Optical_Flow.bib

%% Phase correlation
@ARTICLE{988953,
	author={H. Foroosh and J. B. Zerubia and M. Berthod},
	journal={IEEE Transactions on Image Processing},
	title={Extension of phase correlation to subpixel registration},
	year={2002},
	volume={11},
	number={3},
	pages={188-200},
	keywords={correlation methods;error analysis;image registration;image sampling;image sequences;analytic expressions;closed-form solution;coherent peaks;downsampled images;error analysis;filtered unit impulse;image sequence;phase correlation;polyphase transform;signal power;subpixel registration;subpixel translation estimation;Apertures;Biomedical imaging;Closed-form solution;Error analysis;Image analysis;Image sequences;Interpolation;Noise level;Pixel;Remote sensing},
	doi={10.1109/83.988953},
	ISSN={1057-7149},
	month={Mar},}

@ARTICLE{957286,
	author={H. S. Stone and M. T. Orchard and Ee-Chien Chang and S. A. Martucci},
	journal={IEEE Transactions on Geoscience and Remote Sensing},
	title={A fast direct Fourier-based algorithm for subpixel registration of images},
	year={2001},
	volume={39},
	number={10},
	pages={2235-2243},
	keywords={geophysical signal processing;geophysical techniques;image registration;interpolation;remote sensing;terrain mapping;algorithm;aliasing;fast direct Fourier based algorithm;geophysical measurement technique;image processing;image registration;image-to-image registration;land surface;remote sensing;spline interpolation;subpixel registration;terrain mapping;Fourier transforms;Fuses;Image registration;Interpolation;Iterative algorithms;Iterative methods;Lighting;National electric code;Pixel;Spline},
	doi={10.1109/36.957286},
	ISSN={0196-2892},
	month={Oct},}

@ARTICLE{4767966,
	author={E. De Castro and C. Morandi},
	journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
	title={Registration of Translated and Rotated Images Using Finite Fourier Transforms},
	year={1987},
	volume={PAMI-9},
	number={5},
	pages={700-703},
	keywords={Cameras;Correlation;Density functional theory;Fourier transforms;Humans;Image registration;Lighting;Noise robustness;Retina;TV;Digital image processing;fast Fourier transform;image registration;image sequence analysis;motion estimation},
	doi={10.1109/TPAMI.1987.4767966},
	ISSN={0162-8828},
	month={Sept},}

	% Block matching
@ARTICLE{1176932,
	author={Yao Nie and Kai-Kuang Ma},
	journal={IEEE Transactions on Image Processing},
	title={Adaptive rood pattern search for fast block-matching motion estimation},
	year={2002},
	volume={11},
	number={12},
	pages={1442-1449},
	keywords={adaptive signal processing;data compression;image matching;motion estimation;search problems;video coding;ARPS-ZMP;MPEG-4 Verification Model encoding platform;PSNR;adaptive rood pattern search;diamond search;fast block-matching algorithm;fast block-matching motion estimation;initial search;macroblock;motion content;motion vectors;peak signal-to-noise ratio;refined local search;sequential search;unit-size rood pattern;video coding;video sequences;zero-motion prejudgment;Computer errors;Encoding;MPEG 4 Standard;Motion estimation;PSNR;Transform coding;Video coding;Video compression;Video sequences;Virtual manufacturing},
	doi={10.1109/TIP.2002.806251},
	ISSN={1057-7149},
	month={Dec},}

@ARTICLE{564122,
	author={Jianhua Lu and M. L. Liou},
	journal={IEEE Transactions on Circuits and Systems for Video Technology},
	title={A simple and efficient search algorithm for block-matching motion estimation},
	year={1997},
	volume={7},
	number={2},
	pages={429-433},
	keywords={computational complexity;data compression;image matching;motion estimation;search problems;video coding;block-matching motion estimation;computational complexity;computational reduction;performance;real-time video applications;regularity;search algorithm;three-step search algorithm;video compression;Apertures;Codecs;Computational complexity;Image sequence analysis;Motion compensation;Motion estimation;Samarium;Signal processing algorithms;Video compression;Video signal processing},
	doi={10.1109/76.564122},
	ISSN={1051-8215},
	month={Apr},}

@inproceedings{kaiming_he_computing_2012,
	title = {Computing nearest-neighbor fields via {Propagation}-{Assisted} {KD}-{Trees}},
	isbn = {978-1-4673-1228-8 978-1-4673-1226-4 978-1-4673-1227-1},
	url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6247665},
	doi = {10.1109/CVPR.2012.6247665},
	urldate = {2016-06-13},
	publisher = {IEEE},
	author = {{Kaiming He} and {Jian Sun}},
	month = jun,
	year = {2012},
	pages = {111--118}
}

@INPROCEEDINGS{6619086,
	author={J. Lu and H. Yang and D. Min and M. N. Do},
	booktitle={2013 IEEE Conference on Computer Vision and Pattern Recognition},
	title={Patch Match Filter: Efficient Edge-Aware Filtering Meets Randomized Search for Fast Correspondence Field Estimation},
	year={2013},
	pages={1854-1861},
	keywords={computational complexity;computer vision;edge detection;estimation theory;filtering theory;image matching;image retrieval;image sequences;query formulation;computational complexity;computer vision;edge-aware image filtering;fast correspondence field estimation;optical flow estimation;patch match filter;pixel-labeling problems;pixel-wise randomized search;Complexity theory;Estimation;Image segmentation;Labeling;Optical filters;Optical imaging;Search problems;PatchMatch;cost volume filtering;edge-aware image filtering;nearest-neighbor search;optical flow;stereo},
	doi={10.1109/CVPR.2013.242},
	ISSN={1063-6919},
	month={June},}

@inproceedings{nirmal_jith_o._u._optical_2014,
	title = {Optical flow based on {Approximate} {Nearest} {Neighbor} {Field}},
	isbn = {978-1-4799-2317-5 978-1-4799-2318-2},
	url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6740292},
	doi = {10.1109/CONECCT.2014.6740292},
	urldate = {2016-05-04},
	publisher = {IEEE},
	author = {{Nirmal Jith O. U.} and Ramakanth, S. Avinash and Babu, R. Venkatesh},
	month = jan,
	year = {2014},
	pages = {1--5}
}

@article{bai_edge_2015,
	title = {Edge {Propagation} {KD}-{Trees}: {Computing} {Approximate} {Nearest} {Neighbor} {Fields}},
	volume = {22},
	issn = {1070-9908, 1558-2361},
	shorttitle = {Edge {Propagation} {KD}-{Trees}},
	url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=7208826},
	doi = {10.1109/LSP.2015.2469738},
	number = {12},
	urldate = {2016-05-04},
	journal = {IEEE Signal Processing Letters},
	author = {Bai, Xiuxiu and Dong, Xiaoshe and Su, Yuanqi},
	month = dec,
	year = {2015},
	pages = {2209--2213}
}

@article{bailer_optical_2017,
	title = {Optical {Flow} {Fields}: {Dense} {Correspondence} {Fields} for {Highly} {Accurate} {Large} {Displacement} {Optical} {Flow} {Estimation}},
	shorttitle = {Optical {Flow} {Fields}},
	url = {http://arxiv.org/abs/1703.02563},
	abstract = {Modern large displacement optical flow algorithms usually use an initialization by either sparse descriptor matching techniques or dense approximate nearest neighbor fields. While the latter have the advantage of being dense, they have the major disadvantage of being very outlier-prone as they are not designed to find the optical flow, but the visually most similar correspondence. In this article we present a dense correspondence field approach that is much less outlier-prone and thus much better suited for optical flow estimation than approximate nearest neighbor fields. Our approach does not require explicit regularization, smoothing (like median filtering) or a new data term. Instead we solely rely on patch matching techniques and a novel multi-scale matching strategy. We also present enhancements for outlier filtering. We show that our approach is better suited for large displacement optical flow estimation than modern descriptor matching techniques. We do so by initializing EpicFlow with our approach instead of their originally used state-of-the-art descriptor matching technique. We significantly outperform the original EpicFlow on MPI-Sintel, KITTI 2012, KITTI 2015 and Middlebury. In this extended article of our former conference publication we further improve our approach in matching accuracy as well as runtime and present more experiments and insights.},
	urldate = {2017-07-17},
	journal = {arXiv:1703.02563 [cs]},
	author = {Bailer, Christian and Taetz, Bertram and Stricker, Didier},
	month = mar,
	year = {2017},
	note = {arXiv: 1703.02563},
	keywords = {Computer Science - Computer Vision and Pattern Recognition, I.4.8},
	annote = {Comment: extended version of conference publication arXiv:1508.05151}
}

