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@inproceedings{Adam2006,
author = {Adam, Amit and Rivlin, Ehud and Shimshoni, Ilan},
booktitle = {IEEE Conference on Computer Vision and Pattern Recognition},
doi = {10.1109/CVPR.2006.256},
isbn = {0-7695-2597-0},
title = {{Robust Fragments-Based Tracking using the Integral Histogram}},
url = {http://dl.acm.org/citation.cfm?id=1153170.1153412},
year = {2006}
}
@article{Isard1998,
author = {Isard, Michael and Blake, Andrew},
doi = {10.1023/A:1008078328650},
journal = {International Journal of Computer Vision},
number = {1},
pages = {5--28},
title = {{CONDENSATION - Conditional Density Propagation for Visual Tracking.}},
url = {http://dblp.uni-trier.de/db/journals/ijcv/ijcv29.html\#IsardB98},
volume = {29},
year = {1998}
}
@techreport{Achanta2010,
author = {Achanta, Radhakrishna and Shaji, Appu and Smith, Kevin and Lucchi, Aurelien and Fua, Pascal and S\"{u}sstrunk, Sabine},
institution = {Ecole Polytechnique F\'{e}d\'{e}rale de Lausanne},
title = {{SLIC Superpixels}},
year = {2010}
}
@inproceedings{Bibby2008,
author = {Bibby, Charles and Reid, Ian D.},
booktitle = {European Conference on Computer Vision},
issn = {0302-9743},
title = {{Robust Real-Time Visual Tracking Using Pixel-Wise Posteriors}},
year = {2008}
}
@article{Kumar2006,
author = {Kumar, Sanjiv and Hebert, Martial},
doi = {10.1007/s11263-006-7007-9},
issn = {0920-5691},
journal = {International Journal of Computer Vision},
keywords = {Discriminative Classifiers,Discriminative Random Fields,Graphical Models,Image Classification,Markov Random Field,Spatial Interactions},
number = {2},
pages = {179--201},
title = {{Discriminative Random Fields}},
url = {http://dl.acm.org/citation.cfm?id=1132213.1132242},
volume = {68},
year = {2006}
}
@inproceedings{Rosten2006,
author = {Rosten, Edward and Drummond, Tom},
booktitle = {European Conference on Computer Vision},
doi = {10.1007/11744023\_34},
file = {:Users/sam/Documents/Mendeley/Library/Rosten, Drummond/Machine learning for high-speed corner detection - 2006.pdf:pdf},
month = may,
title = {{Machine learning for high-speed corner detection}},
url = {http://mi.eng.cam.ac.uk/~er258/work/rosten\_2006\_machine.pdf},
year = {2006}
}
@inproceedings{Pan2009,
author = {Pan, Qi and Reitmayr, G and Drummond, Tom},
booktitle = {British Machine Vision Conference},
title = {{ProFORMA: Probabilistic Feature-based On-line Rapid Model Acquisition}},
year = {2009}
}
@article{Black1998,
author = {Black, Michael J. and Jepson, Allan D.},
doi = {10.1023/A:1007939232436},
issn = {0920-5691},
journal = {International Journal of Computer Vision},
keywords = {eigenspace methods,gesture recognition,motion analysis,object recognition,parametric models of optical flow,robust estimation,tracking,view-based representations},
number = {1},
pages = {63--84},
title = {{EigenTracking: Robust Matching and Tracking of Articulated Objects Using a View-Based Representation}},
url = {http://dl.acm.org/citation.cfm?id=288974.288981},
volume = {26},
year = {1998}
}
@article{Aizerman1964,
author = {Aizerman, M A and Braverman, E M and Rozon\'{e}r, L I},
journal = {Automation and Remote Control},
pages = {821--837},
title = {{Theoretical Foundations of the Potential Function Method in Pattern Recognition Learning}},
volume = {25},
year = {1964}
}
@article{Boykov2004,
abstract = {After [15], [31], [19], [8], [25], [5], minimum cut/maximum flow algorithms on graphs emerged as an increasingly useful tool for exact or approximate energy minimization in low-level vision. The combinatorial optimization literature provides many min-cut/max-flow algorithms with different polynomial time complexity. Their practical efficiency, however, has to date been studied mainly outside the scope of computer vision. The goal of this paper is to provide an experimental comparison of the efficiency of min-cut/max flow algorithms for applications in vision. We compare the running times of several standard algorithms, as well as a new algorithm that we have recently developed. The algorithms we study include both Goldberg-Tarjan style "push-relabel" methods and algorithms based on Ford-Fulkerson style "augmenting paths." We benchmark these algorithms on a number of typical graphs in the contexts of image restoration, stereo, and segmentation. In many cases, our new algorithm works several times faster than any of the other methods, making near real-time performance possible. An implementation of our max-flow/min-cut algorithm is available upon request for research purposes.},
author = {Boykov, Yuri and Kolmogorov, Vladimir},
doi = {10.1109/TPAMI.2004.60},
issn = {0162-8828},
journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
keywords = {Algorithms,Artificial Intelligence,Automated,Automated: methods,Cluster Analysis,Computer-Assisted,Computer-Assisted: methods,Energy Transfer,Image Enhancement,Image Enhancement: methods,Image Interpretation,Imaging,Information Storage and Retrieval,Information Storage and Retrieval: methods,Pattern Recognition,Photogrammetry,Photogrammetry: methods,Photography,Photography: methods,Reproducibility of Results,Sensitivity and Specificity,Three-Dimensional,Three-Dimensional: methods},
number = {9},
pages = {1124--37},
pmid = {15742889},
title = {{An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision.}},
url = {http://dl.acm.org/citation.cfm?id=1018034.1018355},
volume = {26},
year = {2004}
}
@article{Babenko2011,
abstract = {In this paper we address the problem of tracking an object in a video given its location in the first frame and no other information. Recently, a class of tracking techniques called "tracking by detection" has been shown to give promising results at real-time speeds. These methods train a discriminative classifier in an online manner to separate the object from the background. This classifier bootstraps itself by using the current tracker state to extract positive and negative examples from the current frame. Slight inaccuracies in the tracker can therefore lead to incorrectly labeled training examples, which degrade the classifier and can cause further drift. In this paper we show that using Multiple Instance Learning (MIL) instead of traditional supervised learning avoids these problems, and can therefore lead to a more robust tracker with fewer parameter tweaks. We propose a novel online MIL algorithm for object tracking that achieves superior results with real-time performance. We present thorough experimental results (both qualitative and quantitative) on a number of challenging video clips.},
author = {Babenko, Boris and Yang, Ming-Hsuan and Belongie, Serge},
doi = {10.1109/TPAMI.2010.226},
issn = {1939-3539},
journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
keywords = {Visual Tracking,multiple instance learning,online boosting.},
number = {8},
pages = {1619--1632},
pmid = {21173445},
title = {{Visual Tracking with Online Multiple Instance Learning}},
url = {http://dl.acm.org/citation.cfm?id=2006853.2006991},
volume = {33},
year = {2011}
}
@article{Mikolajczyk2004,
author = {Mikolajczyk, Krystian and Schmid, Cordelia},
doi = {10.1023/B:VISI.0000027790.02288.f2},
issn = {0920-5691},
journal = {International Journal of Computer Vision},
keywords = {affine invariance,interest points,local features,matching,recognition,scale invariance},
number = {1},
pages = {63--86},
title = {{Scale \& Affine Invariant Interest Point Detectors}},
url = {http://dl.acm.org/citation.cfm?id=990376.990402},
volume = {60},
year = {2004}
}
@techreport{Xu2010,
author = {Xu, Wei},
institution = {Stony Brook University},
title = {{Towards Optimal One Pass Large Scale Learning with Averaged Stochastic Gradient Descent}},
year = {2010}
}
@inproceedings{Saffari2010a,
author = {Saffari, Amir and Godec, Martin and Pock, Thomas and Leistner, Christian and Bischof, Horst},
booktitle = {IEEE Conference on Computer Vision and Pattern Recognition},
keywords = {boosting,online},
title = {{Online Multi-Class LPBoost}},
year = {2010}
}
@article{Lepetit2006,
