vision (pp. 39–42), December 1996.

[Borgefors86] Borgefors, G., “Distance transformations in digital images,” Computer Vision, Graphics and Image Processing 34 (1986): 344–371.

[Bouguet04] Bouguet, J.-Y. “Pyramidal implementation of the Lucas Kanade feature tracker description of the algorithm,” http://robots.stanford.edu/cs223b04/algo_tracking.pdf.

[Boykov01] Boykov, Yuri, Olga Veksler, and Ramin Zabih. “Fast approximate energy minimization via graph cuts.” IEEE Transactions on pattern analysis and machine intelligence 23.11 (2001): 1222-1239.

[Bracewell65] Bracewell, R. “Convolution” and “Two-dimensional convolution,” in The Fourier Transform and Its Applications (pp. 25–50 and 243–244). New York: McGraw-Hill, 1965.

[Bradski] Gary Bradski, founder of OpenCV in 1999 and maintainer ever since. https://en.wikipedia.org/wiki/Gary_Bradski.

[Bradski00] Bradski, G., and J. Davis. “Motion segmentation and pose recognition with motion history gradients,” IEEE Workshop on Applications of Computer Vision, 2000.

[Bradski98a] Bradski, G. R. “Real time face and object tracking as a component of a perceptual user interface,” Proceedings of the 4th IEEE Workshop on Applications of Computer Vision, October 1998.

[Bradski98b] Bradski, G. R. “Computer video face tracking for use in a perceptual user interface,” Intel Technology Journal Q2 (1998): 705–740.

[Breiman01] Breiman, L. “Random forests,” Machine Learning 45 (2001): 5–32.

[Breiman02] Breiman, Leo. “Manual on setting up, using, and understanding random forests v3. 1.” Statistics Department, University of California Berkeley, CA, USA 1 (2002).

[Breiman84] Breiman, L., J. H. Friedman, R. A. Olshen, and C. J. Stone. Classification and Regression Trees. Monterey, CA: Wadsworth, 1984.

[Brown71] Brown, D. C. “Close-range camera calibration,” Photogrammetric Engineering 37 (1971): 855–866.

[Buades05] Mahmoudi, Mona, and Guillermo Sapiro. “Fast image and video denoising via nonlocal means of similar neighborhoods.” IEEE signal processing letters 12.12 (2005): 839–842.

[Burt83] Burt, P. J., and E. H. Adelson. “The Laplacian pyramid as a compact image code,” IEEE Transactions on Communications 31 (1983): 532–540.

[Calonder10] Calonder, M., V. Lepetit, C. Strecha, and P. Fua. Brief: Binary robust independent elementary features. In European Conference on Computer Vision (ECCV), 2010.

[Canny86] Canny, J. “A computational approach to edge detection,” IEEE Transactions on Pattern Analysis and Machine Intelligence 8 (1986): 679–714.

[Carpenter03] Carpenter, G. A., and S. Grossberg. “Adaptive resonance theory,” in M. A. Arbib (Ed.), The Handbook of Brain Theory and Neural Networks, 2nd ed. (pp. 87–90), Cambridge, MA: MIT Press, 2003.

[Carr04] Carr, H., J. Snoeyink, and M. van de Panne. “Progressive topological simplification using contour trees and local spatial measures,” 15th Western Computer Graphics Symposium, Big White, British Columbia, March 2004.

[Chambolle04] Chambolle, Antonin. “An algorithm for total variation minimization and applications.” Journal of Mathematical imaging and vision 20.1–2 (2004): 89–97.

[Chen05] Chen, D., and G. Zhang. “A new sub-pixel detector for x-corners in camera calibration targets,” WSCG Short Papers (2005): 97–100.

[Chu07] Chu, C.-T., S. K. Kim, Y.-A. Lin, Y. Y. Yu, G. Bradski, A. Y. Ng, and K. Olukotun. “Map-reduce for machine learning on multicore,” Proceedings of the Neural Information Processing Systems Conference (vol. 19, pp. 304–310), 2007.

[Ciresan11] Ciresan, Dan Claudiu, et al. “Convolutional neural network committees for handwritten character classification.” 2011 International Conference on Document Analysis and Recognition. IEEE, 2011.

[Colombari07] Colombari, A., A. Fusiello, and V. Murino. “Video objects segmentation by robust background modeling,” International Conference on Image Analysis and Processing (pp. 155–164), September 2007.

[Comaniciu99] Comaniciu, D., and P. Meer. “Mean shift analysis and applications,” IEEE International Conference on Computer Vision (vol. 2, p. 1197), 1999.