%% Local method

@inproceedings{lucas_iterative_1981,
	title = {An {Iterative} {Image} {Registration} {Technique} with an {Application} to {Stereo} {Vision}},
	abstract = {Image registration finds a variety of applications in computer vision. Unfortunately, traditional image registration techniques tend to be costly. We present a new image registration technique that makes use of the spatial intensity gradient of the images to find a good match using a type of Newton-Raphson iteration. Our technique is faster because it examines far fewer potential matches between the images than existing techniques. Furthermore, this registration technique can be generalized to handle rotation, scaling and shearing. We show show our technique can be adapted for use in a stereo vision system. 1. Introduction  Image registration finds a variety of applications in computer vision, such as image matching for stereo vision, pattern recognition, and motion analysis. Untortunately, existing techniques for image registration tend to be costly. Moreover, they generally fail to deal with rotation or other distortions of the images. In this paper we present a new image registratio...},
	author = {Lucas, Bruce D. and Kanade, Takeo},
	year = {1981},
	pages = {674--679},
	file = {Citeseer - Full Text PDF:C\:\\Users\\MAI\\AppData\\Roaming\\Zotero\\Zotero\\Profiles\\8h4pxrbr.default\\zotero\\storage\\E2XQHJ5J\\Lucas and Kanade - 1981 - An Iterative Image Registration Technique with an .pdf:application/pdf;Citeseer - Snapshot:C\:\\Users\\MAI\\AppData\\Roaming\\Zotero\\Zotero\\Profiles\\8h4pxrbr.default\\zotero\\storage\\MNCM9PHP\\summary.html:text/html}
}

@article{bouguet_pyramidal_2000,
	title = {Pyramidal implementation of the {Lucas} {Kanade} feature tracker},
	abstract = {grayscale value of the two images are the location x = [x y] T, where x and y are the two pixel coordinates of a generic image point x. The image I will sometimes be referenced as the first image, and the image J as the second image. For practical issues, the images I and J are discret function (or arrays), and the upper left corner pixel coordinate vector is [0 0] T. Let nx and ny be the width and height of the two images. Then the lower right pixel coordinate vector is [nx − 1 ny − 1] T. Consider an image point u = [ux uy] T on the first image I. The goal of feature tracking is to find the location v = u + d = [ux + dx uy + dy] T on the second image J such as I(u) and J(v) are “similar”. The vector d = [dx dy] T is the image velocity at x, also known as the optical flow at x. Because of the aperture problem, it is essential to define the notion of similarity in a 2D neighborhood sense. Let ωx and ωy two integers. We define the image velocity d as being the vector that minimizes the residual function ɛ defined as follows: ɛ(d)  = ɛ(dx, dy) = ux+ωx ∑ uy+ωy x=ux−ωx y=uy−ωy (I(x, y)  − J(x + dx, y + dy)) 2. (1)},
	journal = {Intel Corporation, Microprocessor Research Labs},
	author = {Bouguet, Jean-yves},
	year = {2000},
	file = {Citeseer - Full Text PDF:C\:\\Users\\MAI\\AppData\\Roaming\\Zotero\\Zotero\\Profiles\\8h4pxrbr.default\\zotero\\storage\\9KW7QNPE\\Bouguet - 2000 - Pyramidal implementation of the Lucas Kanade featu.pdf:application/pdf;Citeseer - Snapshot:C\:\\Users\\MAI\\AppData\\Roaming\\Zotero\\Zotero\\Profiles\\8h4pxrbr.default\\zotero\\storage\\SCEMGGIA\\summary.html:text/html}
}

@inproceedings{black_robust_1991,
	title = {Robust dynamic motion estimation over time},
	doi = {10.1109/CVPR.1991.139705},
	abstract = {A novel approach to incrementally estimating visual motion over a sequence of images is presented. The authors start by formulating constraints on image motion to account for the possibility of multiple motions. This is achieved by exploiting the notions of weak continuity and robust statistics in the formulation of a minimization problem. The resulting objective function is non-convex. Traditional stochastic relaxation techniques for minimizing such functions prove inappropriate for the task. A highly parallel incremental stochastic minimization algorithm is presented which has a number of advantages over previous approaches. The incremental nature of the scheme makes it dynamic and permits the detection of occlusion and disocclusion boundaries},
	booktitle = {, {IEEE} {Computer} {Society} {Conference} on {Computer} {Vision} and {Pattern} {Recognition}, 1991. {Proceedings} {CVPR} '91},
	author = {Black, M. J. and Anandan, P.},
	month = jun,
	year = {1991},
	keywords = {Computed tomography, computerised picture processing, Computer science, Computer vision, Gaussian noise, highly parallel incremental stochastic minimization algorithm, Identity-based encryption, image motion, minimisation, motion estimation, multiple motions, robust dynamic motion estimation, Robustness, robust statistics, sequence of images, Simulated annealing, spatial coherence, Statistics, stochastic processes, stochastic relaxation, visual motion, weak continuity},
	pages = {296--302},
	file = {IEEE Xplore Abstract Record:C\:\\Users\\MAI\\AppData\\Roaming\\Zotero\\Zotero\\Profiles\\8h4pxrbr.default\\zotero\\storage\\3NUD37PF\\abs_all.html:text/html;IEEE Xplore Full Text PDF:C\:\\Users\\MAI\\AppData\\Roaming\\Zotero\\Zotero\\Profiles\\8h4pxrbr.default\\zotero\\storage\\TKCNPX93\\Black and Anandan - 1991 - Robust dynamic motion estimation over time.pdf:application/pdf}
}

@article{bab-hadiashar_robust_1998,
	title = {Robust {Optic} {Flow} {Computation}},
	volume = {29},
	issn = {0920-5691, 1573-1405},
	url = {http://link.springer.com/article/10.1023/A%3A1008090730467},
	doi = {10.1023/A:1008090730467},
	abstract = {This paper formulates the optic flow problem as a set of over-determined simultaneous linear equations. It then introduces and studies two new robust optic flow methods. The first technique is based on using the Least Median of Squares (LMedS) to detect the outliers. Then, the inlier group is solved using the least square technique. The second method employs a new robust statistical method named the Least Median of Squares Orthogonal Distances (LMSOD) to identify the outliers and then uses total least squares to solve the optic flow problem. The performance of both methods are studied by experiments on synthetic and real image sequences. These methods outperform other published methods both in accuracy and robustness.},
	language = {en},
	number = {1},
	urldate = {2016-07-11},
	journal = {International Journal of Computer Vision},
	author = {Bab-Hadiashar, Alireza and Suter, David},
	month = aug,r
	year = {1998},
	keywords = {Artificial Intelligence (incl. Robotics), Automation and Robotics, Computer Imaging, Graphics and Computer Vision, Image Processing},
	pages = {59--77},
	file = {Snapshot:C\:\\Users\\MAI\\AppData\\Roaming\\Zotero\\Zotero\\Profiles\\8h4pxrbr.default\\zotero\\storage\\PUSGIU52\\A1008090730467.html:text/html}
}

@article{fleet_design_2000,
	title = {Design and {Use} of {Linear} {Models} for {Image} {Motion} {Analysis}},
	volume = {36},
	issn = {0920-5691, 1573-1405},
	url = {https://link.springer.com/article/10.1023/A:1008156202475},
	doi = {10.1023/A:1008156202475},
	abstract = {Linear parameterized models of optical flow, particularly affine models, have become widespread in image motion analysis. The linear model coefficients are straightforward to estimate, and they provide reliable estimates of the optical flow of smooth surfaces. Here we explore the use of parameterized motion models that represent much more varied and complex motions. Our goals are threefold: to construct linear bases for complex motion phenomena; to estimate the coefficients of these linear models; and to recognize or classify image motions from the estimated coefficients. We consider two broad classes of motions: i) generic “motion features” such as motion discontinuities and moving bars; and ii) non-rigid, object-specific, motions such as the motion of human mouths. For motion features we construct a basis of steerable flow fields that approximate the motion features. For object-specific motions we construct basis flow fields from example motions using principal component analysis. In both cases, the model coefficients can be estimated directly from spatiotemporal image derivatives with a robust, multi-resolution scheme. Finally, we show how these model coefficients can be use to detect and recognize specific motions such as occlusion boundaries and facial expressions.},
	language = {en},
	number = {3},
	urldate = {2017-05-12},
	journal = {International Journal of Computer Vision},
	author = {Fleet, David J. and Black, Michael J. and Yacoob, Yaser and Jepson, Allan D.},
	month = feb,
	year = {2000},
	pages = {171--193},
	file = {Snapshot:C\:\\Users\\MAI\\AppData\\Roaming\\Zotero\\Zotero\\Profiles\\8h4pxrbr.default\\zotero\\storage\\RJC3IS2B\\A1008156202475.html:text/html}
}

@inproceedings{farneback_fast_2000,
	title = {Fast and accurate motion estimation using orientation tensors and parametric motion models},
	volume = {1},
	isbn = {978-0-7695-0750-7},
	url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=905291},
	doi = {10.1109/ICPR.2000.905291},
	urldate = {2016-07-11},
	publisher = {IEEE Comput. Soc},
	author = {Farneback, G.},
	year = {2000},
	pages = {135--139}
}

@Inbook{Farnebäck2003,
	author="Farneb{\"a}ck, Gunnar",
	editor="Bigun, Josef
	and Gustavsson, Tomas",
	title="Two-Frame Motion Estimation Based on Polynomial Expansion",
	bookTitle="Image Analysis: 13th Scandinavian Conference, SCIA 2003 Halmstad, Sweden, June 29 -- July 2, 2003 Proceedings",
	year="2003",
	publisher="Springer Berlin Heidelberg",
	address="Berlin, Heidelberg",
	pages="363--370",
	isbn="978-3-540-45103-7",
	doi="10.1007/3-540-45103-X_50",
	url="http://dx.doi.org/10.1007/3-540-45103-X_50"
}