abstract = {In many 3D object-detection and pose-estimation problems, runtime performance is of critical importance. However, there usually is time to train the system, which we will show to be very useful. Assuming that several registered images of the target object are available, we developed a keypoint-based approach that is effective in this context by formulating wide-baseline matching of keypoints extracted from the input images to those found in the model images as a classification problem. This shifts much of the computational burden to a training phase, without sacrificing recognition performance. As a result, the resulting algorithm is robust, accurate, and fast-enough for frame-rate performance. This reduction in runtime computational complexity is our first contribution. Our second contribution is to show that, in this context, a simple and fast keypoint detector suffices to support detection and tracking even under large perspective and scale variations. While earlier methods require a detector that can be expected to produce very repeatable results, in general, which usually is very time-consuming, we simply find the most repeatable object keypoints for the specific target object during the training phase. We have incorporated these ideas into a real-time system that detects planar, nonplanar, and deformable objects. It then estimates the pose of the rigid ones and the deformations of the others.},
author = {Lepetit, Vincent and Fua, Pascal},
doi = {10.1109/TPAMI.2006.188},
file = {:Users/sam/Documents/Mendeley/Library/Lepetit, Fua/Keypoint Recognition Using Randomized Trees - 2006.pdf:pdf},
issn = {0162-8828},
journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
keywords = {Algorithms,Artificial Intelligence,Automated,Automated: methods,Computer Simulation,Computer-Assisted,Computer-Assisted: methods,Data Interpretation,Image Enhancement,Image Enhancement: methods,Image Interpretation,Imaging,Information Storage and Retrieval,Information Storage and Retrieval: methods,Models,Pattern Recognition,Statistical,Three-Dimensional,Three-Dimensional: methods},
number = {9},
pages = {1465--79},
pmid = {16929732},
publisher = {Published by the IEEE Computer Society},
title = {{Keypoint Recognition Using Randomized Trees}},
url = {http://www.computer.org/portal/web/csdl/doi/10.1109/TPAMI.2006.188},
volume = {28},
year = {2006}
}
@inproceedings{Taskar2003,
author = {Taskar, Ben and Guestrin, Carlos and Koller, Daphne},
booktitle = {Neural Information Processing Systems},
title = {{Max-Margin Markov Networks}},
year = {2003}
}
@inproceedings{Williams2007,
author = {Williams, B and Klein, Georg and Reid, Ian D},
booktitle = {IEEE International Conference on Computer Vision},
title = {{Real-Time SLAM Relocalisation}},
year = {2007}
}
@article{Schmid1997,
author = {Schmid, Cordelia and Mohr, R},
doi = {10.1109/34.589215},
issn = {01628828},
journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
keywords = {Image retrieval,graylevel invariants,image indexing,interest points.,matching},
number = {5},
pages = {530--535},
title = {{Local grayvalue invariants for image retrieval}},
url = {http://dl.acm.org/citation.cfm?id=252862.252900},
volume = {19},
year = {1997}
}
@inproceedings{Taylor2009,
author = {Taylor, Simon and Drummond, Tom},
booktitle = {British Machine Vision Conference},
file = {:Users/sam/Documents/Mendeley/Library/Taylor, Drummond/Multiple Target Localisation at over 100 FPS - 2009.pdf:pdf},
title = {{Multiple Target Localisation at over 100 FPS}},
year = {2009}
}
@article{Chang2011,
author = {Chang, Chih-Chung and Lin, Chih-Jen},
doi = {10.1145/1961189.1961199},
issn = {2157-6904},
journal = {ACM Transactions on Intelligent Systems and Technology},
keywords = {Classification LIBSVM optimization regression supp},
number = {3},
pages = {27},
title = {{LIBSVM: A Library for Support Vector Machines}},
url = {http://dl.acm.org/citation.cfm?id=1961189.1961199},
volume = {2},
year = {2011}
}
@inproceedings{Seitz2006,
author = {Seitz, S.M. and Curless, B. and Diebel, J. and Scharstein, D. and Szeliski, R.},
booktitle = {IEEE Conference on Computer Vision and Pattern Recognition},
isbn = {0-7695-2597-0},
title = {{A Comparison and Evaluation of Multi-View Stereo Reconstruction Algorithms}},
url = {http://dl.acm.org/citation.cfm?id=1153170.1153518},
year = {2006}
}
@article{Hager1998,
author = {Hager, G.D. and Belhumeur, P.N.},
doi = {10.1109/34.722606},
issn = {01628828},
journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
keywords = {Cameras,Computational geometry,Light sources,Lighting,Motion estimation,Parametric statistics,Real time systems,Robustness,Target tracking,Video sequences,computer vision,geometric distortions,illumination,image regions,image sequences,machine vision,object tracking,optical tracking,parametric models,region tracking,statistical analysis,statistical outliers},
language = {English},
number = {10},
pages = {1025--1039},
title = {{Efficient Region Tracking with Parametric Models of Geometry and Illumination}},
url = {http://ieeexplore.ieee.org/articleDetails.jsp?arnumber=722606\&contentType=Journals+\%26+Magazines},
volume = {20},
year = {1998}
}
@inproceedings{Grabner2007,
author = {Grabner, Michael and Grabner, Helmut and Bischof, Horst},
booktitle = {IEEE Conference on Computer Vision and Pattern Recognition},
doi = {10.1109/CVPR.2007.382995},
file = {:Users/sam/Documents/Mendeley/Library/Grabner, Grabner, Bischof/Learning Features for Tracking - 2007.pdf:pdf},
isbn = {1-4244-1179-3},
title = {{Learning Features for Tracking}},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=4270020},
year = {2007}
}
@article{Ramanan2007,
abstract = {An open vision problem is to automatically track the articulations of people from a video sequence. This problem is difficult because one needs to determine both the number of people in each frame and estimate their configurations. But, finding people and localizing their limbs is hard because people can move fast and unpredictably, can appear in a variety of poses and clothes, and are often surrounded by limb-like clutter. We develop a completely automatic system that works in two stages; it first builds a model of appearance of each person in a video and then it tracks by detecting those models in each frame ("tracking by model-building and detection"). We develop two algorithms that build models; one bottom-up approach groups together candidate body parts found throughout a sequence. We also describe a top-down approach that automatically builds people-models by detecting convenient key poses within a sequence. We finally show that building a discriminative model of appearance is quite helpful since it exploits structure in a background (without background-subtraction). We demonstrate the resulting tracker on hundreds of thousands of frames of unscripted indoor and outdoor activity, a feature-length film ("Run Lola Run"), and legacy sports footage (from the 2002 World Series and 1998 Winter Olympics). Experiments suggest that our system 1) can count distinct individuals, 2) can identify and track them, 3) can recover when it loses track, for example, if individuals are occluded or briefly leave the view, 4) can identify body configuration accurately, and 5) is not dependent on particular models of human motion.},
author = {Ramanan, Deva and Forsyth, David A and Zisserman, Andrew},
doi = {10.1109/TPAMI.2007.22},
issn = {0162-8828},
journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
keywords = {Algorithms,Artificial Intelligence,Automated,Automated: methods,Computer-Assisted,Computer-Assisted: methods,Humans,Image Enhancement,Image Enhancement: methods,Image Interpretation,Imaging,Information Storage and Retrieval,Information Storage and Retrieval: methods,Movement,Pattern Recognition,Reproducibility of Results,Sensitivity and Specificity,Three-Dimensional,Three-Dimensional: methods,Video Recording,Video Recording: methods,Whole Body Imaging,Whole Body Imaging: methods},
number = {1},
pages = {65--81},
pmid = {17108384},
title = {{Tracking People by Learning Their Appearance}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/17108384},
volume = {29},
year = {2007}
}
@article{Ozuysal2010,
abstract = {While feature point recognition is a key component of modern approaches to object detection, existing approaches require computationally expensive patch preprocessing to handle perspective distortion. In this paper, we show that formulating the problem in a naive Bayesian classification framework makes such preprocessing unnecessary and produces an algorithm that is simple, efficient, and robust. Furthermore, it scales well as the number of classes grows. To recognize the patches surrounding keypoints, our classifier uses hundreds of simple binary features and models class posterior probabilities. We make the problem computationally tractable by assuming independence between arbitrary sets of features. Even though this is not strictly true, we demonstrate that our classifier nevertheless performs remarkably well on image data sets containing very significant perspective changes.},
author = {\"{O}zuysal, Mustafa and Calonder, Michael and Lepetit, Vincent and Fua, Pascal},
doi = {10.1109/TPAMI.2009.23},
file = {:Users/sam/Documents/Mendeley/Library/\"{O}zuysal et al/Fast Keypoint Recognition Using Random Ferns - 2010.pdf:pdf},