[Comaniciu03] Comaniciu, D. “Nonparametric information fusion for motion estimation,” IEEE Conference on Computer Vision and Pattern Recognition (vol. 1, pp. 59–66), 2003.

[Cooley65] Cooley, J. W., and O. W. Tukey. “An algorithm for the machine calculation of complex Fourier series,” Mathematics of Computation 19 (1965): 297–301.

[Criminisi13] Criminisi, Antonio, and Jamie Shotton, eds. Decision forests for computer vision and medical image analysis. Springer Science & Business Media, 2013.

[Csurka04] Csurka, Gabriella, et al. “Visual categorization with bags of keypoints.” Workshop on statistical learning in computer vision, ECCV. Vol. 1. No. 1–22. 2004.

[Dahlkamp06] Dahlkamp, H., A. Kaehler, D. Stavens, S. Thrun, and G. Bradski. “Self-supervised monocular road detection in desert terrain,” Robotics: Science and Systems, Philadelphia, 2006.

[Dalal05] Dalal, N., and B. Triggs. “Histograms of oriented gradients for human detection,” Computer Vision and Pattern Recognition (vol. 1, pp. 886–893), June 2005.

[Davis97] Davis, J., and A. Bobick. “The representation and recognition of action using temporal templates” (Technical Report 402), MIT Media Lab, Cambridge, MA, 1997.

[Davis99] Davis, J., and G. Bradski. “Real-time motion template gradients using Intel CVLib,” ICCV Workshop on Framerate Vision, 1999.

[Delaunay34] Delaunay, B. “Sur la sphère vide,” Izvestia Akademii Nauk SSSR, Otdelenie Matematicheskikh i Estestvennykh Nauk 7 (1934): 793–800.

[DeMenthon92] DeMenthon, D. F., and L. S. Davis. “Model-based object pose in 25 lines of code,”Proceedings of the European Conference on Computer Vision (pp. 335–343), 1992.

[Dempster77] Dempster, A., N. Laird, and D. Rubin. “Maximum likelihood from incomplete data via the EM algorithm,” Journal of the Royal Statistical Society, Series B 39 (1977): 1–38.

[Douglas73] Douglas, D., and T. Peucker. “Algorithms for the reduction of the number of points required for represent a digitized line or its caricature,” Canadian Cartographer 10(1973): 112–122.

[Drucker97] Drucker, Harris, Chris J.C. Burges, Linda Kaufman, Alex Smola, and Vladimir Vapnik. “Support vector regression machines.” Advances in neural information processing systems 9 (1997): 155-161.

[Duda72] Duda, R. O., and P. E. Hart. “Use of the Hough transformation to detect lines and curves in pictures,” Communications of the Association for Computing Machinery 15 (1972): 11–15.

[Duda73] Duda, R. O., and P. E. Hart. Pattern Recognition and Scene Analysis. New York: Wiley, 1973.

[Duda00] Duda, R. O., P. E. Hart, and D. G. Stork. Pattern Classification. New York: Wiley, 2001.

[Farin04] Farin, D., P. H. N. de With, and W. Effelsberg. “Video-object segmentation using multi-sprite background subtraction,” Proceedings of the IEEE International Conference on Multimedia and Expo, 2004.

[Farnebäck03] Farnebäck, Gunnar. “Two-frame motion estimation based on polynomial expansion.” Scandinavian conference on image analysis. Springer Berlin Heidelberg, 2003.

[Faugeras93] Faugeras, O. Three-Dimensional Computer Vision: A Geometric Viewpoint. Cambridge, MA: MIT Press, 1993.

[Felzenszwalb2004] Felzenszwalb, Pedro, and Daniel Huttenlocher. Distance transforms of sampled functions. Cornell University, 2004.

[Felzenszwalb2006] Felzenszwalb, Pedro F., and Daniel P. Huttenlocher. “Efficient belief propagation for early vision.” International Journal of Computer Vision, 70(1), October 2006.

[Felzenszwalb2010] Felzenszwalb, Pedro F., et al. “Object detection with discriminatively trained part-based models.” IEEE transactions on pattern analysis and machine intelligence 32.9 (2010): 1627–1645.

[Fischler81] Fischler, M. A., and R. C. Bolles. “Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography,” Communications of the Association for Computing Machinery 24 (1981): 381–395.

[Fitzgibbon95] Fitzgibbon, A. W., and R. B. Fisher. “A buyer’s guide to conic fitting,” Proceedings of the 5th British Machine Vision Conference (pp. 513–522), Birmingham, 1995.

[Fix51] Fix, E., and J. L. Hodges. “Discriminatory analysis, nonparametric discrimination: Consistency properties” (Technical Report 4), USAF School of Aviation Medicine, Randolph Field, Texas, 1951.