@article{black_robust_1996-1,
	title = {The {Robust} {Estimation} of {Multiple} {Motions}: {Parametric} and {Piecewise}-{Smooth} {Flow} {Fields}},
	volume = {63},
	issn = {1077-3142},
	shorttitle = {The {Robust} {Estimation} of {Multiple} {Motions}},
	url = {http://www.sciencedirect.com/science/article/pii/S1077314296900065},
	doi = {10.1006/cviu.1996.0006},
	abstract = {Most approaches for estimating optical flow assume that, within a finite image region, only a single motion is present. Thissingle motion assumptionis violated in common situations involving transparency, depth discontinuities, independently moving objects, shadows, and specular reflections. To robustly estimate optical flow, the single motion assumption must be relaxed. This paper presents a framework based onrobust estimationthat addresses violations of the brightness constancy and spatial smoothness assumptions caused by multiple motions. We show how therobust estimation frameworkcan be applied to standard formulations of the optical flow problem thus reducing their sensitivity to violations of their underlying assumptions. The approach has been applied to three standard techniques for recovering optical flow: area-based regression, correlation, and regularization with motion discontinuities. This paper focuses on the recovery of multiple parametric motion models within a region, as well as the recovery of piecewise-smooth flow fields, and provides examples with natural and synthetic image sequences.},
	number = {1},
	urldate = {2016-07-11},
	journal = {Computer Vision and Image Understanding},
	author = {Black, Michael J. and Anandan, P.},
	month = jan,
	year = {1996},
	pages = {75--104},
	file = {ScienceDirect Snapshot:C\:\\Users\\MAI\\AppData\\Roaming\\Zotero\\Zotero\\Profiles\\8h4pxrbr.default\\zotero\\storage\\ISBKUMNW\\S1077314296900065.html:text/html}
}

@incollection{nagel_spatiotemporally_1998,
	series = {Lecture {Notes} in {Computer} {Science}},
	title = {Spatiotemporally adaptive estimation and segmentation of {OF}-fields},
	copyright = {©1998 Springer-Verlag},
	isbn = {978-3-540-64613-6 978-3-540-69235-5},
	url = {http://link.springer.com/chapter/10.1007/BFb0054735},
	abstract = {A grayvalue structure tensor provides knowledge about a local grayvalue variation. This knowledge can be used to devise a spatiotemporally adaptive optic flow estimation process. Such an adaptive estimation lowers the level at which the resulting optic flow (OF) field is disturbed by noise and estimation artefacts. This in turn substantially simplifies the analysis of remaining — often subtle — effects which easily jeopardize a ‘naive’ segmentation approach. Appropriate treatment of such effects eventually results in a basically simple, but nevertheless surprisingly robust segmentation approach. Various stages of this approach are illustrated by examples for the extraction of moving vehicle images from a digitized road intersection video-sequence.},
	language = {en},
	number = {1407},
	urldate = {2016-07-11},
	booktitle = {Computer {Vision} — {ECCV}’98},
	publisher = {Springer Berlin Heidelberg},
	author = {Nagel, H.-H. and Gehrke, A.},
	editor = {Burkhardt, Hans and Neumann, Bernd},
	month = jun,
	year = {1998},
	note = {DOI: 10.1007/BFb0054735},
	keywords = {Artificial Intelligence (incl. Robotics), Computer Graphics, Image Processing and Computer Vision, Pattern Recognition},
	pages = {86--102},
	file = {Snapshot:C\:\\Users\\MAI\\AppData\\Roaming\\Zotero\\Zotero\\Profiles\\8h4pxrbr.default\\zotero\\storage\\876X5UN7\\BFb0054735.html:text/html}
}

@incollection{brox_nonlinear_2002,
	series = {Lecture {Notes} in {Computer} {Science}},
	title = {Nonlinear {Matrix} {Diffusion} for {Optic} {Flow} {Estimation}},
	copyright = {©2002 Springer-Verlag Berlin Heidelberg},
	isbn = {978-3-540-44209-7 978-3-540-45783-1},
	url = {http://link.springer.com/chapter/10.1007/3-540-45783-6_54},
	abstract = {In this paper we present a method for nonlinear diffusion of matrix-valued data. We adapt this technique to the well-known linear structure tensor in order to develop a new nonlinear structure tensor. It is then used to improve the optic flow estimation methods of Lucas and Kanade and its spatio-temporal variant of Bigün et al.. Our experiments show that the nonlinear structure tensor leads to a better preservation of discontinuities in the optic flow field.},
	language = {en},
	number = {2449},
	urldate = {2016-07-11},
	booktitle = {Pattern {Recognition}},
	publisher = {Springer Berlin Heidelberg},
	author = {Brox, Thomas and Weickert, Joachim},
	editor = {Gool, Luc Van},
	month = sep,
	year = {2002},
	note = {DOI: 10.1007/3-540-45783-6\_54},
	keywords = {Artificial Intelligence (incl. Robotics), Computer Graphics, Image Processing and Computer Vision, Pattern Recognition},
	pages = {446--453},
	file = {Snapshot:C\:\\Users\\MAI\\AppData\\Roaming\\Zotero\\Zotero\\Profiles\\8h4pxrbr.default\\zotero\\storage\\DF4XQBD2\\10.html:text/html}
}

@inproceedings{middendorf_estimation_2001,
	title = {Estimation and interpretation of discontinuities in optical flow fields},
	volume = {1},
	doi = {10.1109/ICCV.2001.937515},
	abstract = {A systematic categorization of an adaptively determined local estimate for an OF-vector allows to detect non-local `walls of discontinuities' which establish an essentially closed boundary around images of objects whose motion differs from that of foreground and background. The estimation process has been refined to the point where occasionally observable-initially counter-intuitive-discontinuity blobs inside a region corresponding to the image of a moving object found an acceptable explanation. Segmentation results obtained on this basis for different real-world image sequences will be used to illustrate the approach},
	booktitle = {Eighth {IEEE} {International} {Conference} on {Computer} {Vision}, 2001. {ICCV} 2001. {Proceedings}},
	author = {Middendorf, M. and Nagel, H. H.},
	year = {2001},
	keywords = {Image motion analysis, image segmentation, image sequences, Layout, Motion detection, motion estimation, moving object, Object detection, optical flow fields, Optical recording, segmentation, Surveillance, systematic categorization, walls of discontinuities, Workstations},
	pages = {178--183 vol.1},
	file = {IEEE Xplore Abstract Record:C\:\\Users\\MAI\\AppData\\Roaming\\Zotero\\Zotero\\Profiles\\8h4pxrbr.default\\zotero\\storage\\UZ8SXPI2\\abs_all.html:text/html;IEEE Xplore Full Text PDF:C\:\\Users\\MAI\\AppData\\Roaming\\Zotero\\Zotero\\Profiles\\8h4pxrbr.default\\zotero\\storage\\4NAZVCZR\\Middendorf and Nagel - 2001 - Estimation and interpretation of discontinuities i.pdf:application/pdf}
}

@article{liu_accurate_2003,
	title = {Accurate dense optical flow estimation using adaptive structure tensors and a parametric model},
	volume = {12},
	issn = {1057-7149},
	doi = {10.1109/TIP.2003.815296},
	abstract = {An accurate optical flow estimation algorithm is proposed in this paper. By combining the three-dimensional (3D) structure tensor with a parametric flow model, the optical flow estimation problem is converted to a generalized eigenvalue problem. The optical flow can be accurately estimated from the generalized eigenvectors. The confidence measure derived from the generalized eigenvalues is used to adaptively adjust the coherent motion region to further improve the accuracy. Experiments using both synthetic sequences with ground truth and real sequences illustrate our method. Comparisons with classical and recently published methods are also given to demonstrate the accuracy of our algorithm.},
	number = {10},
	journal = {IEEE Transactions on Image Processing},
	author = {Liu, Haiying and Chellappa, R. and Rosenfeld, A.},
	month = oct,
	year = {2003},
	keywords = {3D structure tensor, accuracy, adaptive estimation, Adaptive optics, adaptive structure tensors, Biomedical optical imaging, coherent motion region, confidence measure, dense optical flow estimation, eigenvalues and eigenfunctions, generalized eigenvalue problem, generalized eigenvectors, ground truth, Image motion analysis, image sequences, Motion analysis, motion estimation, Nonlinear optics, Optical sensors, parameter estimation, parametric flow model, parametric model, Parametric statistics, real sequences, synthetic sequences, Tensile stress, tensors, three-dimensional structure tensor},
	pages = {1170--1180},
	file = {IEEE Xplore Abstract Record:C\:\\Users\\MAI\\AppData\\Roaming\\Zotero\\Zotero\\Profiles\\8h4pxrbr.default\\zotero\\storage\\C8T7JD4T\\abs_all.html:text/html;IEEE Xplore Full Text PDF:C\:\\Users\\MAI\\AppData\\Roaming\\Zotero\\Zotero\\Profiles\\8h4pxrbr.default\\zotero\\storage\\ZZERR987\\Liu et al. - 2003 - Accurate dense optical flow estimation using adapt.pdf:application/pdf}
}

%% Gloval method

@article{horn_determining_1981,
	title = {Determining optical flow},
	volume = {17},
	issn = {00043702},
	url = {http://linkinghub.elsevier.com/retrieve/pii/0004370281900242},
	doi = {10.1016/0004-3702(81)90024-2},
	language = {en},
	number = {1-3},
	urldate = {2016-05-04},
	journal = {Artificial Intelligence},
	author = {Horn, Berthold K.P. and Schunck, Brian G.},
	month = aug,
	year = {1981},
	pages = {185--203}
}