issn = {1939-3539},
journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
number = {3},
pages = {448--61},
pmid = {20075471},
publisher = {Published by the IEEE Computer Society},
title = {{Fast Keypoint Recognition Using Random Ferns}},
url = {http://www.computer.org/portal/web/csdl/doi/10.1109/TPAMI.2009.23},
volume = {32},
year = {2010}
}
@inproceedings{Boykov2001a,
author = {Boykov, Yuri and Jolly, Marie-Pierre},
booktitle = {IEEE International Conference on Computer Vision},
title = {{Interactive Graph Cuts for Optimal Boundary \& Region Segmentation of Objects in N-D Images}},
year = {2001}
}
@inproceedings{Leistner2009,
author = {Leistner, Christian and Saffari, Amir and Roth, Peter M and Bischof, Horst},
booktitle = {Proc. ICCV-OLCV},
keywords = {boosting},
title = {{On Robustness of On-line Boosting - A Competitive Study}},
year = {2009}
}
@inproceedings{Scholkopf2001,
author = {Sch\"{o}lkopf, Bernhard and Herbrich, Ralf and Smola, Alex J},
booktitle = {Conference on Computational Learning Theory},
title = {{A Generalized Representer Theorem}},
year = {2001}
}
@article{Joachims2009,
author = {Joachims, Thorsten and Finley, Thomas and Yu, Chun-Nam John},
doi = {10.1007/s10994-009-5108-8},
issn = {0885-6125},
journal = {Machine Learning},
keywords = {Structural SVMs,Structured output prediction,Support vector machines,Training algorithms},
number = {1},
pages = {27--59},
title = {{Cutting-Plane Training of Structural SVMs}},
url = {http://dl.acm.org/citation.cfm?id=1612990.1613006},
volume = {77},
year = {2009}
}
@inproceedings{Muja2009,
author = {Muja, Marius and Lowe, David G.},
booktitle = {International Conference on Computer Vision Theory and Applications},
title = {{Fast Approximate Nearest Neighbors with Automatic Algorithm Configuration}},
year = {2009}
}
@inproceedings{Dalal2005,
author = {Dalal, Navneet and Triggs, Bill},
booktitle = {IEEE Conference on Computer Vision and Pattern Recognition},
doi = {10.1109/CVPR.2005.177},
isbn = {0-7695-2372-2},
title = {{Histograms of Oriented Gradients for Human Detection}},
url = {http://dl.acm.org/citation.cfm?id=1068507.1069007},
year = {2005}
}
@article{Rother2004,
author = {Rother, Carsten and Kolmogorov, Vladimir and Blake, Andrew},
doi = {10.1145/1015706.1015720},
issn = {07300301},
journal = {ACM Transactions on Graphics},
keywords = {Alpha Matting,Foreground extraction,Graph Cuts,Image Editing,Interactive Image Segmentation},
number = {3},
pages = {309},
title = {{"GrabCut": Interactive Foreground Extraction Using Iterated Graph Cuts}},
url = {http://dl.acm.org/citation.cfm?id=1015706.1015720},
volume = {23},
year = {2004}
}
@inproceedings{Rublee2011,
author = {Rublee, Ethan and Rabaud, Vincent and Konolige, Kurt and Bradski, Gary},
booktitle = {IEEE International Conference on Computer Vision},
title = {{ORB: an Efﬁcient Alternative to SIFT or SURF}},
year = {2011}
}
@inproceedings{Klein2007,
author = {Klein, Georg and Murray, David},
booktitle = {IEEE International Symposium on Mixed and Augmented Reality},
doi = {10.1109/ISMAR.2007.4538852},
isbn = {978-1-4244-1749-0},
month = nov,
title = {{Parallel Tracking and Mapping for Small AR Workspaces}},
url = {http://dl.acm.org/citation.cfm?id=1514339.1514363},
year = {2007}
}
@techreport{Hammersley1971,
author = {Hammersley, J M and Clifford, P},
booktitle = {Unpublished},
institution = {Unpublished},
publisher = {Unpublished},
title = {{Markov Fields on Finite Graphs and Lattices}},
year = {1971}
}
@inproceedings{Li2009,
author = {Li, Hongdong},
booktitle = {IEEE International Conference on Computer Vision},
doi = {10.1109/ICCV.2009.5459398},
file = {:Users/sam/Documents/Mendeley/Library/Li/Consensus Set Maximization with Guaranteed Global Optimality for Robust Geometry Estimation - 2009.pdf:pdf},
isbn = {978-1-4244-4420-5},
title = {{Consensus Set Maximization with Guaranteed Global Optimality for Robust Geometry Estimation}},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5459398},
year = {2009}
}
@article{Viola2004,
author = {Viola, Paul and Jones, Michael J},
issn = {0920-5691},
journal = {International Journal of Computer Vision},
number = {2},
pages = {137--154},
publisher = {Springer Netherlands},
title = {{Robust Real-Time Face Detection}},
url = {http://dx.doi.org/10.1023/B:VISI.0000013087.49260.fb},
volume = {57},
year = {2004}
}
@inbook{Platt1999,
address = {Cambridge, MA, USA},
author = {Platt, John C},
booktitle = {Advances in Kernel Methods},
isbn = {0-262-19416-3},
pages = {185--208},
publisher = {MIT Press},
title = {{Fast Training of Support Vector Machines Using Sequential Minimal Optimization}},
url = {http://portal.acm.org/citation.cfm?id=299094.299105},
year = {1999}
}
@inproceedings{Bordes2007,
author = {Bordes, Antoine and Bottou, L\'{e}on and Gallinari, Patrick and Weston, Jason},
booktitle = {International Conference on Machine Learning},
title = {{Solving multiclass support vector machines with LaRank}},
year = {2007}
}
@article{Halevy2009,
author = {Halevy, Alon and Norvig, Peter and Pereira, Fernando},
doi = {10.1109/MIS.2009.36},
issn = {1541-1672},
journal = {IEEE Intelligent Systems},
keywords = {Broadcasting,Brown Corpus,Data mining,English words,Frequency estimation,Humans,Internet,Natural language processing,Semantic Web,Speech recognition,Tagging,Videos,Web pages,Web-derived corpora,data handling,data unreasonable effectiveness,frequency counts,grammatical errors,hand-corrected part-of-speech tags,incomplete sentences,machine learning,spelling errors,trillion-word corpus,unfiltered Web pages,very large data bases},
language = {English},
number = {2},
pages = {8--12},
title = {{The Unreasonable Effectiveness of Data}},
url = {http://ieeexplore.ieee.org/articleDetails.jsp?arnumber=4804817\&contentType=Journals+\%26+Magazines},
volume = {24},
year = {2009}
}
@article{Baker2004,
author = {Baker, Simon and Matthews, Iain},
doi = {10.1023/B:VISI.0000011205.11775.fd},
issn = {0920-5691},
journal = {International Journal of Computer Vision},
keywords = {Gauss-Newton,Levenberg-Marquardt,Lucas-Kanade,Newton,a unifying framework,additive vs. compositional algorithms,efficiency,forwards vs. inverse algorithms,image alignment,steepest descent,the inverse compositional algorithm},
number = {3},
pages = {221--255},
title = {{Lucas-Kanade 20 Years On: A Unifying Framework}},
url = {http://dl.acm.org/citation.cfm?id=964568.964604},
volume = {56},
year = {2004}
}
@article{Comaniciu2003,
author = {Comaniciu, D. and Ramesh, V. and Meer, P.},
doi = {10.1109/TPAMI.2003.1195991},
issn = {0162-8828},
journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
keywords = {Bhattacharyya coefficient,Nonrigid object tracking,face tracking.,spatially-smooth similarity function,target localization and representation},
number = {5},
pages = {564--577},
title = {{Kernel-based object tracking}},
url = {http://dl.acm.org/citation.cfm?id=776753.776799},
volume = {25},
year = {2003}
}
@inproceedings{Bordes2008,
author = {Bordes, Antoine and Usunier, Nicolas and Bottou, L\'{e}on},
booktitle = {Proc. ECML-PKDD},
title = {{Sequence Labelling SVMs Trained in One Pass}},
year = {2008}
}
@article{Zhang1999,
author = {Zhang, Ruo and Tsai, Ping-Sing and Cryer, James Edwin and Shah, Mubarak},
doi = {10.1109/34.784284},
issn = {01628828},
journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
keywords = {Lambertian model,Shape from shading,analysis of algorithms,survey of shape from shading algorithms.},
number = {8},
pages = {690--706},
title = {{Shape from Shading: A Survey}},
url = {http://dl.acm.org/citation.cfm?id=314270.314272},
volume = {21},
year = {1999}
}
@article{Lindeberg1998,
author = {Lindeberg, Tony},
doi = {10.1023/A:1008045108935},
issn = {0920-5691},
journal = {International Journal of Computer Vision},
keywords = {Gaussian derivative,blob detection,computer vision,corner detection,feature detection,frequency estimation,multi-scale representation,normalized derivative,scale,scale selection,scale-space},
number = {2},
pages = {79--116},
title = {{Feature Detection with Automatic Scale Selection}},
url = {http://dl.acm.org/citation.cfm?id=305297.305298},
volume = {30},
year = {1998}
}
@inproceedings{Yang2005,
author = {Yang, Changjiang and Duraiswami, Ramani and Davis, Larry S.},
booktitle = {IEEE Conference on Computer Vision and Pattern Recognition},
doi = {10.1109/CVPR.2005.139},
isbn = {0-7695-2372-2},
keywords = {Improved fast Gauss transform,Mean-shift algorithm,Similarity measure,Spatial-feature tracking},
title = {{Efficient Mean-Shift Tracking via a New Similarity Measure}},
url = {http://dl.acm.org/citation.cfm?id=1068507.1068913},
year = {2005}
}
@article{Fukunaga1975,