[Forsyth03] Forsyth, D., and J. Ponce. Computer Vision: A Modern Approach. Englewood Cliffs, NJ: Prentice-Hall, 2003.

[FourCC85] Morrison, J. “EA IFF 85 standard for interchange format files,” http://www.martinreddy.net/gfx/2d/IFF.txt.

[Fourier] “Joseph Fourier,” http://en.wikipedia.org/wiki/Joseph_Fourier.

[Freeman95] Freeman, W. T., and M. Roth. “Orientation histograms for hand gesture recognition,” International Workshop on Automatic Face and Gesture Recognition (pp. 296–301), June 1995.

[Freund97] Freund, Y., and R. E. Schapire. “A decision-theoretic generalization of on-line learning and an application to boosting,” Journal of Computer and System Sciences 55 (1997): 119–139.

[Fryer86] Fryer, J. G., and D. C. Brown. “Lens distortion for close-range photogrammetry,” Photogrammetric Engineering and Remote Sensing 52 (1986): 51–58.

[Fukunaga90] Fukunaga, K. Introduction to Statistical Pattern Recognition. Boston: Academic Press, 1990.

[Fukushima80] Fukushima, Kunihiko. “Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position.” Biological cybernetics 36.4 (1980): 193-202.

[Galton] “Francis Galton,” http://en.wikipedia.org/wiki/Francis_Galton.

[Gao03] Gao, Xiao-Shan, Xiao-Rong Hou, Jianliang Tang, and Hang-Fei Cheng. “Complete solution classification for the perspective-three-point problem,” IEEE Transactions Pattern Analysis and Machine Intelligence 25 (2003), 930–943.

[Garrido-Jurado] Garrido-Jurado, S., R. Munoz-Salinas, F. J. Madrid-Cuevas and M. J. Marin-Jimenez. “Automatic generation and detection of highly reliable fiducial markers under occlusion,” Pattern Recognition 47, no. 6 (June 2014).

[Göktürk01] Göktürk, S. B., J.-Y. Bouguet, and R. Grzeszczuk. “A data-driven model for monocular face tracking,” Proceedings of the IEEE International Conference on Computer Vision (vol. 2, pp. 701–708), 2001.

[Göktürk02] Göktürk, S. B., J.-Y. Bouguet, C. Tomasi, and B. Girod. “Model-based face tracking for view-independent facial expression recognition,” Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition (pp. 287–293), May 2002.

[Goresky03] Goresky, Mark, and Andrew Klapper. “Efficient multiply-with-carry random number generators with maximal period.” ACM Transactions on Modeling and Computer Simulation (TOMACS) 13.4 (2003): 310-321.

[Grossberg87] Grossberg, S., “Competitive learning: From interactive activation to adaptive resonance,” Cognitive Science 11 (1987): 23–63.

[Harris88] Harris, C., and M. Stephens. “A combined corner and edge detector,” Proceedings of the 4th Alvey Vision Conference (pp. 147–151), 1988.

[Hartley98] Hartley, R. I. “Theory and practice of projective rectification,” International Journal of Computer Vision 35 (1998): 115–127.

[Hartley06] Hartley, R., and A. Zisserman. Multiple View Geometry in Computer Vision. Cambridge, UK: Cambridge University Press, 2006.

[Hastie01] Hastie, T., R. Tibshirani, and J. Friedman. The Elements of Statistical Learning: Data Mining, Inference and Prediction. New York: Springer-Verlag, 2001.

[Heckbert90] Heckbert, P. A Seed Fill Algorithm (Graphics Gems I). New York: Academic Press, 1990.

[Heikkila97] Heikkila, J., and O. Silven. “A four-step camera calibration procedure with implicit image correction,” Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (p. 1106), 1997.

[Hinton06] Hinton, G. E., S. Osindero, and Y. Teh. “A fast learning algorithm for deep belief nets,” Neural Computation 18 (2006): 1527–1554.

[Hirschmuller 08] Hirschmuller, H. “Stereo Processing by Semiglobal Matching and Mutual Information,” Pattern Analysis and Machine Intelligence PAMI 30, No. 2, February 2008, pp. 328–341.

[Ho95] Ho, T. K. “Random decision forest,” Proceedings of the 3rd International Conference on Document Analysis and Recognition (pp. 278–282), August 1995.

[Horn81] Horn, B. K. P., and B. G. Schunck. “Determining optical flow,” Artificial Intelligence 17 (1981): 185–203.

[Hough59] Hough, P. V. C. “Machine analysis of bubble chamber pictures,” Proceedings of the International Conference on High Energy Accelerators and Instrumentation (pp. 554–556), 1959.