@Article{Horn1988,
	author="Horn, Berthold K. P.
	and Weldon, E. J.",
	title="Direct methods for recovering motion",
	journal="International Journal of Computer Vision",
	year="1988",
	month="Jun",
	day="01",
	volume="2",
	number="1",
	pages="51--76",
	abstract="We have developed direct methods for recovering the motion of an observer in a static environment in the case of pure rotation, pure translation, and arbitrary motion when the rotation is known. Some of these methods are based on the minimization of the difference between the observed time derivative of brightness and that predicted from the spatial brightness gradient, given the estimated motion. We minimize the square of the integral of this difference taken over the image region of interest. Other methods presented here exploit the fact that surfaces have to be in front of the observer in order to be seen.",
	issn="1573-1405",
	doi="10.1007/BF00836281",
	url="https://doi.org/10.1007/BF00836281"
}

@article{black_robust_1996-1,
	title = {The {Robust} {Estimation} of {Multiple} {Motions}: {Parametric} and {Piecewise}-{Smooth} {Flow} {Fields}},
	volume = {63},
	issn = {1077-3142},
	shorttitle = {The {Robust} {Estimation} of {Multiple} {Motions}},
	url = {},
	doi = {},
	abstract = {Most approaches for estimating optical flow assume that, within a finite image region, only a single motion is present. Thissingle motion assumptionis violated in common situations involving transparency, depth discontinuities, independently moving objects, shadows, and specular reflections. To robustly estimate optical flow, the single motion assumption must be relaxed. This paper presents a framework based onrobust estimationthat addresses violations of the brightness constancy and spatial smoothness assumptions caused by multiple motions. We show how therobust estimation frameworkcan be applied to standard formulations of the optical flow problem thus reducing their sensitivity to violations of their underlying assumptions. The approach has been applied to three standard techniques for recovering optical flow: area-based regression, correlation, and regularization with motion discontinuities. This paper focuses on the recovery of multiple parametric motion models within a region, as well as the recovery of piecewise-smooth flow fields, and provides examples with natural and synthetic image sequences.},
	number = {1},
	urldate = {2016-07-11},
	journal = {Computer Vision and Image Understanding},
	author = {Black, Michael J. and Anandan, P.},
	month = jan,
	year = {1996},
	pages = {75--104},
	file = {ScienceDirect Snapshot:C\:\\Users\\MAI\\AppData\\Roaming\\Zotero\\Zotero\\Profiles\\8h4pxrbr.default\\zotero\\storage\\ISBKUMNW\\S1077314296900065.html:text/html}
}

@article{black_robust_1996,
	title = {The {Robust} {Estimation} of {Multiple} {Motions}: {Parametric} and {Piecewise}-{Smooth} {Flow} {Fields}},
	volume = {63},
	issn = {10773142},
	shorttitle = {The {Robust} {Estimation} of {Multiple} {Motions}},
	url = {},
	doi = {},
	language = {en},
	number = {1},
	urldate = {2016-05-04},
	journal = {Computer Vision and Image Understanding},
	author = {Black, Michael J. and Anandan, P.},
	month = jan,
	year = {1996},
	pages = {75--104}
}

@inproceedings{wedel_structure-_2009,
	title = {Structure- and motion-adaptive regularization for high accuracy optic flow},
	isbn = {978-1-4244-4420-5},
	url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5459375},
	doi = {10.1109/ICCV.2009.5459375},
	urldate = {2016-05-04},
	publisher = {IEEE},
	author = {Wedel, Andreas and Cremers, Daniel and Pock, Thomas and Bischof, Horst},
	month = sep,
	year = {2009},
	pages = {1663--1668}
}

@inproceedings{kim_optical_2013,
	title = {Optical {Flow} via {Locally} {Adaptive} {Fusion} of {Complementary} {Data} {Costs}},
	isbn = {978-1-4799-2840-8},
	url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6751527},
	doi = {10.1109/ICCV.2013.415},
	urldate = {2016-05-04},
	publisher = {IEEE},
	author = {Kim, Tae Hyun and Lee, Hee Seok and Lee, Kyoung Mu},
	month = dec,
	year = {2013},
	pages = {3344--3351}
}

@inproceedings{xu_motion_2010,
	title = {Motion detail preserving optical flow estimation},
	isbn = {978-1-4244-6984-0},
	url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5539820},
	doi = {10.1109/CVPR.2010.5539820},
	urldate = {2016-06-13},
	publisher = {IEEE},
	author = {Xu, Li and Jia, Jiaya and Matsushita, Yasuyuki},
	month = jun,
	year = {2010},
	pages = {1293--1300}
}

@inproceedings{stoll_joint_2013,
	title = {Joint trilateral filtering for multiframe optical flow},
	doi = {10.1109/ICIP.2013.6738792},
	abstract = {Since two years there is a recent trend in optical flow estimation to improve the results of state-of-the-art variational methods by applying additional filtering steps such as median filters, bilateral filters, and non-local techniques. So far, however, the application of such filters has been restricted to two-frame optical flow methods. In this paper, we go beyond this two-frame case and investigate the usefulness of such filtering steps for multi-frame optical flow estimation. Thereby we consider both the application to single flow fields as well as the filtering of the entire spatio-temporal flow volume. In this context, we propose the use of a joint trilateral filter that processes all flow fields simultaneously while imposing consistency of joint flow structures at the same time. Evaluations on the Middlebury benchmark clearly demonstrate the success of our filtering strategy. Achieving rank 3, our method yields state-of-the art results and significantly outperforms the baseline method providing considerably sharper results.},
	booktitle = {2013 {IEEE} {International} {Conference} on {Image} {Processing}},
	author = {Stoll, M. and Volz, S. and Bruhn, A.},
	month = sep,
	year = {2013},
	keywords = {bilateral filters, cross bilateral upsampling, filtering theory, image sequences, joint flow structure consistency, joint trilateral filtering, median filters, Middlebury benchmark, Multiframe Optical Flow, multiframe optical flow estimation, nonlocal techniques, spatio-temporal flow volume, variational methods, variational techniques},
	pages = {3845--3849},
	file = {IEEE Xplore Abstract Record:C\:\\Users\\MAI\\AppData\\Roaming\\Zotero\\Zotero\\Profiles\\8h4pxrbr.default\\zotero\\storage\\PXAHFZSV\\abstractAuthors.html:text/html;IEEE Xplore Full Text PDF:C\:\\Users\\MAI\\AppData\\Roaming\\Zotero\\Zotero\\Profiles\\8h4pxrbr.default\\zotero\\storage\\GDHDVIP9\\Stoll et al. - 2013 - Joint trilateral filtering for multiframe optical .pdf:application/pdf}
}

@incollection{hutchison_improved_2009,
	address = {Berlin, Heidelberg},
	title = {An {Improved} {Algorithm} for {TV}-{L} 1 {Optical} {Flow}},
	volume = {5604},
	isbn = {978-3-642-03060-4 978-3-642-03061-1},
	url = {http://link.springer.com/10.1007/978-3-642-03061-1_2},
	urldate = {2016-05-04},
	booktitle = {Statistical and {Geometrical} {Approaches} to {Visual} {Motion} {Analysis}},
	publisher = {Springer Berlin Heidelberg},
	author = {Wedel, Andreas and Pock, Thomas and Zach, Christopher and Bischof, Horst and Cremers, Daniel},
	editor = {Hutchison, David and Kanade, Takeo and Kittler, Josef and Kleinberg, Jon M. and Mattern, Friedemann and Mitchell, John C. and Naor, Moni and Nierstrasz, Oscar and Pandu Rangan, C. and Steffen, Bernhard and Sudan, Madhu and Terzopoulos, Demetri and Tygar, Doug and Vardi, Moshe Y. and Weikum, Gerhard and Cremers, Daniel and Rosenhahn, Bodo and Yuille, Alan L. and Schmidt, Frank R.},
	year = {2009},
	pages = {23--45}
}

@inproceedings{yang_dense_2015,
	title = {Dense, accurate optical flow estimation with piecewise parametric model},
	isbn = {978-1-4673-6964-0},
	url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=7298704},
	doi = {10.1109/CVPR.2015.7298704},
	urldate = {2016-06-13},
	publisher = {IEEE},
	author = {Yang, Jiaolong and Li, Hongdong},
	month = jun,
	year = {2015},
	pages = {1019--1027}
}