author = {Fukunaga, K. and Hostetler, L.},
doi = {10.1109/TIT.1975.1055330},
issn = {0018-9448},
journal = {IEEE Transactions on Information Theory},
number = {1},
pages = {32--40},
title = {{The Estimation of the Gradient of a Density Function, with Applications in Pattern Recognition}},
url = {http://dl.acm.org/citation.cfm?id=2263309.2268796},
volume = {21},
year = {1975}
}
@inproceedings{Grabner2008,
author = {Grabner, H and Leistner, C and Bischof, H},
booktitle = {European Conference on Computer Vision},
title = {{Semi-Supervised On-Line Boosting for Robust Tracking}},
year = {2008}
}
@inproceedings{Ke2004,
author = {Ke, Yan and Sukthankar, Rahul},
booktitle = {IEEE Conference on Computer Vision and Pattern Recognition},
title = {{PCA-SIFT: A More Distinctive Representation for Local Image Descriptors}},
url = {http://dl.acm.org/citation.cfm?id=1896300.1896374},
year = {2004}
}
@inproceedings{Tuytelaars2007,
author = {Tuytelaars, Tinne and Schmid, Cordelia},
booktitle = {IEEE International Conference on Computer Vision},
title = {{Vector Quantizing Feature Space with a Regular Lattice}},
year = {2007}
}
@inproceedings{Crammer2003,
author = {Crammer, Koby and Kandola, Jaz and Holloway, Royal and Singer, Yoram},
booktitle = {Neural Information Processing Systems},
title = {{Online Classification on a Budget}},
year = {2003}
}
@inproceedings{Calonder2010,
author = {Calonder, Michael and Lepetit, Vincent and Strecha, Christoph and Fua, Pascal},
booktitle = {European Conference on Computer Vision},
file = {:Users/sam/Documents/Mendeley/Library/Calonder et al/BRIEF Binary Robust Independent Elementary Features - 2010.pdf:pdf},
title = {{BRIEF: Binary Robust Independent Elementary Features}},
url = {http://www.springerlink.com/index/H8H1824827036042.pdf},
year = {2010}
}
@inproceedings{Blaschko2008,
author = {Blaschko, Matthew B. and Lampert, Christoph H.},
booktitle = {European Conference on Computer Vision},
issn = {0302-9743},
title = {{Learning to Localize Objects with Structured Output Regression}},
year = {2008}
}
@book{Lindeberg1994,
author = {Lindeberg, Tony},
publisher = {Kluwer Academic Publishers},
title = {{Scale-Space Theory in Computer Vision}},
year = {1994}
}
@article{Isack2011,
author = {Isack, Hossam and Boykov, Yuri},
doi = {10.1007/s11263-011-0474-7},
issn = {0920-5691},
journal = {International Journal of Computer Vision},
keywords = {Geometric models,Graph cuts,Labeling,Parameter estimation,Sampling,$\alpha$-Expansion},
number = {2},
pages = {123--147},
title = {{Energy-Based Geometric Multi-Model Fitting}},
url = {http://dl.acm.org/citation.cfm?id=2158827.2158836},
volume = {97},
year = {2011}
}
@article{Hoiem2007,
author = {Hoiem, Derek and Efros, Alexei A. and Hebert, Martial},
doi = {10.1007/s11263-006-0031-y},
issn = {0920-5691},
journal = {International Journal of Computer Vision},
keywords = {context,geometric context,image understanding,model-driven segmentation,multiple segmentations,object detection,object recognition,scene understanding,spatial layout,surface layout},
number = {1},
pages = {151--172},
title = {{Recovering Surface Layout from an Image}},
url = {http://dl.acm.org/citation.cfm?id=1285519.1285528},
volume = {75},
year = {2007}
}
@article{Boykov2001,
author = {Boykov, Y. and Veksler, O. and Zabih, R.},
doi = {10.1109/34.969114},
issn = {01628828},
journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
keywords = {Energy minimization,Markov Random Fields,Potts model,early vision,graph algorithms,image restoration,maximum flow,minimum cut,motion,multiway cut.,stereo},
number = {11},
pages = {1222--1239},
title = {{Fast Approximate Energy Minimization via Graph Cuts}},
url = {http://dl.acm.org/citation.cfm?id=505471.505473},
volume = {23},
year = {2001}
}
@inproceedings{Harris1988,
author = {Harris, C and Stephens, M},
booktitle = {Proc. 4th Alvey Vision Conference},
title = {{A Combined Corner and Edge Detector}},
year = {1988}
}
@article{Avidan2004,
abstract = {Support Vector Tracking (SVT) integrates the Support Vector Machine (SVM) classifier into an optic-flow-based tracker. Instead of minimizing an intensity difference function between successive frames, SVT maximizes the SVM classification score. To account for large motions between successive frames, we build pyramids from the support vectors and use a coarse-to-fine approach in the classification stage. We show results of using SVT for vehicle tracking in image sequences.},
author = {Avidan, Shai},
doi = {10.1109/TPAMI.2004.53},
issn = {0162-8828},
journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
keywords = {Algorithms,Artificial Intelligence,Automated,Automated: methods,Computer Graphics,Computer-Assisted,Computer-Assisted: methods,Image Enhancement,Image Enhancement: methods,Image Interpretation,Imaging,Information Storage and Retrieval,Information Storage and Retrieval: methods,Movement,Numerical Analysis,Pattern Recognition,Reproducibility of Results,Sensitivity and Specificity,Signal Processing,Subtraction Technique,Three-Dimensional,Three-Dimensional: methods,Video Recording,Video Recording: methods},
number = {8},
pages = {1064--72},
pmid = {15641735},
title = {{Support vector tracking.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/15641735},
volume = {26},
year = {2004}
}
@inproceedings{Saffari2010,
author = {Saffari, Amir and Leistner, Christian and Godec, Martin and Bischof, Horst},
booktitle = {European Conference on Computer Vision},
title = {{Robust multi-view boosting with priors}},
year = {2010}
}
@techreport{Bouguet1999,
author = {Bouguet, Jean-Yves},
institution = {Intel Corporation Microprocessor Research Labs},
title = {{Pyramidal Implementation of the Lucas Kanade Feature Tracker}},
year = {1999}
}
@inproceedings{Chum2005,
abstract = {A new robust matching method is proposed. The progressive sample consensus (PROSAC) algorithm exploits the linear ordering defined on the set of correspondences by a similarity function used in establishing tentative correspondences. Unlike RANSAC, which treats all correspondences equally and draws random samples uniformly from the full set, PROSAC samples are drawn from progressively larger sets of top-ranked correspondences. Under the mild assumption that the similarity measure predicts correctness of a match better than random guessing, we show that PROSAC achieves large computational savings. Experiments demonstrate it is often significantly faster (up to more than hundred times) than RANSAC. For the derived size of the sampled set of correspondences as a function of the number of samples already drawn, PROSAC converges towards RANSAC in the worst case. The power of the method is demonstrated on wide-baseline matching problems.},
author = {Chum, Ondrej and Matas, Jiri},
booktitle = {IEEE Conference on Computer Vision and Pattern Recognition},
doi = {10.1109/CVPR.2005.221},
file = {:Users/sam/Documents/Mendeley/Library/Chum, Matas/Matching with PROSAC — Progressive Sample Consensus - 2005.pdf:pdf},
isbn = {0-7695-2372-2},
title = {{Matching with PROSAC — Progressive Sample Consensus}},
url = {http://ieeexplore.ieee.org/xpl/freeabs\_all.jsp?arnumber=1467271},
year = {2005}
}
@article{Kalal2011,
abstract = {This paper investigates long-term tracking of unknown objects in a video stream. The object is defined by its location and extent in a single frame. In every frame that follows, the task is to determine the object's location and extent or indicate that the object is not present. We propose a novel tracking framework (TLD) that explicitly decomposes the long-term tracking task into tracking, learning and detection. The tracker follows the object from frame to frame. The detector localizes all appearances that have been observed so far and corrects the tracker if necessary. The learning estimates detector's errors and updates it to avoid these errors in the future. We study how to identify detector's errors and learn from them. We develop a novel learning method (P-N learning) which estimates the errors by a pair of "experts'': (i) P-expert estimates missed detections, and (ii) N-expert estimates false alarms. The learning process is modeled as a discrete dynamical system and the conditions under which the learning guarantees improvement are found. We describe our real-time implementation of the TLD framework and the P-N learning. We carry out an extensive quantitative evaluation which shows a significant improvement over state-of-the-art approaches.},
author = {Kalal, Zdenek and Mikolajczyk, Krystian and Matas, Jiri},
doi = {10.1109/TPAMI.2011.239},
issn = {1939-3539},
journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