Huttenlocher, Daniel P., Gregory A. Klanderman, and William J. Rucklidge. “Comparing images using the Hausdorff distance.” IEEE Transactions on pattern analysis and machine intelligence 15.9 (1993): 850-863.

[Intel] Intel Corporation, http://www.intel.com/.

[Inui03] Inui, K., S. Kaneko, and S. Igarashi. “Robust line fitting using LmedS clustering,” Systems and Computers in Japan 34 (2003): 92–100.

[IPP] Intel Integrated Performance Primitives, https://software.intel.com/en-us/intel-ipp.

[Itseez] A computer vision company that grew out of the original OpenCV project and one of the key maintainers of the free and open OpenCV.org. Now sold to Intel Corporation.

[Jaehne95] Jaehne, B. Digital Image Processing, 3rd ed. Berlin: Springer-Verlag, 1995.

[Jaehne97] Jaehne, B. Practical Handbook on Image Processing for Scientific Applications. Boca Raton, FL: CRC Press, 1997.

[Jain77] Jain, A. “A fast Karhunen-Loeve transform for digital restoration of images degraded by white and colored noise,” IEEE Transactions on Computers 26 (1997): 560–571.

[Jain86] Jain, A. Fundamentals of Digital Image Processing. Englewood Cliffs, NJ: Prentice-Hall, 1986.

[Johnson84] Johnson, D. H. “Gauss and the history of the fast Fourier transform,” IEEE Acoustics, Speech, and Signal Processing Magazine 1 (1984): 14–21.

[KaewTraKulPong2001] KaewTraKulPong, P., and R. Bowden. “An Improved Adaptive Background Mixture Model for Realtime Tracking with Shadow Detection,” Proc. 2nd European Workshop on Advanced Video Based Surveillance Systems, AVBS01. Sept 2001.

[Kalman60] Kalman, R. E. “A new approach to linear filtering and prediction problems,” Journal of Basic Engineering 82 (1960): 35–45.

[Kim05] Kim, K., T. H. Chalidabhongse, D. Harwood, and L. Davis. “Real-time foreground-background segmentation using codebook model,” Real-Time Imaging 11 (2005): 167–256.

[Kimme75] Kimme, C., D. H. Ballard, and J. Sklansky. “Finding circles by an array of accumulators,” Communications of the Association for Computing Machinery 18 (1975): 120–122.

[Kiryati91] Kiryati, N., Y. Eldar, and A. M. Bruckshtein. “A probablistic Hough transform,” Pattern Recognition 24 (1991): 303–316.

[Koller09] Koller, Daphne, and Nir Friedman. Probabilistic graphical models: principles and techniques. Cambridge, MA: MIT Press, 2009.

[Konolige97] Konolige, K., “Small vision system: Hardware and implementation,” Proceedings of the International Symposium on Robotics Research (pp. 111–116), Hayama, Japan, 1997.

[Kopf07] Kopf, Johannes, et al. “Joint bilateral upsampling.” ACM Transactions on Graphics (TOG). Vol. 26. No. 3. ACM, 2007.

[Kreveld97] van Kreveld, M., R. van Oostrum, C. L. Bajaj, V. Pascucci, and D. R. Schikore. “Contour trees and small seed sets for isosurface traversal,” Proceedings of the 13th ACM Symposium on Computational Geometry (pp. 212–220), 1997.

[Lakoff08] Lakoff, G., and M. Johnson. “Metaphors we live by,” University of Chicago Press, 2008.

[Laughlin81] Laughlin, S. B. “A simple coding procedure enhances a neuron’s information capacity,” Zeitschrift für Naturforschung 9/10 (1981): 910–912.

[LeCun98a] LeCun, Y., L. Bottou, Y. Bengio, and P. Haffner. “Gradient-based learning applied to document recognition,” Proceedings of the IEEE 86 (1998): 2278–2324.

[LeCun98b] LeCun, Y., L. Bottou, G. Orr, and K. Muller. “Efficient BackProp,” in G. Orr and K. Muller (Eds.), Neural Networks: Tricks of the Trade. New York: Springer-Verlag, 1998.

[Leutenegger11] Leutenegger, Stefan, Margarita Chli, and Roland Y. Siegwart. “BRISK: Binary robust invariant scalable keypoints.” 2011 International conference on computer vision. IEEE, 2011.

[Lienhart02] Lienhart, Rainer, and Jochen Maydt. “An extended set of haar-like features for rapid object detection.” Proceedings 2002 International Conference on Image Processing. Vol. 1. IEEE, 2002.

[Liu07] Liu, Y. Z., H. X. Yao, W. Gao, X. L. Chen, and D. Zhao. “Nonparametric background generation,” Journal of Visual Communication and Image Representation 18 (2007): 253–263.