@article{alvarez_reliable_nodate,
	title = {Reliable {Estimation} of {Dense} {Optical} {Flow} {Fields} with {Large} {Displacements}},
	volume = {39},
	issn = {0920-5691, 1573-1405},
	url = {http://link.springer.com/article/10.1023/A:1008170101536},
	doi = {10.1023/A:1008170101536},
	abstract = {In this paper we show that a classic optical flow technique by Nagel and Enkelmann (1986, IEEE Trans. Pattern Anal. Mach. Intell., Vol. 8, pp. 565–593) can be regarded as an early anisotropic diffusion method with a diffusion tensor. We introduce three improvements into the model formulation that (i) avoid inconsistencies caused by centering the brightness term and the smoothness term in different images, (ii) use a linear scale-space focusing strategy from coarse to fine scales for avoiding convergence to physically irrelevant local minima, and (iii) create an energy functional that is invariant under linear brightness changes. Applying a gradient descent method to the resulting energy functional leads to a system of diffusion–reaction equations. We prove that this system has a unique solution under realistic assumptions on the initial data, and we present an efficient linear implicit numerical scheme in detail. Our method creates flow fields with 100 \% density over the entire image domain, it is robust under a large range of parameter variations, and it can recover displacement fields that are far beyond the typical one-pixel limits which are characteristic for many differential methods for determining optical flow. We show that it performs better than the optical flow methods with 100 \% density that are evaluated by Barron et al. (1994, Int. J. Comput. Vision, Vol. 12, pp. 43–47). Our software is available from the Internet.},
	language = {en},
	number = {1},
	urldate = {2016-08-25},
	journal = {International Journal of Computer Vision},
	author = {Alvarez, Luis and Weickert, Joachim and Sánchez, Javier},
	pages = {41--56},
	file = {Snapshot:C\:\\Users\\MAI\\AppData\\Roaming\\Zotero\\Zotero\\Profiles\\8h4pxrbr.default\\zotero\\storage\\4WZRMFSR\\A1008170101536.html:text/html}
}

@article{yan_niu_locally_2012,
	title = {Locally {Oriented} {Optical} {Flow} {Computation}},
	volume = {21},
	issn = {1057-7149, 1941-0042},
	url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6093967},
	doi = {10.1109/TIP.2011.2177847},
	number = {4},
	urldate = {2016-06-14},
	journal = {IEEE Transactions on Image Processing},
	author = {{Yan Niu} and Dick, A. and Brooks, M.},
	month = apr,
	year = {2012},
	pages = {1573--1586}
}

@inproceedings{brox_large_2009,
	title = {Large displacement optical flow},
	isbn = {978-1-4244-3992-8},
	url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5206697},
	doi = {10.1109/CVPR.2009.5206697},
	urldate = {2016-06-13},
	publisher = {IEEE},
	author = {Brox, T. and Bregler, C. and Malik, J.},
	month = jun,
	year = {2009},
	pages = {41--48}
}

@article{brox_large_2011,
	title = {Large {Displacement} {Optical} {Flow}: {Descriptor} {Matching} in {Variational} {Motion} {Estimation}},
	volume = {33},
	issn = {0162-8828},
	shorttitle = {Large {Displacement} {Optical} {Flow}},
	url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5551149},
	doi = {10.1109/TPAMI.2010.143},
	number = {3},
	urldate = {2016-05-04},
	journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
	author = {Brox, T and Malik, J},
	month = mar,
	year = {2011},
	pages = {500--513}
}

@article{ce_liu_sift_2011,
	title = {{SIFT} {Flow}: {Dense} {Correspondence} across {Scenes} and {Its} {Applications}},
	volume = {33},
	issn = {0162-8828, 2160-9292},
	shorttitle = {{SIFT} {Flow}},
	url = {http://ieeexplore.ieee.org/document/5551153/},
	doi = {10.1109/TPAMI.2010.147},
	number = {5},
	urldate = {2016-12-14},
	journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
	author = {{Ce Liu} and Yuen, Jenny and Torralba, Antonio},
	month = may,
	year = {2011},
	pages = {978--994}
}

@inproceedings{torr_feature_1999,
	title = {Feature {Based} {Methods} for {Structure} and {Motion} {Estimation}},
	url = {https://link.springer.com/chapter/10.1007/3-540-44480-7_19},
	doi = {10.1007/3-540-44480-7_19},
	abstract = {This report is a brief overview of the use of “feature based” methods in structure and motion computation. A companion paper by Irani and Anandan [16] reviews “direct” methods.},
	language = {en},
	urldate = {2017-04-14},
	booktitle = {Vision {Algorithms}: {Theory} and {Practice}},
	publisher = {Springer, Berlin, Heidelberg},
	author = {Torr, P. H. S. and Zisserman, A.},
	month = sep,
	year = {1999},
	pages = {278--294},
	file = {Snapshot:C\:\\Users\\MAI\\AppData\\Roaming\\Zotero\\Zotero\\Profiles\\8h4pxrbr.default\\zotero\\storage\\G33P7AFP\\3-540-44480-7_19.html:text/html}
}

@inproceedings{weinzaepfel:hal-00873592,
	AUTHOR = {Weinzaepfel, Philippe and Revaud, Jerome and Harchaoui, Zaid and Schmid, Cordelia},
	TITLE = {{DeepFlow: Large displacement optical flow with deep matching}},
	BOOKTITLE = {{IEEE Intenational Conference on Computer Vision (ICCV)}},
	YEAR = {2013},
	MONTH = Dec,
	ADDRESS = {Sydney, Australia},
	URL = {http://hal.inria.fr/hal-00873592}
}

@inproceedings{Menze2015GCPR,
	title = {Discrete Optimization for Optical Flow},
	author = {Menze, Moritz and Heipke, Christian and Geiger, Andreas},
	booktitle = {German Conference on Pattern Recognition (GCPR)},
	volume = {9358},
	pages = {16--28},
	publisher = {Springer International Publishing},
	year = {2015}
}

@inproceedings{chen_large_2013,
	title = {Large {Displacement} {Optical} {Flow} from {Nearest} {Neighbor} {Fields}},
	isbn = {978-0-7695-4989-7},
	url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6619160},
	doi = {10.1109/CVPR.2013.316},
	urldate = {2016-05-04},
	publisher = {IEEE},
	author = {Chen, Zhuoyuan and Jin, Hailin and Lin, Zhe and Cohen, Scott and Wu, Ying},
	month = jun,
	year = {2013},
	pages = {2443--2450}
}

@inproceedings{sun_secrets_2010,
	title = {Secrets of optical flow estimation and their principles},
	isbn = {978-1-4244-6984-0},
	url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5539939},
	doi = {10.1109/CVPR.2010.5539939},
	urldate = {2016-05-04},
	publisher = {IEEE},
	author = {Sun, Deqing and Roth, Stefan and Black, Michael J.},
	month = jun,
	year = {2010},
	pages = {2432--2439}
}

@article{Sun:IJCV:2014,
	title = {A Quantitative Analysis of Current Practices in Optical Flow Estimation and the Principles behind Them},
	author = {Sun, Deqing and Roth, Stefan and Black, Michael J.},
	journal = {International Journal of Computer Vision (IJCV)},
	volume = {106},
	number = {2},
	pages = {115--137},
	year = {2014}
}

@inproceedings{Sun10,
	added-at = {2011-05-09T11:38:19.000+0200},
	author = {Sun, D. and Sudderth, E. and Black, M. J.},
	biburl = {http://www.bibsonomy.org/bibtex/2266fde18afbbd16f0fc2de6a051a678e/meduz},
	citeulike-article-id = {9076408},
	citeulike-linkout-0 = {http://www.cs.brown.edu/\~{}black/Papers/nips10paperplus.pdf},
	editor = {Press, M. I. T.},
	groups = {public},
	interhash = {a21697b158e2f24ab55cbd87d43c3351},
	intrahash = {266fde18afbbd16f0fc2de6a051a678e},
	journal = {Advances in Neural Information Processing Systems},
	keywords = {motion\_depth},
	posted-at = {2011-03-30 10:12:20},
	priority = {0},
	timestamp = {2011-05-09T15:46:21.000+0200},
	title = {Layered Image Motion with Explicit Occlusions, Temporal Consistency, and Depth Ordering},
	url = {http://www.cs.brown.edu/\~{}black/Papers/nips10paperplus.pdf},
	username = {meduz},
	volume = 23,
	year = 2010
}

@INPROCEEDINGS{6751281,
	author={C. Vogel and K. Schindler and S. Roth},
	booktitle={2013 IEEE International Conference on Computer Vision},
	title={Piecewise Rigid Scene Flow},
	year={2013},
	pages={1377-1384},
	keywords={image motion analysis;image segmentation;image sequences;parameter estimation;stereo image processing;2D motion estimates;2D optical flow estimation;3D position;3D scene flow methods;KITTI benchmark;dense 3D scene flow estimation;dynamic 3D scene;initial superpixelization;local segments;motion parameter estimation;occlusion-sensitive data;optical flow techniques;piecewise rigid scene flow;pixel-to-segment assignment;planar collection;real-world image sets;rigid motion parameters;scene flow estimation;segmentation regularizers;shape parameter estimation;stereo sequences;Estimation;Image segmentation;Motion segmentation;Optical imaging;Shape;Stereo vision;Three-dimensional displays},
	doi={10.1109/ICCV.2013.174},
	ISSN={1550-5499},
	month={Dec},}

@Inbook{Vogel2014,
	author="Vogel, Christoph
	and Roth, Stefan
	and Schindler, Konrad",
	editor="Fleet, David
	and Pajdla, Tomas
	and Schiele, Bernt
	and Tuytelaars, Tinne",
	title="View-Consistent 3D Scene Flow Estimation over Multiple Frames",
	bookTitle="Computer Vision -- ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part IV",
	year="2014",
	publisher="Springer International Publishing",
	address="Cham",
	pages="263--278",
	abstract="We propose a method to recover dense 3D scene flow from stereo video. The method estimates the depth and 3D motion field of a dynamic scene from multiple consecutive frames in a sliding temporal window, such that the estimate is consistent across both viewpoints of all frames within the window. The observed scene is modeled as a collection of planar patches that are consistent across views, each undergoing a rigid motion that is approximately constant over time. Finding the patches and their motions is cast as minimization of an energy function over the continuous plane and motion parameters and the discrete pixel-to-plane assignment. We show that such a view-consistent multi-frame scheme greatly improves scene flow computation in the presence of occlusions, and increases its robustness against adverse imaging conditions, such as specularities. Our method currently achieves leading performance on the KITTI benchmark, for both flow and stereo.",
	isbn="978-3-319-10593-2",
	doi="10.1007/978-3-319-10593-2_18",
	url="http://dx.doi.org/10.1007/978-3-319-10593-2_18"
}