keywords = {Long-term tracking,bootstrapping,learning from video,real time,semi-supervised learning.},
number = {7},
pages = {1409--1422},
pmid = {22156098},
title = {{Tracking-Learning-Detection}},
url = {http://dl.acm.org/citation.cfm?id=2225045.2225082},
volume = {34},
year = {2011}
}
@inproceedings{Elgammal2003,
author = {Elgammal, Ahmed and Duraiswami, Ramani and Davis, Larry S.},
booktitle = {IEEE Conference on Computer Vision and Pattern Recognition},
isbn = {0-7695-1900-8, 978-0-7695-1900-5},
title = {{Probabilistic Tracking in Joint Feature-Spatial Spaces}},
url = {http://dl.acm.org/citation.cfm?id=1965841.1965943},
year = {2003}
}
@article{Jepson2003,
author = {Jepson, A. D. and Fleet, D. J. and El-Maraghi, T. F.},
doi = {10.1109/TPAMI.2003.1233903},
issn = {0162-8828},
journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
number = {10},
pages = {1296--1311},
title = {{Robust Online Appearance Models for Visual Tracking}},
url = {http://dl.acm.org/citation.cfm?id=2286437.2286507},
volume = {25},
year = {2003}
}
@article{Shalev-Shwartz2010,
abstract = {We describe and analyze a simple and effective stochastic sub-gradient descent algorithm for solving the optimization problem cast by Support Vector Machines (SVM). We prove that the number of iterations required to obtain a solution of accuracy \$\$\{$\backslash$epsilon\}\$\$ is \$\$\{$\backslash$tilde\{O\}(1 / $\backslash$epsilon)\}\$\$, where each iteration operates on a single training example. In contrast, previous analyses of stochastic gradient descent methods for SVMs require \$\$\{$\backslash$Omega(1 / $\backslash$epsilon\^{}2)\}\$\$ iterations. As in previously devised SVM solvers, the number of iterations also scales linearly with 1/ $\lambda$ , where $\lambda$ is the regularization parameter of SVM. For a linear kernel, the total run-time of our method is \$\$\{$\backslash$tilde\{O\}(d/($\backslash$lambda $\backslash$epsilon))\}\$\$, where d is a bound on the number of non-zero features in each example. Since the run-time does not depend directly on the size of the training set, the resulting algorithm is especially suited for learning from large datasets. Our approach also extends to non-linear kernels while working solely on the primal objective function, though in this case the runtime does depend linearly on the training set size. Our algorithm is particularly well suited for large text classification problems, where we demonstrate an order-of-magnitude speedup over previous SVM learning methods.},
author = {Shalev-Shwartz, Shai and Singer, Yoram and Srebro, Nathan and Cotter, Andrew},
doi = {10.1007/s10107-010-0420-4},
file = {:Users/sam/Documents/Mendeley/Library/Shalev-Shwartz et al/Pegasos Primal Estimated sub-GrAdient SOlver for SVM - 2010.pdf:pdf},
issn = {0025-5610},
journal = {Mathematical Programming},
keywords = {Mathematics and Statistics},
number = {1},
pages = {3--30},
publisher = {Springer Berlin / Heidelberg},
title = {{Pegasos: Primal Estimated sub-GrAdient SOlver for SVM}},
url = {http://www.springerlink.com/content/h7554g1n611m3813/},
volume = {127},
year = {2010}
}
@inproceedings{Nister2006,
author = {Nister, David and Stewenius, Henrik},
booktitle = {IEEE Conference on Computer Vision and Pattern Recognition},
title = {{Scalable Recognition with a Vocabulary Tree}},
year = {2006}
}
@inproceedings{Gallup2010,
author = {Gallup, David and Frahm, Jan-Michael and Pollefeys, Marc},
booktitle = {IEEE Conference on Computer Vision and Pattern Recognition},
title = {{Piecewise Planar and Non-Planar Stereo for Urban Scene Reconstruction}},
year = {2010}
}
@inproceedings{Wang2010,
author = {Wang, Zhuang and Crammer, Koby and Vucetic, Slobodan},
booktitle = {International Conference on Machine Learning},
keywords = {svm},
title = {{Multi-Class Pegasos on a Budget}},
url = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.170.2394},
year = {2010}
}
@article{Gonen2011,
author = {G\"{o}nen, Mehmet and Alpaydın, Ethem},
issn = {1532-4435},
journal = {Journal of Machine Learning Research},
pages = {2211--2268},
title = {{Multiple Kernel Learning Algorithms}},
url = {http://dl.acm.org/citation.cfm?id=1953048.2021071},
volume = {12},
year = {2011}
}
@book{Pearl1988,
author = {Pearl, Judea},
isbn = {0-934613-73-7},
publisher = {Morgan Kaufmann},
title = {{Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference}},
url = {http://dl.acm.org/citation.cfm?id=52121},
year = {1988}
}
@inproceedings{Hare2011,
author = {Hare, Sam and Saffari, Amir and Torr, Philip H. S.},
booktitle = {IEEE International Conference on Computer Vision},
title = {{Struck: Structured Output Tracking with Kernels}},
year = {2011}
}
@article{Felzenszwalb2010,
address = {Los Alamitos, CA, USA},
author = {Felzenszwalb, P F and Girshick, R B and McAllester, D and Ramanan, D},
doi = {10.1109/TPAMI.2009.167},
issn = {0162-8828},
journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
number = {9},
pages = {1627--1645},
publisher = {IEEE Computer Society},
title = {{Object Detection with Discriminatively Trained Part-Based Models}},
url = {http://dx.doi.org/10.1109/TPAMI.2009.167},
volume = {32},
year = {2010}
}
@inproceedings{Shotton2008,
author = {Shotton, Jamie and Johnson, Matthew and Cipolla, Roberto},
booktitle = {IEEE Conference on Computer Vision and Pattern Recognition},
title = {{Semantic Texton Forests for Image Categorization and Segmentation}},
year = {2008}
}
@incollection{LeCun1998,
annote = {10.1007/3-540-49430-8\_2},
author = {LeCun, Yann and Bottou, Leon and Orr, Genevieve and M\"{u}ller, Klaus},
booktitle = {Neural Networks: Tricks of the Trade},
editor = {Orr, Genevieve and M\"{u}ller, Klaus-Robert},
file = {:Users/sam/Documents/Mendeley/Library/LeCun et al/Efficient BackProp - 1998.pdf:pdf},
isbn = {978-3-540-65311-0},
pages = {546},
publisher = {Springer Berlin / Heidelberg},
series = {Lecture Notes in Computer Science},
title = {{Efficient BackProp}},
url = {http://dx.doi.org/10.1007/3-540-49430-8\_2},
volume = {1524},
year = {1998}
}
@phdthesis{Calonder2010a,
author = {Calonder, Michael},
school = {Ecole Polytechnique F\'{e}d\'{e}rale de Lausanne},
title = {{Robust, High-Speed Interest Point Matching for Real-Time Applications}},
year = {2010}
}
@techreport{Cannons2008,
author = {Cannons, Kevin},
institution = {York University},
title = {{A Review of Visual Tracking}},
url = {http://www.cse.yorku.ca/techreports/2008/?abstract=CSE-2008-07},
year = {2008}
}
@article{Shotton2007,
author = {Shotton, Jamie and Winn, John and Rother, Carsten and Criminisi, Antonio},
doi = {10.1007/s11263-007-0109-1},
issn = {0920-5691},
journal = {International Journal of Computer Vision},
keywords = {Boosting,Conditional random field,Context,Image understanding,Layout,Object recognition,Piecewise training,Segmentation,Semantic image segmentation,Textons,Texture},
number = {1},
pages = {2--23},
title = {{TextonBoost for Image Understanding: Multi-Class Object Recognition and Segmentation by Jointly Modeling Texture, Layout, and Context}},
url = {http://dl.acm.org/citation.cfm?id=1487450.1487516},
volume = {81},
year = {2007}
}
@inproceedings{Philbin2007,
author = {Philbin, James and Chum, Ondrej and Isard, Michael and Sivic, Josef and Zisserman, Andrew},
booktitle = {IEEE Conference on Computer Vision and Pattern Recognition},
title = {{Object Retrieval with Large Vocabularies and Fast Spatial Matching}},
year = {2007}
}
@article{Matthews2004,
abstract = {Template tracking dates back to the 1981 Lucas-Kanade algorithm. One question that has received very little attention, however, is how to update the template so that it remains a good model of the tracked object. We propose a template update algorithm that avoids the "drifting" inherent in the naive algorithm.},
author = {Matthews, Iain and Ishikawa, Takahiro and Baker, Simon},
doi = {10.1109/TPAMI.2004.16},
issn = {0162-8828},
journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
keywords = {Algorithms,Artificial Intelligence,Automated,Automated: methods,Computer-Assisted,Computer-Assisted: methods,Image Enhancement,Image Enhancement: methods,Image Interpretation,Pattern Recognition,Reproducibility of Results,Sensitivity and Specificity,Video Recording,Video Recording: methods},
number = {6},
pages = {810--5},
pmid = {18579941},
title = {{The template update problem.}},
url = {http://dl.acm.org/citation.cfm?id=987526.987632},
volume = {26},
year = {2004}
}
@article{Nowozin2010,
author = {Nowozin, Sebastian and Lampert, Christoph H.},
doi = {10.1561/0600000033},
issn = {1572-2740},
journal = {Foundations and Trends in Computer Graphics and Vision},
month = mar,
number = {3-4},
pages = {185--365},
title = {{Structured Learning and Prediction in Computer Vision}},