[Lloyd57] Lloyd, S. “Least square quantization in PCM’s” (Bell Telephone Laboratories Paper), 1957. [“Lloyd’s algorithm” was later published in IEEE Transactions on Information Theory 28 (1982): 129–137.]

[Lloyd82] Lloyd, Stuart. “Least squares quantization in PCM.” IEEE transactions on information theory 28.2 (1982): 129-137.

[Lowe04] Lowe, D. G. “Distinctive image features from scale-invariant keypoints,” International Journal of Computer Vision 60, no. 2 (2004): 91–110.

[LTI] LTI-Lib, Vision Library, http://ltilib.sourceforge.net/doc/homepage/index.shtml.

[Lucas81] Lucas, B. D., and T. Kanade. “An iterative image registration technique with an application to stereo vision,” Proceedings of the 1981 DARPA Imaging Understanding Workshop (pp. 121–130), 1981.

[Lucchese02] Lucchese, L., and S. K. Mitra. “Using saddle points for subpixel feature detection in camera calibration targets,” Proceedings of the 2002 Asia Pacific Conference on Circuits and Systems (pp. 191–195), December 2002.

[Lv07] Lv, Q., W. Josephson, Z. Wang, M. Charikar, and K. Li. “Multiprobe LSH: efficient indexing for high-dimensional similarity search.” In VLDB, pages 950–961, 2007.

[Mahalanobis36] Mahalanobis, P. “On the generalized distance in statistics,” Proceedings of the National Institute of Science 12 (1936): 49–55.

[Mair10] Mair, Elmar, et al. “Adaptive and generic corner detection based on the accelerated segment test.” European conference on Computer vision. Springer Berlin Heidelberg, 2010.

[Maron61] Maron, M. E. “Automatic indexing: An experimental inquiry,” Journal of the Association for Computing Machinery 8 (1961): 404–417.

[Marr82] Marr, D. Vision. San Francisco: Freeman, 1982.

[Marsaglia00] Marsaglia, George, and Wai Wan Tsang. “The ziggurat method for generating random variables.” Journal of statistical software 5.8 (2000): 1-7.

[Martins99] Martins, F. C. M., B. R. Nickerson, V. Bostrom, and R. Hazra. “Implementation of a real-time foreground/background segmentation system on the Intel architecture,” IEEE International Conference on Computer Vision Frame Rate Workshop, 1999.

[Matas00] Matas, J., C. Galambos, and J. Kittler. “Robust detection of lines using the progressive probabilistic Hough transform,” Computer Vision Image Understanding 78 (2000): 119–137.

[Meer91] Meer, P., D. Mintz, and A. Rosenfeld. “Robust regression methods for computer vision: A review,” International Journal of Computer Vision 6 (1991): 59–70.

[Merwe00] van der Merwe, R., A. Doucet, N. de Freitas, and E. Wan. “The unscented particle filter,” Advances in Neural Information Processing Systems, December 2000.

[Meyer78] Meyer, F. “Contrast feature extraction,” in J.-L. Chermant (Ed.), Quantitative Analysis of Microstructures in Material Sciences, Biology and Medicine [Special issue of Practical Metallography], Stuttgart: Riederer, 1978.

[Meyer92] Meyer, F. “Color image segmentation,” Proceedings of the International Conference on Image Processing and Its Applications (pp. 303–306), 1992.

[Minsky61] Minsky, M. “Steps toward artificial intelligence,” Proceedings of the Institute of Radio Engineers 49 (1961): 8–30.

[Moreno-Noguer07] Moreno-Noguer, F., Lepetit, V., Fua, P. “Accurate Non-Iterative O(n) Solution to the PnP Problem,” ICCV 2007. IEEE 11th International Conference on Computer Vision, pp. 1–8, 2007.

[Morse53] Morse, P. M., and H. Feshbach. “Fourier transforms,” in Methods of Theoretical Physics (Part I, pp. 453–471), New York: McGraw-Hill, 1953.

[Muja09] Muja, Marius, and David G. Lowe. “Fast Approximate Nearest Neighbors with Automatic Algorithm Configuration.” VISAPP (1) 2.331–340 (2009): 2.

[Neapolitan04] Neapolitan, Richard E. Learning Bayesian Networks. Upper Saddle River, New Jersey: Pearson, 2004.

[O’Connor02] O’Connor, J. J., and E. F. Robertson. “Light through the ages: Ancient Greece to Maxwell,” http://www-groups.dcs.st-and.ac.uk/~history/HistTopics/Light_1.html.