@Article{Vogel2015,
	author="Vogel, Christoph
	and Schindler, Konrad
	and Roth, Stefan",
	title="3D Scene Flow Estimation with a Piecewise Rigid Scene Model",
	journal="International Journal of Computer Vision",
	year="2015",
	month="Oct",
	day="01",
	volume="115",
	number="1",
	pages="1--28",
	abstract="3D scene flow estimation aims to jointly recover dense geometry and 3D motion from stereoscopic image sequences, thus generalizes classical disparity and 2D optical flow estimation. To realize its conceptual benefits and overcome limitations of many existing methods, we propose to represent the dynamic scene as a collection of rigidly moving planes, into which the input images are segmented. Geometry and 3D motion are then jointly recovered alongside an over-segmentation of the scene. This piecewise rigid scene model is significantly more parsimonious than conventional pixel-based representations, yet retains the ability to represent real-world scenes with independent object motion. It, furthermore, enables us to define suitable scene priors, perform occlusion reasoning, and leverage discrete optimization schemes toward stable and accurate results. Assuming the rigid motion to persist approximately over time additionally enables us to incorporate multiple frames into the inference. To that end, each view holds its own representation, which is encouraged to be consistent across all other viewpoints and frames in a temporal window. We show that such a view-consistent multi-frame scheme significantly improves accuracy, especially in the presence of occlusions, and increases robustness against adverse imaging conditions. Our method currently achieves leading performance on the KITTI benchmark, for both flow and stereo.",
	issn="1573-1405",
	doi="10.1007/s11263-015-0806-0",
	url="http://dx.doi.org/10.1007/s11263-015-0806-0"
}

@ARTICLE{Menze2018JPRS,
	author = {Moritz Menze and Christian Heipke and Andreas Geiger},
	title = {Object Scene Flow},
	journal = {ISPRS Journal of Photogrammetry and Remote Sensing (JPRS)},
	year = {2018}
} 

@book{jahne_handbook_1999,
	series = {Handbook of {Computer} {Vision} and {Applications}},
	title = {Handbook of {Computer} {Vision} and {Applications}: {Signal} processing and pattern recognition},
	isbn = {978-0-12-379772-8},
	url = {},
	number = {Vol. 2},
	publisher = {Academic Press},
	author = {Jähne, B. and Haussecker, H. and Geissler, P.},
	year = {1999},
	lccn = {98042541}
}

@Inbook{Kondermann2008,
	author="Kondermann, Claudia
	and Mester, Rudolf
	and Garbe, Christoph",
	editor="Forsyth, David
	and Torr, Philip
	and Zisserman, Andrew",
	title="A Statistical Confidence Measure for Optical Flows",
	bookTitle="Computer Vision -- ECCV 2008: 10th European Conference on Computer Vision, Marseille, France, October 12-18, 2008, Proceedings, Part III",
	year="2008",
	publisher="Springer Berlin Heidelberg",
	address="Berlin, Heidelberg",
	pages="290--301",
	abstract="Confidence measures are crucial to the interpretation of any optical flow measurement. Even though numerous methods for estimating optical flow have been proposed over the last three decades, a sound, universal, and statistically motivated confidence measure for optical flow measurements is still missing. We aim at filling this gap with this contribution, where such a confidence measure is derived, using statistical test theory and measurable statistics of flow fields from the regarded domain. The new confidence measure is computed from merely the results of the optical flow estimator and hence can be applied to any optical flow estimation method, covering the range from local parametric to global variational approaches. Experimental results using state-of-the-art optical flow estimators and various test sequences demonstrate the superiority of the proposed technique compared to existing 'confidence' measures.",
	isbn="978-3-540-88690-7",
	doi="10.1007/978-3-540-88690-7_22",
	url="https://doi.org/10.1007/978-3-540-88690-7_22"
}

@ARTICLE{6261321,
	author={O. Mac Aodha and A. Humayun and M. Pollefeys and G. J. Brostow},
	journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
	title={Learning a Confidence Measure for Optical Flow},
	year={2013},
	volume={35},
	number={5},
	pages={1107-1120},
	keywords={estimation theory;feature extraction;image sequences;image texture;learning (artificial intelligence);spatiotemporal phenomena;vectors;image pixel confidence estimation;image texture;occlusion boundaries;optical flow algorithm selection;optical flow confidence measure learning;optical flow vectors;real sequences;spatiotemporal feature vector;supervised learning-based method;synthetic sequences;Accuracy;Adaptive optics;Optical imaging;Optical variables measurement;Prediction algorithms;Supervised learning;Vectors;Optical flow;Random Forest;algorithm selection;confidence measure;synthetic data},
	doi={10.1109/TPAMI.2012.171},
	ISSN={0162-8828},
	month={May},}

@conference{visapp17,
	author={Tan Khoa Mai and Michèle Gouiffès and Samia Bouchafa},
	title={Optical Flow Refinement using Reliable Flow Propagation},
	booktitle={Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 6: VISAPP, (VISIGRAPP 2017)},
	year={2017},
	pages={451-458},
	doi={},
	isbn={978-989-758-227-1},
}

@article{baker_database_2010,
	title = {A {Database} and {Evaluation} {Methodology} for {Optical} {Flow}},
	volume = {92},
	issn = {0920-5691, 1573-1405},
	url = {http://link.springer.com/article/10.1007/s11263-010-0390-2},
	doi = {10.1007/s11263-010-0390-2},
	abstract = {The quantitative evaluation of optical flow algorithms by Barron et al. (1994) led to significant advances in performance. The challenges for optical flow algorithms today go beyond the datasets and evaluation methods proposed in that paper. Instead, they center on problems associated with complex natural scenes, including nonrigid motion, real sensor noise, and motion discontinuities. We propose a new set of benchmarks and evaluation methods for the next generation of optical flow algorithms. To that end, we contribute four types of data to test different aspects of optical flow algorithms: (1) sequences with nonrigid motion where the ground-truth flow is determined by tracking hidden fluorescent texture, (2) realistic synthetic sequences, (3) high frame-rate video used to study interpolation error, and (4) modified stereo sequences of static scenes. In addition to the average angular error used by Barron et al., we compute the absolute flow endpoint error, measures for frame interpolation error, improved statistics, and results at motion discontinuities and in textureless regions. In October 2007, we published the performance of several well-known methods on a preliminary version of our data to establish the current state of the art. We also made the data freely available on the web at http://vision.middlebury.edu/flow/. Subsequently a number of researchers have uploaded their results to our website and published papers using the data. A significant improvement in performance has already been achieved. In this paper we analyze the results obtained to date and draw a large number of conclusions from them.},
	language = {en},
	number = {1},
	urldate = {2016-08-24},
	journal = {International Journal of Computer Vision},
	author = {Baker, Simon and Scharstein, Daniel and Lewis, J. P. and Roth, Stefan and Black, Michael J. and Szeliski, Richard},
	month = nov,
	year = {2010},
	pages = {1--31},
	file = {Full Text PDF:C\:\\Users\\MAI\\AppData\\Roaming\\Zotero\\Zotero\\Profiles\\8h4pxrbr.default\\zotero\\storage\\QDJJCP4K\\Baker et al. - 2010 - A Database and Evaluation Methodology for Optical .pdf:application/pdf;Snapshot:C\:\\Users\\MAI\\AppData\\Roaming\\Zotero\\Zotero\\Profiles\\8h4pxrbr.default\\zotero\\storage\\2CEVJW2A\\s11263-010-0390-2.html:text/html}
}

@Inbook{Bruhn2006,
	author="Bruhn, A.
	and Weickert, J.",
	editor="Klette, Reinhard
	and Kozera, Ryszard
	and Noakes, Lyle
	and Weickert, Joachim",
	title="A Confidence Measure for Variational Optic flow Methods",
	bookTitle="Geometric Properties for Incomplete data",
	year="2006",
	publisher="Springer Netherlands",
	address="Dordrecht",
	pages="283--298",
	isbn="978-1-4020-3858-7",
	doi="10.1007/1-4020-3858-8_15",
	url="https://doi.org/10.1007/1-4020-3858-8_15"
}


%%--------------------------------------------------------------------------------------------------------------------------------------------------
%%--------------------------------------------------------------------------------------------------------------------------------------------------

@article{besse_pmbp:_2014,
	title = {{PMBP}: {PatchMatch} {Belief} {Propagation} for {Correspondence} {Field} {Estimation}},
	volume = {110},
	issn = {0920-5691, 1573-1405},
	shorttitle = {{PMBP}},
	url = {http://link.springer.com/10.1007/s11263-013-0653-9},
	doi = {10.1007/s11263-013-0653-9},
	language = {en},
	number = {1},
	urldate = {2016-05-09},
	journal = {International Journal of Computer Vision},
	author = {Besse, Frederic and Rother, Carsten and Fitzgibbon, Andrew and Kautz, Jan},
	month = oct,
	year = {2014},
	pages = {2--13}
}