url = {http://dl.acm.org/citation.cfm?id=2185833.2185834},
volume = {6},
year = {2010}
}
@article{Fischler1981,
author = {Fischler, Martin A. and Bolles, Robert C.},
doi = {10.1145/358669.358692},
issn = {00010782},
journal = {Communications of the ACM},
keywords = {automated cartography,camera calibration,image matching,location determination,model fitting,scene analysis},
number = {6},
pages = {381--395},
title = {{Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography}},
url = {http://dl.acm.org/citation.cfm?id=358669.358692},
volume = {24},
year = {1981}
}
@inproceedings{Vedaldi2009,
author = {Vedaldi, Andrea and Gulshan, Varun and Varma, Manik and Zisserman, Andrew},
booktitle = {IEEE International Conference on Computer Vision},
title = {{Multiple kernels for object detection}},
year = {2009}
}
@inproceedings{Alahi2012,
author = {Alahi, Alexandre and Ortiz, Raphael and Vandergheynst, Pierre},
booktitle = {IEEE Conference on Computer Vision and Pattern Recognition},
title = {{FREAK: Fast Retina Keypoint}},
year = {2012}
}
@article{Dietterich1997,
author = {Dietterich, Thomas G.},
doi = {10.1016/S0004-3702(96)00034-3},
issn = {00043702},
journal = {Artificial Intelligence},
keywords = {drug design,machine learning,structure-activity relationships},
number = {1-2},
pages = {31--71},
title = {{Solving the Multiple Instance Problem with Axis-Parallel Rectangles}},
url = {http://dl.acm.org/citation.cfm?id=249678.249682},
volume = {89},
year = {1997}
}
@inproceedings{Stuehmer2010,
author = {Stuehmer, J and Gumhold, S and Cremers, Daniel},
booktitle = {DAGM Symposium on Pattern Recognition},
title = {{Real-Time Dense Geometry from a Handheld Camera}},
year = {2010}
}
@inproceedings{Leutenegger2011,
author = {Leutenegger, Stefan and Chli, Margarita and Siegwart, Roland},
booktitle = {IEEE International Conference on Computer Vision},
file = {:Users/sam/Documents/Mendeley/Library/Leutenegger, Chli, Siegwart/BRISK Binary Robust Invariant Scalable Keypoints - 2011.pdf:pdf},
title = {{BRISK: Binary Robust Invariant Scalable Keypoints}},
year = {2011}
}
@article{Vogiatzis2011,
author = {Vogiatzis, George and Hern\'{a}ndez, Carlos},
doi = {10.1016/j.imavis.2011.01.006},
issn = {02628856},
journal = {Image and Vision Computing},
keywords = {3d reconstruction,Multi-view stereo,Real-time,Shape-from-X},
number = {7},
pages = {434--441},
title = {{Video-based, Real-Time Multi-View Stereo}},
url = {http://dl.acm.org/citation.cfm?id=1994006.1994153},
volume = {29},
year = {2011}
}
@inproceedings{Gehler2009,
author = {Gehler, Peter and Nowozin, Sebastian},
booktitle = {IEEE International Conference on Computer Vision},
keywords = {boosting},
title = {{On Feature Combination for Multiclass Object Classification}},
year = {2009}
}
@article{Bay2008,
abstract = {This article presents a novel scale- and rotation-invariant detector and descriptor, coined SURF (Speeded-Up Robust Features). SURF approximates or even outperforms previously proposed schemes with respect to repeatability, distinctiveness, and robustness, yet can be computed and compared much faster. This is achieved by relying on integral images for image convolutions; by building on the strengths of the leading existing detectors and descriptors (specifically, using a Hessian matrix-based measure for the detector, and a distribution-based descriptor); and by simplifying these methods to the essential. This leads to a combination of novel detection, description, and matching steps. The paper encompasses a detailed description of the detector and descriptor and then explores the effects of the most important parameters. We conclude the article with SURF’s application to two challenging, yet converse goals: camera calibration as a special case of image registration, and object recognition. Our experiments underline SURF’s usefulness in a broad range of topics in computer vision.},
author = {Bay, Herbert and Ess, Andreas and Tuytelaars, Tinne and {Van Gool}, Luc},
doi = {10.1016/j.cviu.2007.09.014},
file = {:Users/sam/Documents/Mendeley/Library/Bay et al/Speeded-Up Robust Features (SURF) - 2008.pdf:pdf},
issn = {10773142},
journal = {Computer Vision and Image Understanding},
keywords = {camera calibration,feature description,interest points,local features,object recognition},
number = {3},
pages = {346--359},
title = {{Speeded-Up Robust Features (SURF)}},
url = {http://dx.doi.org/10.1016/j.cviu.2007.09.014},
volume = {110},
year = {2008}
}
@inproceedings{Sinha2009,
author = {Sinha, Sudipta N and Steedly, Drew and Szeliski, Richard},
booktitle = {IEEE International Conference on Computer Vision},
title = {{Piecewise Planar Stereo for Image-Based Rendering}},
year = {2009}
}
@inproceedings{Kuhn1951,
author = {Kuhn, H W and Tucker, A W},
booktitle = {Second Berkley Symposium on Mathematical Statistics and Probability},
title = {{Nonlinear Programming}},
year = {1951}
}
@article{Schapire1990,
author = {Schapire, Robert E.},
doi = {10.1023/A:1022648800760},
issn = {0885-6125},
journal = {Machine Learning},
keywords = {Machine learning,PAC learning,learnability theory,learning from examples,polynomial-time identification},
number = {2},
pages = {197--227},
title = {{The Strength of Weak Learnability}},
url = {http://dl.acm.org/citation.cfm?id=83637.83645},
volume = {5},
year = {1990}
}
@inproceedings{Newcombe2010,
author = {Newcombe, Richard and Davison, Andrew J},
booktitle = {IEEE Conference on Computer Vision and Pattern Recognition},
title = {{Live Dense Reconstruction with a Single Moving Camera}},
year = {2010}
}
@article{Cortes1995,
author = {Cortes, Corinna and Vapnik, Vladimir},
doi = {10.1023/A:1022627411411},
issn = {0885-6125},
journal = {Machine Learning},
keywords = {efficient learning algorithms,neural networks,pattern recognition,polynomial classifiers,radial basis function classifiers},
number = {3},
pages = {273--297},
title = {{Support-Vector Networks}},
url = {http://dl.acm.org/citation.cfm?id=218919.218929},
volume = {20},
year = {1995}
}
@inproceedings{Ozuysal2006,
author = {\"{O}zuysal, Mustafa and Lepetit, Vincent and Fleuret, Fran\c{c}ois and Fua, Pascal},
booktitle = {European Conference on Computer Vision},
file = {:Users/sam/Documents/Mendeley/Library/\"{O}zuysal et al/Feature Harvesting for Tracking-by-Detection - 2006.pdf:pdf},
title = {{Feature Harvesting for Tracking-by-Detection}},
year = {2006}
}
@inproceedings{Zeisl2010,
author = {Zeisl, Bernhard and Leistner, Christian and Saffari, Amir and Bischof, Horst},
booktitle = {IEEE Conference on Computer Vision and Pattern Recognition},
keywords = {multi-instance,semi-supervised},
title = {{Online Semi-Supervised Multiple-Instance Boosting}},
year = {2010}
}
@article{Polyak1992,
author = {Polyak, B. T. and Juditsky, A. B.},
doi = {10.1137/0330046},
issn = {0363-0129},
journal = {SIAM Journal on Control and Optimization},
keywords = {optimal algorithms,recursive estimation,stochastic approximation,stochastic optimization},
number = {4},
pages = {838--855},
title = {{Acceleration of Stochastic Approximation by Averaging}},
url = {http://dl.acm.org/citation.cfm?id=131092.131098},
volume = {30},
year = {1992}
}
@inproceedings{Wagner2008,
author = {Wagner, Daniel and Reitmayr, G and Mulloni, A and Drummond, T and Schmalstieg, D},
booktitle = {IEEE International Symposium on Mixed and Augmented Reality},
title = {{Pose Tracking from Natural Features on Mobile Phones}},
year = {2008}
}
@article{Bordes2005,
author = {Bordes, Antoine and Ertekin, Seyda and Weston, Jason and Bottou, L\'{e}on},
issn = {1532-4435},
journal = {The Journal of Machine Learning Research},
pages = {1579--1619},
title = {{Fast Kernel Classifiers with Online and Active Learning}},
url = {http://dl.acm.org/citation.cfm?id=1046920.1194898},
volume = {6},
year = {2005}
}
@inproceedings{Buehler2008,
author = {Buehler, Patrick and Everingham, Mark and Huttenlocher, Daniel and Zisserman, Andrew},
booktitle = {British Machine Vision Conference},
title = {{Long Term Arm and Hand Tracking for Continuous Sign Language TV Broadcasts}},
year = {2008}
}
@inproceedings{Williams2003,
author = {Williams, Oliver},
booktitle = {IEEE International Conference on Computer Vision},
title = {{A sparse probabilistic learning algorithm for real-time tracking}},
year = {2003}
}
@book{Hartley2004,
author = {Hartley, Richard and Zisserman, Andrew},
publisher = {Cambridge University Press},
title = {{Multiple View Geometry in Computer Vision}},
year = {2004}
}
@article{Tibshirani1996,
author = {Tibshirani, Robert},
journal = {Journal of the Royal Statistical Society. Series B (Methodological)},
number = {1},
pages = {267--288},
title = {{Regression Shrinkage and Selection via the Lasso}},