[Ojala94] Ojala, T., M. Pietikäinen, and D. Harwood. “Performance evaluation of texture measures with classification based on Kullback discrimination of distributions,” Pattern Recognition, 1994. Vol. 1 [Oliva06] A. Oliva and A. Torralba, “Building the gist of a scene: The role of global image features in recognition visual perception,” Progress in Brain Research 155 (2006): 23–36.

[OpenCV] Open Source Computer Vision Library (OpenCV) (free, BSD license), http://opencv.org.

[opencv_contrib] Newer content and higher functionality is separated into this repository. https://github.com/opencv/opencv_contrib.

[Papoulis62] Papoulis, A. The Fourier Integral and Its Applications. New York: McGraw-Hill, 1962.

[Pascucci02] Pascucci, V., and K. Cole-McLaughlin. “Efficient computation of the topology of level sets,” Proceedings of IEEE Visualization 2002 (pp. 187–194), 2002.

[Pearson] “Karl Pearson,” http://en.wikipedia.org/wiki/Karl_Pearson.

[Pollefeys99a] Pollefeys, M. “Self-calibration and metric 3D reconstruction from uncalibrated image sequences,” Ph.D. thesis, Katholieke Universiteit, Leuven, 1999.

[Pollefeys99b] Pollefeys, M., R. Koch, and L. V. Gool. “A simple and efficient rectification method for general motion,” Proceedings of the 7th IEEE Conference on Computer Vision, 1999.

[Porter84] Porter, T., and T. Duff. “Compositing digital images,” Computer Graphics 18 (1984): 253–259.

[Ranger07] Ranger, C., R. Raghuraman, A. Penmetsa, G. Bradski, and C. Kozyrakis. “Evaluating mapreduce for multi-core and multiprocessor systems,” Proceedings of the 13th International Symposium on High-Performance Computer Architecture (pp. 13–24), 2007.

[Reeb46] Reeb, G. “Sur les points singuliers d’une forme de Pfaff completement integrable ou d’une fonction numerique,” Comptes Rendus de l’Academie des Sciences de Paris 222 (1946): 847–849.

[Riedmiller93] Riedmiller, Martin, and Heinrich Braun. “A direct adaptive method for faster backpropagation learning: The RPROP algorithm.” 1993 IEEE International Conference on Neural Networks.. IEEE, 1993.

[Rodgers88] Rodgers, J. L., and W. A. Nicewander. “Thirteen ways to look at the correlation coefficient,” American Statistician 42 (1988): 59–66.

[Rosenfeld73] Rosenfeld, A., and E. Johnston. “Angle detection on digital curves,” IEEE Transactions on Computers 22 (1973): 875–878.

[Rosenfeld80] Rosenfeld, A. “Some Uses of Pyramids in Image Processing and Segmentation,” Proceedings of the DARPA Imaging Understanding Workshop (pp. 112–120), 1980.

[Rosten06] Rosten, Edward. “FAST corner detection.” Engineering Department, Machine Intelligence Laboratory, University of Cambridge, 2006.

[Rother04] Rother, Carsten, Vladimir Kolmogorov, and Andrew Blake. “Grabcut: Interactive foreground extraction using iterated graph cuts.” ACM transactions on graphics (TOG). Vol. 23. No. 3. ACM, 2004.

[Rousseeuw84] Rousseeuw, P. J. “Least median of squares regression,” Journal of the American Statistical Association, 79 (1984): 871–880.

[Rousseeuw87] Rousseeuw, P. J., and A. M. Leroy. Robust Regression and Outlier Detection. New York: Wiley, 1987.

[Rublee11] Rublee, E., V. Rabaud, K. Konolige, and G. Bradski. “ORB an efficient alternative to SIFT or SURF.” Proceedings of the 2011 IEEE International Conference on Computer Vision, 2011.

[Rubner00] Rubner, Y., C. Tomasi, and L. J. Guibas. “The earth mover’s distance as a metric for image retrieval,” International Journal of Computer Vision 40 (2000): 99–121.

[Rumelhart88] Rumelhart, D. E., G. E. Hinton, and R. J. Williams. “Learning internal representations by error propagation,” in D. E. Rumelhart, J. L. McClelland, and PDP Research Group (Eds.), Parallel Distributed Processing. Explorations in the Microstructures of Cognition (vol. 1, pp. 318–362), Cambridge, MA: MIT Press, 1988.

[Russ02] Russ, J. C. The Image Processing Handbook, 4th ed. Boca Raton, FL: CRC Press, 2002.

[Sánchez13] Sánchez, Javier, Enric Meinhardt-Llopis, and Gabriele Facciolo. “TV-L1 optical flow estimation.” Image Processing On Line 2013 (2013): 137-150.