@inproceedings{yacoob_temporal_1997,
	title = {Temporal multi-scale models for flow and acceleration},
	doi = {10.1109/CVPR.1997.609438},
	abstract = {A model for computing image flow in image sequences containing a very wide range of instantaneous flows is proposed. This model integrates the spatio-temporal image derivatives from multiple temporal scales to provide both reliable and accurate instantaneous flow estimates. The integration employs robust regression and automatic scale weighting in a generalized brightness constancy framework. In addition to instantaneous flow estimation the model supports recovery of dense estimates of image acceleration and can be readily combined with parameterized flow and acceleration models. A demonstration of performance on image sequences of typical human actions taken with a high frame-rate camera, is given},
	booktitle = {, 1997 {IEEE} {Computer} {Society} {Conference} on {Computer} {Vision} and {Pattern} {Recognition}, 1997. {Proceedings}},
	author = {Yacoob, Y. and Davis, L. S.},
	month = jun,
	year = {1997},
	keywords = {acceleration, automatic scale weighting, Cameras, Computer vision, dense estimates, flow, Fluid flow measurement, Frequency estimation, generalized brightness constancy framework, high frame-rate camera, Humans, image acceleration, image flow, image sequences, instantaneous flows, motion estimation, multiple temporal scales, multi-scale models, Pixel, Robustness, robust regression, spatio-temporal image derivatives},
	pages = {921--927},
	file = {IEEE Xplore Abstract Record:C\:\\Users\\MAI\\AppData\\Roaming\\Zotero\\Zotero\\Profiles\\8h4pxrbr.default\\zotero\\storage\\RG595KSZ\\abs_all.html:text/html;IEEE Xplore Full Text PDF:C\:\\Users\\MAI\\AppData\\Roaming\\Zotero\\Zotero\\Profiles\\8h4pxrbr.default\\zotero\\storage\\SFCAPADH\\Yacoob and Davis - 1997 - Temporal multi-scale models for flow and accelerat.pdf:application/pdf}
}


@inproceedings{besnerais_dense_2005,
	title = {Dense optical flow by iterative local window registration},
	volume = {1},
	doi = {10.1109/ICIP.2005.1529706},
	abstract = {We study dense optical flow estimation using iterative registration of local window, also known as iterative Lucas-Kanade (LK) [B. Lucas et al, 1981]. We show that the usual iterative-warping scheme encounters divergence problems and propose a modified scheme with better behavior. It yields good results with a much lower cost than the exact dense LK algorithm, on simulated and real sequences.},
	booktitle = {{IEEE} {International} {Conference} on {Image} {Processing} 2005},
	author = {Besnerais, G. Le and Champagnat, F.},
	month = sep,
	year = {2005},
	keywords = {convergent scheme, dense optical flow estimation, Design for disassembly, Image motion analysis, image registration, image sequences, Interpolation, Iterative algorithms, iterative local window registration, iterative Lucas-Kanade algorithm, iterative methods, Least squares methods, Newton method, Nonlinear optics, Optical filters, Recursive estimation, Spline},
	pages = {I--137--40},
	file = {IEEE Xplore Abstract Record:C\:\\Users\\MAI\\AppData\\Roaming\\Zotero\\Zotero\\Profiles\\8h4pxrbr.default\\zotero\\storage\\KUV5CJ6Q\\1529706.html:text/html;IEEE Xplore Full Text PDF:C\:\\Users\\MAI\\AppData\\Roaming\\Zotero\\Zotero\\Profiles\\8h4pxrbr.default\\zotero\\storage\\NZQZ5PHH\\Besnerais and Champagnat - 2005 - Dense optical flow by iterative local window regis.pdf:application/pdf}
}

@article{bigun_multidimensional_1991,
	title = {Multidimensional orientation estimation with applications to texture analysis and optical flow},
	volume = {13},
	issn = {0162-8828},
	doi = {10.1109/34.85668},
	abstract = {The problem of detection of orientation in finite dimensional Euclidean spaces is solved in the least squares sense. The theory is developed for the case when such orientation computations are necessary at all local neighborhoods of the n-dimensional Euclidean space. Detection of orientation is shown to correspond to fitting an axis or a plane to the Fourier transform of an n-dimensional structure. The solution of this problem is related to the solution of a well-known matrix eigenvalue problem. The computations can be performed in the spatial domain without actually doing a Fourier transformation. Along with the orientation estimate, a certainty measure, based on the error of the fit, is proposed. Two applications in image analysis are considered: texture segmentation and optical flow. The theory is verified by experiments which confirm accurate orientation estimates and reliable certainty measures in the presence of noise. The comparative results indicate that the theory produces algorithms computing robust texture features as well as optical flow},
	number = {8},
	journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
	author = {Bigun, J. and Granlund, G. H. and Wiklund, J.},
	month = aug,
	year = {1991},
	keywords = {certainty measure, Computer vision, eigenvalues and eigenfunctions, finite dimensional Euclidean spaces, Fourier transform, Fourier transforms, image analysis, Image motion analysis, image segmentation, Image texture analysis, least squares approximations, Least squares methods, multidimensional orientation estimation, Multidimensional systems, optical flow, Optical noise, Optical sensors, orientation estimate, Reliability theory, spatial domain, texture analysis, texture segmentation},
	pages = {775--790},
	file = {IEEE Xplore Abstract Record:C\:\\Users\\MAI\\AppData\\Roaming\\Zotero\\Zotero\\Profiles\\8h4pxrbr.default\\zotero\\storage\\PMTDV3RR\\85668.html:text/html;IEEE Xplore Full Text PDF:C\:\\Users\\MAI\\AppData\\Roaming\\Zotero\\Zotero\\Profiles\\8h4pxrbr.default\\zotero\\storage\\TG35IAJG\\Bigun et al. - 1991 - Multidimensional orientation estimation with appli.pdf:application/pdf}
}

@inproceedings{giachetti_refinement_1996,
	title = {Refinement of {Optical} {Flow} {Estimation} and {Detection} of {Motion} {Edges}},
	abstract = {. In this paper a multi-scale method for the estimation of optical flow and a simple technique for the extraction of motion edges from an image sequence are presented. The proposed method is based on a differential neighborhood-sampling technique combined with a multi-scale approach and flow filtering techniques. The multi-scale approach is introduced to overcome the aliasing problem in the computation of spatial and temporal derivatives. The flow filtering is useful near motion boundaries to preserve discontinuities. A residual function, which is a confidence measure of the least-squares fit used to compute the optical flow, is introduced and used to filter the flow and to detect motion boundaries. These boundaries, that we call motion edges are extracted by searching for the directional maxima of the map obtained by thinning this residual function. The proposed method has been tested in a variety of conditions. The results obtained with test images show that the proposed approach is ...},
	booktitle = {In {ECCV}96},
	author = {Giachetti, Andrea and Torre, Vincent},
	year = {1996},
	pages = {151--160},
	file = {Citeseer - Full Text PDF:C\:\\Users\\MAI\\AppData\\Roaming\\Zotero\\Zotero\\Profiles\\8h4pxrbr.default\\zotero\\storage\\WH68MV7D\\Giachetti and Torre - 1996 - Refinement of Optical Flow Estimation and Detectio.pdf:application/pdf;Citeseer - Snapshot:C\:\\Users\\MAI\\AppData\\Roaming\\Zotero\\Zotero\\Profiles\\8h4pxrbr.default\\zotero\\storage\\N6FMPTDQ\\summary.html:text/html}
}


@inproceedings{singh_robust_2004,
	title = {Robust {KLT} tracking with {Gaussian} and {Laplacian} of {Gaussian} weighting functions},
	volume = {4},
	doi = {10.1109/ICPR.2004.1333859},
	abstract = {Object tracking algorithms extensively found in literature are either constrained with assumptions or are overly sensitive to noise. We propose and successfully test two new weighting functions for a feature-based object tracker to achieve superior tracking performance and noise immunity. The paper also presents a mechanism for image based optimal weighting function determination.},
	booktitle = {Proceedings of the 17th {International} {Conference} on {Pattern} {Recognition}, 2004. {ICPR} 2004.},
	author = {Singh, M. and Mandal, M. and Basu, A.},
	month = aug,
	year = {2004},
	keywords = {Clustering algorithms, Computer vision, Feature extraction, Gaussian processes, Gaussian weighting function, Humans, Image Processing, Iterative algorithms, Karhunen-Loeve transforms, KLT tracking, Laplace equations, Laplacian weighting function, Motion analysis, object tracking algorithm, Robustness, tracking},
	pages = {661--664 Vol.4},
	file = {IEEE Xplore Abstract Record:C\:\\Users\\MAI\\AppData\\Roaming\\Zotero\\Zotero\\Profiles\\8h4pxrbr.default\\zotero\\storage\\RWE8ZG8W\\1333859.html:text/html;IEEE Xplore Full Text PDF:C\:\\Users\\MAI\\AppData\\Roaming\\Zotero\\Zotero\\Profiles\\8h4pxrbr.default\\zotero\\storage\\TE9SI6PR\\Singh et al. - 2004 - Robust KLT tracking with Gaussian and Laplacian of.pdf:application/pdf}
}