volume = {58},
year = {1996}
}
@inproceedings{Szummer2008,
author = {Szummer, Martin and Kohli, Pushmeet and Hoiem, Derek},
booktitle = {European Conference on Computer Vision},
doi = {10.1007/978-3-540-88688-4},
isbn = {978-3-540-88685-3},
issn = {0302-9743},
series = {Lecture Notes in Computer Science},
title = {{Learning CRFs Using Graph Cuts}},
url = {http://dl.acm.org/citation.cfm?id=1479250.1479296},
year = {2008}
}
@inproceedings{Bastian2010,
author = {Bastian, John W. and Ward, Ben and Hill, Rhys and van den Hengel, Anton and Dick, Anthony R.},
booktitle = {IEEE International Symposium on Mixed and Augmented Reality},
title = {{Interactive Modelling for AR Applications}},
year = {2010}
}
@article{Lampert2009,
abstract = {Most successful object recognition systems rely on binary classification, deciding only if an object is present or not, but not providing information on the actual object location. To estimate the object's location, one can take a sliding window approach, but this strongly increases the computational cost because the classifier or similarity function has to be evaluated over a large set of candidate subwindows. In this paper, we propose a simple yet powerful branch and bound scheme that allows efficient maximization of a large class of quality functions over all possible subimages. It converges to a globally optimal solution typically in linear or even sublinear time, in contrast to the quadratic scaling of exhaustive or sliding window search. We show how our method is applicable to different object detection and image retrieval scenarios. The achieved speedup allows the use of classifiers for localization that formerly were considered too slow for this task, such as SVMs with a spatial pyramid kernel or nearest-neighbor classifiers based on the $\backslash$chi;2 distance. We demonstrate state-of-the-art localization performance of the resulting systems on the UIUC Cars data set, the PASCAL VOC 2006 data set, and in the PASCAL VOC 2007 competition.},
author = {Lampert, Christoph H and Blaschko, Matthew B and Hofmann, Thomas},
doi = {10.1109/TPAMI.2009.144},
issn = {1939-3539},
journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
keywords = {Object localization,branch and bound.,global optimization,sliding window},
number = {12},
pages = {2129--42},
pmid = {19834136},
title = {{Efficient Subwindow Search: A Branch and Bound Framework for Object Localization}},
url = {http://dl.acm.org/citation.cfm?id=1638615.1639292},
volume = {31},
year = {2009}
}
@inproceedings{Campbell2008,
author = {Campbell, N. D. F. and Vogiatzis, G. and Hern\'{a}ndez, C. and Cipolla, Roberto},
booktitle = {European Conference on Computer Vision},
title = {{Using Multiple Hypotheses to Improve Depth-Maps for Multi-View Stereo}},
year = {2008}
}
@inproceedings{Masnadi-shirazi2010,
author = {Masnadi-shirazi, Hamed and Mahadevan, Vijay and Vasconcelos, Nuno},
booktitle = {IEEE Conference on Computer Vision and Pattern Recognition},
keywords = {boosting},
title = {{On the design of robust classifiers for computer vision}},
url = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.172.6416},
year = {2010}
}
@article{Davison2007,
abstract = {We present a real-time algorithm which can recover the 3D trajectory of a monocular camera, moving rapidly through a previously unknown scene. Our system, which we dub MonoSLAM, is the first successful application of the SLAM methodology from mobile robotics to the "pure vision" domain of a single uncontrolled camera, achieving real time but drift-free performance inaccessible to Structure from Motion approaches. The core of the approach is the online creation of a sparse but persistent map of natural landmarks within a probabilistic framework. Our key novel contributions include an active approach to mapping and measurement, the use of a general motion model for smooth camera movement, and solutions for monocular feature initialization and feature orientation estimation. Together, these add up to an extremely efficient and robust algorithm which runs at 30 Hz with standard PC and camera hardware. This work extends the range of robotic systems in which SLAM can be usefully applied, but also opens up new areas. We present applications of MonoSLAM to real-time 3D localization and mapping for a high-performance full-size humanoid robot and live augmented reality with a hand-held camera.},
author = {Davison, Andrew J and Reid, Ian D and Molton, Nicholas D and Stasse, Olivier},
doi = {10.1109/TPAMI.2007.1049},
file = {:Users/sam/Documents/Mendeley/Library/Davison et al/MonoSLAM real-time single camera SLAM. - 2007.pdf:pdf},
issn = {01628828},
journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
keywords = {algorithms,artificial intelligence,automated,automated methods,computer assisted,computer assisted methods,computer systems,image enhancement,image enhancement methods,image interpretation,imaging,information storage retrieval,information storage retrieval methods,numerical analysis,pattern recognition,photogrammetry,photogrammetry methods,signal processing,three dimensional,three dimensional methods,video recording,video recording methods},
number = {6},
pages = {1052--67},
pmid = {17431302},
title = {{MonoSLAM: real-time single camera SLAM.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/17431302},
volume = {29},
year = {2007}
}
@inproceedings{Oza2005,
author = {Oza, N. C.},
booktitle = {IEEE International Conference on Systems, Man and Cybernetics},
title = {{Online Bagging and Boosting}},
year = {2005}
}
@article{Tsochantaridis2005,
author = {Tsochantaridis, Ioannis and Joachims, Thorsten and Hofmann, Thomas and Altun, Yasemin},
file = {:Users/sam/Documents/Mendeley/Library/Tsochantaridis et al/Large Margin Methods for Structured and Interdependent Output Variables - 2005.pdf:pdf},
issn = {1532-4435},
journal = {Journal of Machine Learning Research},
pages = {1453--1484},
title = {{Large Margin Methods for Structured and Interdependent Output Variables}},
volume = {6},
year = {2005}
}
@inproceedings{Duan2005,
author = {Duan, Kai-Bo and Keerthi, S},
booktitle = {International Conference on Multiple Classifier Systems},
title = {{Which is the Best Multiclass SVM Method? An Empirical Study}},
year = {2005}
}
@article{Ross2007,
author = {Ross, David A. and Lim, Jongwoo and Lin, Ruei-Sung and Yang, Ming-Hsuan},
doi = {10.1007/s11263-007-0075-7},
issn = {0920-5691},
journal = {International Journal of Computer Vision},
keywords = {Adaptive methods,Illumination,Online algorithms,Particle filter,Subspace update,Visual tracking},
number = {1-3},
pages = {125--141},
title = {{Incremental Learning for Robust Visual Tracking}},
url = {http://dl.acm.org/citation.cfm?id=1345995.1346002},
volume = {77},
year = {2007}
}
@article{Dietterich2000,
author = {Dietterich, Thomas G.},
doi = {10.1023/A:1007607513941},
issn = {0885-6125},
journal = {Machine Learning},
keywords = {C4.5,Monte Carlo methods,bagging,boosting,decision trees,ensemble learning},
number = {2},
pages = {139--157},
title = {{An Experimental Comparison of Three Methods for Constructing Ensembles of Decision Trees: Bagging, Boosting, and Randomization}},
url = {http://dl.acm.org/citation.cfm?id=350128.350131},
volume = {40},
year = {2000}
}
@inproceedings{Newcombe2011,
author = {Newcombe, Richard and Lovegrove, Steven J and Davison, Andrew J},
booktitle = {IEEE International Conference on Computer Vision},
title = {{DTAM: Dense Tracking and Mapping in Real-Time}},
year = {2011}
}
@incollection{Joachims1999a,
author = {Joachims, Thorsten},
booktitle = {Advances in Kernel Methods},
editor = {Sch\"{o}lkopf, B and Burges, C and Smola, A},
publisher = {MIT Press},
title = {{Making Large-Scale SVM Learning Practical}},
year = {1999}
}
@inproceedings{Shi1994,
author = {Shi, Jianbo and Tomasi, Carlo},
booktitle = {IEEE Conference on Computer Vision and Pattern Recognition},
doi = {10.1109/CVPR.1994.323794},
isbn = {0-8186-5825-8},
issn = {1063-6919},
keywords = {Feature extraction,Machine vision,Newton-Raphson style search methods,Tracking,affine image transformations,computer vision,disocclusions,feature monitoring,feature selection,feature-based,occlusions,performance,tracker,vision system},
language = {English},
title = {{Good Features to Track}},
url = {http://ieeexplore.ieee.org/articleDetails.jsp?arnumber=323794\&contentType=Conference+Publications},
year = {1994}
}
@inproceedings{Sivic2003,
author = {Sivic, Josef and Zisserman, Andrew},
booktitle = {IEEE International Conference on Computer Vision},
isbn = {0-7695-1950-4},
month = oct,
title = {{Video Google: A Text Retrieval Approach to Object Matching in Videos}},