[Scharr00] Scharr, Hanno. “Optimal operators in digital image processing.” Diss. 2000.

[Schiele96] Schiele, B., and J. L. Crowley. “Object recognition using multidimensional receptive field histograms,” European Conference on Computer Vision (vol. I, pp. 610–619), April 1996.

[Schmidt66] Schmidt, S. “Applications of state-space methods to navigation problems,” in C. Leondes (Ed.), Advances in Control Systems (vol. 3, pp. 293–340), New York: Academic Press, 1966.

[Schölkopf00] Schölkopf, Bernhard, et al. “New support vector algorithms.” Neural computation 12.5 (2000): 1207-1245.

[Schwartz80] Schwartz, E. L. “Computational anatomy and functional architecture of the striate cortex: A spatial mapping approach to perceptual coding,” Vision Research 20 (1980): 645–669.

[Schwarz78] Schwarz, A. A., and J. M. Soha. “Multidimensional histogram normalization contrast enhancement,” Proceedings of the Canadian Symposium on Remote Sensing (pp. 86–93), 1978.

[Semple79] Semple, J., and G. Kneebone. Algebraic Projective Geometry. Oxford, UK: Oxford University Press, 1979.

[Serra83] Serra, J. Image Analysis and Mathematical Morphology. New York: Academic Press, 1983.

[Sezgin04] Sezgin, M., and B. Sankur. “Survey over image thresholding techniques and quantitative performance evaluation,” Journal of Electronic Imaging 13 (2004): 146–165.

[Shannon49] Shannon, C. E.. “Communication in the presence of noise,” Proc. Institute of Radio Engineers, vol. 37, no. 1, pp. 10–21, Jan. 1949. Reprint as classic paper in: Proc. IEEE, vol. 86, no. 2, (Feb. 1998), http://www.stanford.edu/class/ee104/shannonpaper.pdf.

[Shapiro02] Shapiro, L. G., and G. C. Stockman. Computer Vision. Englewood Cliffs, NJ: Prentice-Hall, 2002.

[Shaw04] Shaw, J. R. “QuickFill: An efficient flood fill algorithm,” http://www.codeproject.com/gdi/QuickFill.asp.

[Shi94] Shi, J., and C. Tomasi. “Good features to track,” 9th IEEE Conference on Computer Vision and Pattern Recognition, June 1994.

[Smith79] Smith, A. R. “Painting tutorial notes,” Computer Graphics Laboratory, New York Institute of Technology, Islip, NY, 1979.

[Sobel73] Sobel, I., and G. Feldman. “A 3 x 3 Isotropic Gradient Operator for Image Processing,” in R. Duda and P. Hart (Eds.), Pattern Classification and Scene Analysis (pp. 271–272), New York: Wiley, 1973.

[Sochman05] Sochman, J., and J. Matas. “WaldBoost - learning for time constrained sequential detection,” in Computer Vision and Pattern Recognition, 2005. CVPR 2005.

[Steinhaus56] Steinhaus, H. “Sur la division des corp materiels en parties,” Bulletin of the Polish Academy of Sciences and Mathematics 4 (1956): 801–804.

[Sturm99] Sturm, P. F., and S. J. Maybank. “On plane-based camera calibration: A general algorithm, singularities, applications,” IEEE Conference on Computer Vision and Pattern Recognition, 1999.

[Suzuki85] Suzuki, S., and K. Abe, “Topological structural analysis of digital binary images by border following,” Computer Vision, Graphics and Image Processing 30 (1985): 32–46.

[Swain91] Swain, M. J., and D. H. Ballard. “Color indexing,” International Journal of Computer Vision 7 (1991): 11–32.

[Szeliski04] Szeliski, R. “Image Alignment and Stitching: A Tutorial,” http://research.microsoft.com/apps/pubs/default.aspx?id=70092. October 2004.

[Tao12] Tao, Michael, et al. “SimpleFlow: A Non-iterative, Sublinear Optical Flow Algorithm.” Computer Graphics Forum. Vol. 31. No. 2pt1. Blackwell Publishing Ltd, 2012.

[Teh89] Teh, C. H., and R. T. Chin. “On the detection of dominant points on digital curves,” IEEE Transactions on Pattern Analysis and Machine Intelligence 11 (1989): 859–872.

[Telea04] Telea, A. “An image inpainting technique based on the fast marching method,” Journal of Graphics Tools 9 (2004): 25–36.

[Thrun05] Thrun, S., W. Burgard, and D. Fox. Probabilistic Robotics: Intelligent Robotics and Autonomous Agents, Cambridge, MA: MIT Press, 2005.