@conference{Guney2016ACCV,
	title = {Deep Discrete Flow},
	author = {G{\"u}ney, Fatma and Geiger, Andreas},
	booktitle = {Asian Conference on Computer Vision (ACCV)},
	year = {2016}
}

@inproceedings{Lauze2004ACT,
	title={A Coarse To Fine Multiscale Approach For Linear Least Squares Optical Flow Estimation},
	author={F. Lauze and P. Kornprobst and E. M{\'e}min},
	year={2004}
}

@inproceedings{valgaerts_variational_2008,
	title = {A {Variational} {Model} for the {Joint} {Recovery} of the {Fundamental} {Matrix} and the {Optical} {Flow}},
	url = {https://link.springer.com/chapter/10.1007/978-3-540-69321-5_32},
	doi = {10.1007/978-3-540-69321-5_32},
	abstract = {Traditional estimation methods for the fundamental matrix rely on a sparse set of point correspondences that have been established by matching salient image features between two images. Recovering the fundamental matrix from dense correspondences has not been extensively researched until now. In this paper we propose a new variational model that recovers the fundamental matrix from a pair of uncalibrated stereo images, and simultaneously estimates an optical flow field that is consistent with the corresponding epipolar geometry. The model extends the highly accurate optical flow technique of Brox et al. (2004) by taking the epipolar constraint into account. In experiments we demonstrate that our approach is able to produce excellent estimates for the fundamental matrix and that the optical flow computation is on par with the best techniques to date.},
	language = {en},
	urldate = {2017-06-02},
	booktitle = {Pattern {Recognition}},
	publisher = {Springer, Berlin, Heidelberg},
	author = {Valgaerts, Levi and Bruhn, Andrés and Weickert, Joachim},
	month = jun,
	year = {2008},
	pages = {314--324}
}

@InProceedings{Wulff_2015_CVPR,
	author = {Wulff, Jonas and Black, Michael J.},
	title = {Efficient Sparse-to-Dense Optical Flow Estimation Using a Learned Basis and Layers},
	booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
	month = {June},
	year = {2015}
} 

@INPROCEEDINGS{MatthieuFastSemi,
	author={M. Garrigues and A. Manzanera},
	booktitle={2017 IEEE Winter Conference on Applications of Computer Vision (WACV)},
	title={Fast Semi Dense Epipolar Flow Estimation},
	year={2017},
	pages={427-435},
	keywords={Adaptive optics;Benchmark testing;Cameras;Estimation;Optical computing;Optical imaging;Three-dimensional displays},
	doi={10.1109/WACV.2017.54},
	month={March},}

@Inbook{Kroeger2016,
	author="Kroeger, Till
	and Timofte, Radu
	and Dai, Dengxin
	and Van Gool, Luc",
	editor="Leibe, Bastian
	and Matas, Jiri
	and Sebe, Nicu
	and Welling, Max",
	title="Fast Optical Flow Using Dense Inverse Search",
	bookTitle="Computer Vision -- ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11--14, 2016, Proceedings, Part IV",
	year="2016",
	publisher="Springer International Publishing",
	address="Cham",
	pages="471--488",
	abstract="Most recent works in optical flow extraction focus on the accuracy and neglect the time complexity. However, in real-life visual applications, such as tracking, activity detection and recognition, the time complexity is critical. We propose a solution with very low time complexity and competitive accuracy for the computation of dense optical flow. It consists of three parts: (1) inverse search for patch correspondences; (2) dense displacement field creation through patch aggregation along multiple scales; (3) variational refinement. At the core of our Dense Inverse Search-based method (DIS) is the efficient search of correspondences inspired by the inverse compositional image alignment proposed by Baker and Matthews (2001, 2004). DIS is competitive on standard optical flow benchmarks. DIS runs at 300 Hz up to 600 Hz on a single CPU core (1024                                                                                   {\$}{\$}{\backslash}times {\$}{\$}                                                                  {\texttimes}                                                             436 resolution. 42 Hz/46 Hz when including preprocessing: disk access, image re-scaling, gradient computation. More details in Sect. 3.1.), reaching the temporal resolution of human's biological vision system. It is order(s) of magnitude faster than state-of-the-art methods in the same range of accuracy, making DIS ideal for real-time applications.",
	isbn="978-3-319-46493-0",
	doi="10.1007/978-3-319-46493-0_29",
	url="http://dx.doi.org/10.1007/978-3-319-46493-0_29"
}

@Article{Plyer2016,
	author="Plyer, Aur{\'e}lien
	and Le Besnerais, Guy
	and Champagnat, Fr{\'e}d{\'e}ric",
	title="Massively parallel Lucas Kanade optical flow for real-time video processing applications",
	journal="Journal of Real-Time Image Processing",
	year="2016",
	month="Apr",
	day="01",
	volume="11",
	number="4",
	pages="713--730",
	abstract="This paper deals with dense optical flow estimation from the perspective of the trade-off between quality of the estimated flow and computational cost which is required by real-world applications. We propose a fast and robust local method, denoted by eFOLKI, and describe its implementation on GPU. It leads to very high performance even on large image formats such as 4 K (3,840 {\texttimes} 2,160) resolution. In order to assess the interest of eFOLKI, we first present a comparative study with currently available GPU codes, including local and global methods, on a large set of data with ground truth. eFOLKI appears significantly faster while providing quite accurate and highly robust estimated flows. We then show, on four real-time video processing applications based on optical flow, that eFOLKI reaches the requirements both in terms of estimated flows quality and of processing rate.",
	issn="1861-8219",
	doi="10.1007/s11554-014-0423-0",
	url="http://dx.doi.org/10.1007/s11554-014-0423-0"
}

@inproceedings{Butler:ECCV:2012,
	title = {A naturalistic open source movie for optical flow evaluation},
	author = {Butler, D. J. and Wulff, J. and Stanley, G. B. and Black, M. J.},
	booktitle = {European Conf. on Computer Vision (ECCV)},
	editor = {{A. Fitzgibbon et al. (Eds.)}},
	publisher = {Springer-Verlag},
	series = {Part IV, LNCS 7577},
	month = oct,
	pages = {611--625},
	year = {2012}
}

@inproceedings{Wulff_CVPR_2017,
	title = {Optical Flow in Mostly Rigid Scenes},
	author = {Wulff, Jonas and Sevilla-Lara, Laura and Black, Michael J.},
	booktitle = {IEEE Conf. on Computer Vision and Pattern Recognition (CVPR)},
	month = jul,
	year = {2017},
	month_numeric = {7}
}

@inproceedings{Sun2010LayeredIM,
	title={Layered image motion with explicit occlusions, temporal consistency, and depth ordering},
	author={Deqing Sun and Erik B. Sudderth and Michael J. Black},
	booktitle={NIPS},
	year={2010}
}

@Article{Bro11a,
	author       = "T. Brox and J. Malik",
	title        = "Large displacement optical flow: descriptor matching in variational motion estimation",
	journal      = "IEEE Transactions on Pattern Analysis and Machine Intelligence",
	number       = "3",
	volume       = "33",
	pages        = "500-513",
	year         = "2011",
}

@INPROCEEDINGS{FOLKI,
	author={G. Le Besnerais and F. Champagnat},
	booktitle={IEEE International Conference on Image Processing 2005},
	title={Dense optical flow by iterative local window registration},
	year={2005},
	volume={1},
	number={},
	pages={I-137},
	keywords={image sequences;iterative methods;image registration;dense optical flow estimation;iterative local window registration;convergent scheme;iterative Lucas-Kanade algorithm;Image motion analysis;Nonlinear optics;Iterative algorithms;Design for disassembly;Optical filters;Spline;Interpolation;Newton method;Least squares methods;Recursive estimation},
	doi={10.1109/ICIP.2005.1529706},
	ISSN={1522-4880},
	month={Sept},}

@InProceedings{High_accuracy_brox,
	author="Brox, Thomas
	and Bruhn, Andr{\'e}s
	and Papenberg, Nils
	and Weickert, Joachim",
	editor="Pajdla, Tom{\'a}s
	and Matas, Ji{\v{r}}{\'i}",
	title="High Accuracy Optical Flow Estimation Based on a Theory for Warping",
	booktitle="Computer Vision - ECCV 2004",
	year="2004",
	publisher="Springer Berlin Heidelberg",
	address="Berlin, Heidelberg",
	pages="25--36",
	abstract="We study an energy functional for computing optical flow that combines three assumptions: a brightness constancy assumption, a gradient constancy assumption, and a discontinuity-preserving spatio-temporal smoothness constraint. In order to allow for large displacements, linearisations in the two data terms are strictly avoided. We present a consistent numerical scheme based on two nested fixed point iterations. By proving that this scheme implements a coarse-to-fine warping strategy, we give a theoretical foundation for warping which has been used on a mainly experimental basis so far. Our evaluation demonstrates that the novel method gives significantly smaller angular errors than previous techniques for optical flow estimation. We show that it is fairly insensitive to parameter variations, and we demonstrate its excellent robustness under noise.",
	isbn="978-3-540-24673-2"
}

@ARTICLE{Bouguet00pyramidalimplementation,
	author = {Jean-yves Bouguet},
	title = {Pyramidal implementation of the Lucas Kanade feature tracker},
	journal = {Intel Corporation, Microprocessor Research Labs},
	year = {2000}
}