url = {http://dl.acm.org/citation.cfm?id=946247.946751},
year = {2003}
}
@article{Kolmogorov2004,
abstract = {In the last few years, several new algorithms based on graph cuts have been developed to solve energy minimization problems in computer vision. Each of these techniques constructs a graph such that the minimum cut on the graph also minimizes the energy. Yet, because these graph constructions are complex and highly specific to a particular energy function, graph cuts have seen limited application to date. In this paper, we give a characterization of the energy functions that can be minimized by graph cuts. Our results are restricted to functions of binary variables. However, our work generalizes many previous constructions and is easily applicable to vision problems that involve large numbers of labels, such as stereo, motion, image restoration, and scene reconstruction. We give a precise characterization of what energy functions can be minimized using graph cuts, among the energy functions that can be written as a sum of terms containing three or fewer binary variables. We also provide a general-purpose construction to minimize such an energy function. Finally, we give a necessary condition for any energy function of binary variables to be minimized by graph cuts. Researchers who are considering the use of graph cuts to optimize a particular energy function can use our results to determine if this is possible and then follow our construction to create the appropriate graph. A software implementation is freely available.},
author = {Kolmogorov, Vladimir and Zabih, Ramin},
doi = {10.1109/TPAMI.2004.1262177},
issn = {0162-8828},
journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
keywords = {Algorithms,Artificial Intelligence,Automated,Computer Graphics,Computer-Assisted,Computer-Assisted: methods,Image Enhancement,Image Enhancement: methods,Image Interpretation,Imaging,Information Storage and Retrieval,Information Storage and Retrieval: methods,Numerical Analysis,Pattern Recognition,Reproducibility of Results,Sensitivity and Specificity,Signal Processing,Subtraction Technique,Three-Dimensional,Three-Dimensional: methods,User-Computer Interface},
language = {English},
number = {2},
pages = {147--59},
pmid = {15376891},
title = {{What Energy Functions can be Minimized via Graph Cuts?}},
url = {http://ieeexplore.ieee.org/articleDetails.jsp?arnumber=1262177\&contentType=Journals+\%26+Magazines},
volume = {26},
year = {2004}
}
@inproceedings{Leistner2010,
author = {Leistner, Christian and Saffari, Amir and Bischof, Horst},
booktitle = {European Conference on Computer Vision},
keywords = {forest},
title = {{MIForests: Multiple-Instance Learning with Randomized Trees}},
year = {2010}
}
@inproceedings{Saffari2009,
author = {Saffari, Amir and Leistner, Christian and Santner, J and Godec, Martin and Bischof, Horst},
booktitle = {Proc. ICCV-OLCV},
title = {{On-line Random Forests}},
year = {2009}
}
@article{Kelley1960,
author = {{Kelley Jr}, J E},
journal = {Journal of the Society for Industrial \& Applied Mathematics},
number = {4},
pages = {703--712},
publisher = {SIAM},
title = {{The Cutting-Plane Method for Solving Convex Programs}},
volume = {8},
year = {1960}
}
@inproceedings{Benhimane2004,
author = {Benhimane, S and Malis, E},
booktitle = {IEEE/RSJ International Conference on Intelligent Robots and Systems},
title = {{Real-Time Image-Based Tracking of Planes Using Efficient Second-Order Minimization}},
year = {2004}
}
@inproceedings{Tuytelaars2000,
author = {Tuytelaars, Tinne and {Van Gool}, Luc},
booktitle = {British Machine Vision Conference},
title = {{Wide Baseline Stereo Matching based on Local, Afﬁnely Invariant Regions}},
year = {2000}
}
@article{Torr2000,
abstract = {A new method is presented for robustly estimating multiple view relations from point correspondences. The method comprises two parts. The first is a new robust estimator MLESAC which is a generalization of the RANSAC estimator. It adopts the same sampling strategy as RANSAC to generate putative solutions, but chooses the solution that maximizes the likelihood rather than just the number of inliers. The second part of the algorithm is a general purpose method for automatically parameterizing these relations, using the output of MLESAC. A difficulty with multiview image relations is that there are often nonlinear constraints between the parameters, making optimization a difficult task. The parameterization method overcomes the difficulty of nonlinear constraints and conducts a constrained optimization. The method is general and its use is illustrated for the estimation of fundamental matrices, image–image homographies, and quadratic transformations. Results are given for both synthetic and real images. It is demonstrated that the method gives results equal or superior to those of previous approaches.},
author = {Torr, Philip H. S. and Zisserman, Andrew},
doi = {10.1006/cviu.1999.0832},
issn = {10773142},
journal = {Computer Vision and Image Understanding},
number = {1},
pages = {138--156},
title = {{MLESAC: A New Robust Estimator with Application to Estimating Image Geometry}},
url = {http://dx.doi.org/10.1006/cviu.1999.0832},
volume = {78},
year = {2000}
}
@article{Breiman2001,
author = {Breiman, Leo},
doi = {10.1023/A:1010933404324},
issn = {0885-6125},
journal = {Machine Learning},
keywords = {classification,ensemble,regression},
number = {1},
pages = {5--32},
title = {{Random Forests}},
url = {http://dl.acm.org/citation.cfm?id=570181.570182},
volume = {45},
year = {2001}
}
@inproceedings{Alahari2010,
author = {Alahari, Karteek and Russell, Chris and Torr, Philip H. S.},
booktitle = {IEEE Conference on Computer Vision and Pattern Recognition},
title = {{Efficient Piecewise Learning for Conditional Random Fields}},
year = {2010}
}
@book{Shor1985,
author = {Shor, N Z},
publisher = {Springer},
title = {{Minimization Methods for Non-Differentiable Functions}},
year = {1985}
}
@article{Lowe2004,
abstract = {This paper presents a method for extracting distinctive invariant features from images that can be used to perform reliable matching between different views of an object or scene. The features are invariant to image scale and rotation, and are shown to provide robust matching across a substantial range of affine distortion, change in 3D viewpoint, addition of noise, and change in illumination. The features are highly distinctive, in the sense that a single feature can be correctly matched with high probability against a large database of features from many images. This paper also describes an approach to using these features for object recognition. The recognition proceeds by matching individual features to a database of features from known objects using a fast nearest-neighbor algorithm, followed by a Hough transform to identify clusters belonging to a single object, and finally performing verification through least-squares solution for consistent pose parameters. This approach to recognition can robustly identify objects among clutter and occlusion while achieving near real-time performance.},
author = {Lowe, David G.},
file = {:Users/sam/Documents/Mendeley/Library/Lowe/Distinctive Image Features from Scale-Invariant Keypoints - 2004.pdf:pdf},
journal = {International Journal of Computer Vision},
number = {2},
pages = {91--110},
title = {{Distinctive Image Features from Scale-Invariant Keypoints}},
url = {http://www.springerlink.com/content/h4l02691327px768/},
volume = {60},
year = {2004}
}
@inproceedings{Matas2002,
author = {Matas, Jiri and Chum, Ondrej and Urban, M and Pajdla, T},
booktitle = {British Machine Vision Conference},
title = {{Robust Wide Baseline Stereo from Maximally Stable Extremal Regions}},
year = {2002}
}
@article{Delong2011,
author = {Delong, Andrew and Osokin, Anton and Isack, Hossam N. and Boykov, Yuri},
doi = {10.1007/s11263-011-0437-z},
issn = {0920-5691},
journal = {International Journal of Computer Vision},
keywords = {Energy minimization,Graph cuts,Metric labeling,Minimum description length,Multi-model fitting},
number = {1},
pages = {1--27},
title = {{Fast Approximate Energy Minimization with Label Costs}},
url = {http://dl.acm.org/citation.cfm?id=2124511.2124539},
volume = {96},
year = {2011}
}
@inproceedings{Grabner2006,
author = {Grabner, H and Grabner, M and Bischof, H},
booktitle = {British Machine Vision Conference},
title = {{Real-Time Tracking via On-line Boosting}},
year = {2006}
}
@inproceedings{Lucas1981,
author = {Lucas, Bruce D and Kanade, Takeo},
booktitle = {International Joint Conference on Artificial Intelligence},
title = {{An Iterative Image Registration Technique with an Application to Stereo Vision}},
year = {1981}
}