[Thrun06] Thrun, S., M. Montemerlo, H. Dahlkamp, D. Stavens, A. Aron, J. Diebel, P. Fong, J. Gale, M. Halpenny, G. Hoffmann, K. Lau, C. Oakley, M. Palatucci, V. Pratt, P. Stang, S. Strohband, C. Dupont, L.-E. Jendrossek, C. Koelen, C. Markey, C. Rummel, J. van Niekerk, E. Jensen, P. Alessandrini, G. Bradski, B. Davies, S. Ettinger, A. Kaehler, A. Nefian, and P. Mahoney. “Stanley, the robot that won the DARPA Grand Challenge,” Journal of Robotic Systems 23 (2006): 661–692.

[Titchmarsh26] Titchmarsh, E. C. “The zeros of certain integral functions,” Proceedings of the London Mathematical Society 25 (1926): 283–302.

[Tomasi98] Tomasi, C., and R. Manduchi. “Bilateral filtering for gray and color images,” Sixth International Conference on Computer Vision (pp. 839–846), New Delhi, 1998.

[Tou77] Tou, J., and R. Gonzales. Pattern Recognition Principles. Addison Wesley Publishing (1977), p. 377.

[Toyama99] Toyama, K., J. Krumm, B. Brumitt, and B. Meyers. “Wallflower: Principles and practice of background maintenance,” Proceedings of the 7th IEEE International Conference on Computer Vision (pp. 255–261), 1999.

[Trucco98] Trucco, E., and A. Verri. Introductory Techniques for 3-D Computer Vision. Englewood Cliffs, NJ: Prentice-Hall, 1998.

[Tsai87] Tsai, R. Y. “A versatile camera calibration technique for high accuracy 3D machine vision metrology using off-the-shelf TV cameras and lenses,” IEEE Journal of Robotics and Automation 3 (1987): 323–344.

[Vandevenne04] Vandevenne, Lode. “Lode’s computer graphics tutorial, flood fill.” 2004.

[Vapnik95] Vapnik, V. The Nature of Statistical Learning Theory. New York: Springer-Verlag, 1995.

[Viola01] Viola, Paul, and Michael Jones. “Rapid object detection using a boosted cascade of simple features.” Computer Vision and Pattern Recognition, 2001. CVPR 2001. Proceedings of the 2001 IEEE Computer Society Conference on. Vol. 1. IEEE, 2001.

[Viola04] Viola, P., and M. J. Jones. “Robust real-time face detection,” International Journal of Computer Vision 57 (2004): 137–154.

[Welsh95] Welsh, G., and G. Bishop. “An introduction to the Kalman filter” (Technical Report TR95-041), University of North Carolina, Chapel Hill, NC, 1995.

[Wharton71] Wharton, W., and D. Howorth. Principles of Television Reception. London: Pitman, 1971.

[Wu08] Wu, K., O. Ekow and K. Suzuki. Two Strategies to Speed up Connected Component Labeling Algorithms, http://escholarship.org/uc/item/5pc9s496, 06-02-2008.

[Xu96] Xu, G., and Z. Zhang. Epipolar Geometry in Stereo, Motion and Object Recognition. Dordrecht: Kluwer, 1996.

[Zach07] Zach, Christopher, Thomas Pock, and Horst Bischof. “A duality based approach for realtime TV-L 1 optical flow.” Joint Pattern Recognition Symposium. Springer Berlin Heidelberg, 2007.

[Zhang96] Zhang, Z. “Parameter estimation techniques: A tutorial with application to conic fitting,” Image and Vision Computing 15 (1996): 59–76.

[Zhang99] Zhang, R., P.-S. Tsi, J. E. Cryer, and M. Shah. “Shape form shading: A survey,” IEEE Transactions on Pattern Analysis and Machine Intelligence 21 (1999): 690–706.

[Zhang99] Zhang, Z. “Flexible camera calibration by viewing a plane from unknown orientations,” Proceedings of the 7th International Conference on Computer Vision (pp. 666–673), Corfu, September 1999.

[Zhang00] Zhang, Z. “A flexible new technique for camera calibration,” IEEE Transactions on Pattern Analysis and Machine Intelligence 22 (2000): 1330–1334.

[Zhang04] Zhang, H. “The optimality of naive Bayes,” Proceedings of the 17th International FLAIRS Conference, 2004.

[Zivkovic04] Zivkovic, Z. “Improved adaptive Gaussian mixture model for background subtraction,” International Conference Pattern Recognition, UK, August 2004.

[Zivkovic06] Zivkovic, Z., and F. van der Heijden. “Efficient Adaptive Density Estimation per Image Pixel for the Task of Background Subtraction,” Pattern Recognition Letters, vol. 27, no. 7, pages 773–780, 2006.