A nonparametric Riemannian framework on tensor field with application to foreground segmentation
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[1] M. M. G. Ricci,et al. Méthodes de calcul différentiel absolu et leurs applications , 1900 .
[2] Memoria di T. Levi-Civita. Nozione di parallelismo in una varietà qualunque e conseguente specificazione geometrica della curvatura riemanniana , 1916 .
[3] I. Holopainen. Riemannian Geometry , 1927, Nature.
[4] W. Boothby. An introduction to differentiable manifolds and Riemannian geometry , 1975 .
[5] T. Willmore. MANIFOLDS ALL OF WHOSE GEODESICS ARE CLOSED , 1979 .
[6] C. Atkinson. Rao's distance measure , 1981 .
[7] L. Skovgaard. A Riemannian geometry of the multivariate normal model , 1984 .
[8] Silvano Di Zenzo,et al. A note on the gradient of a multi-image , 1986, Comput. Vis. Graph. Image Process..
[9] Christopher G. Harris,et al. A Combined Corner and Edge Detector , 1988, Alvey Vision Conference.
[10] J. Bigun. Recognition of local symmetries in gray value images by harmonic functions , 1988 .
[11] Johan Wiklund,et al. Multidimensional Orientation Estimation with Applications to Texture Analysis and Optical Flow , 1991, IEEE Trans. Pattern Anal. Mach. Intell..
[12] C. R. Rao,et al. Information and the Accuracy Attainable in the Estimation of Statistical Parameters , 1992 .
[13] I. Chavel. Riemannian Geometry: Subject Index , 2006 .
[14] Matthew P. Wand,et al. Kernel Smoothing , 1995 .
[15] A. Ryan. Russell , 1996 .
[16] Alex Pentland,et al. Pfinder: Real-Time Tracking of the Human Body , 1997, IEEE Trans. Pattern Anal. Mach. Intell..
[17] Stuart J. Russell,et al. Image Segmentation in Video Sequences: A Probabilistic Approach , 1997, UAI.
[18] E. García‐Río,et al. Semi-Riemannian Manifolds , 1999 .
[19] W. Eric L. Grimson,et al. Adaptive background mixture models for real-time tracking , 1999, Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149).
[20] Azriel Rosenfeld,et al. Detection and location of people in video images using adaptive fusion of color and edge information , 2000, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000.
[21] Alex Pentland,et al. A Bayesian Computer Vision System for Modeling Human Interactions , 1999, IEEE Trans. Pattern Anal. Mach. Intell..
[22] Larry S. Davis,et al. Non-parametric Model for Background Subtraction , 2000, ECCV.
[23] Mubarak Shah,et al. A hierarchical approach to robust background subtraction using color and gradient information , 2002, Workshop on Motion and Video Computing, 2002. Proceedings..
[24] L. Davis,et al. Background and foreground modeling using nonparametric kernel density estimation for visual surveillance , 2002, Proc. IEEE.
[25] John M. Lee. Introduction to Smooth Manifolds , 2002 .
[26] Kai-Kuang Ma,et al. Accurate optical flow estimation using adaptive scale-space and 3D structure tensor , 2002, Proceedings. International Conference on Image Processing.
[27] Mohan M. Trivedi,et al. Detecting Moving Shadows: Algorithms and Evaluation , 2003, IEEE Trans. Pattern Anal. Mach. Intell..
[28] Nikos Paragios,et al. Background modeling and subtraction of dynamic scenes , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.
[29] Kazuhiko Sumi,et al. Background subtraction based on cooccurrence of image variations , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..
[30] Xavier Pennec,et al. A Riemannian Framework for Tensor Computing , 2005, International Journal of Computer Vision.
[31] Rachid Deriche,et al. Tensor Processing for Texture and Colour Segmentation , 2005, SCIA.
[32] Bruno Pelletier. Kernel density estimation on Riemannian manifolds , 2005 .
[33] Fatih Murat Porikli,et al. A Bayesian Approach to Background Modeling , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops.
[34] Maher Moakher,et al. A Differential Geometric Approach to the Geometric Mean of Symmetric Positive-Definite Matrices , 2005, SIAM J. Matrix Anal. Appl..
[35] Robert Pless,et al. Spatio-temporal Background Models for Outdoor Surveillance , 2005, EURASIP J. Adv. Signal Process..
[36] Yaser Sheikh,et al. Bayesian modeling of dynamic scenes for object detection , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[37] Fatih Murat Porikli,et al. Covariance Tracking using Model Update Based on Lie Algebra , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).
[38] Xavier Pennec,et al. Statistical Computing on Manifolds for Computational Anatomy , 2006 .
[39] Thrasyvoulos N. Pappas,et al. Spatiotemporal algorithm for joint video segmentation and foreground detection , 2006, 2006 14th European Signal Processing Conference.
[40] Marko Heikkilä,et al. A texture-based method for modeling the background and detecting moving objects , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[41] Rachid Deriche,et al. DTI segmentation by statistical surface evolution , 2006, IEEE Transactions on Medical Imaging.
[42] Rachid Deriche,et al. Statistics on the Manifold of Multivariate Normal Distributions: Theory and Application to Diffusion Tensor MRI Processing , 2006, Journal of Mathematical Imaging and Vision.
[43] Fatih Murat Porikli,et al. Region Covariance: A Fast Descriptor for Detection and Classification , 2006, ECCV.
[44] Nicholas Ayache,et al. Geometric Means in a Novel Vector Space Structure on Symmetric Positive-Definite Matrices , 2007, SIAM J. Matrix Anal. Appl..
[45] Xavier Pennec,et al. Intrinsic Statistics on Riemannian Manifolds: Basic Tools for Geometric Measurements , 2006, Journal of Mathematical Imaging and Vision.
[46] Edwin R. Hancock,et al. A Riemannian approach to graph embedding , 2007, Pattern Recognit..
[47] Patrick Pérez,et al. Detection and segmentation of moving objects in highly dynamic scenes , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.
[48] Yogesh Rathi,et al. A Graph Cut Approach to Image Segmentation in Tensor Space , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.
[49] Nicolas Martel-Brisson,et al. Learning and Removing Cast Shadows through a Multidistribution Approach , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[50] Suyash P. Awate,et al. Fuzzy Nonparametric DTI Segmentation for Robust Cingulum-Tract Extraction , 2007, MICCAI.
[51] Jean-Marc Odobez,et al. Multi-Layer Background Subtraction Based on Color and Texture , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.
[52] Yi-Ping Hung,et al. Efficient hierarchical method for background subtraction , 2007, Pattern Recognit..
[53] N. Ayache,et al. Clinical DT-MRI Estimation, Smoothing, and Fiber Tracking With Log-Euclidean Metrics , 2007 .
[54] Shengping Zhang,et al. Dynamic background modeling and subtraction using spatio-temporal local binary patterns , 2008, 2008 15th IEEE International Conference on Image Processing.
[55] Fatih Murat Porikli,et al. Pedestrian Detection via Classification on Riemannian Manifolds , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[56] Deborah Estrin,et al. Background Subtraction on Distributions , 2008, ECCV.
[57] Vassilios Morellas,et al. Robust Foreground Detection In Video Using Pixel Layers , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[58] Thierry Bouwmans,et al. Background Modeling using Mixture of Gaussians for Foreground Detection - A Survey , 2008 .
[59] Shireen Elhabian,et al. Moving Object Detection in Spatial Domain using Background Removal Techniques - State-of-Art , 2008 .
[60] Nuno Vasconcelos,et al. Background subtraction in highly dynamic scenes , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.
[61] Pascal Fua,et al. Making Background Subtraction Robust to Sudden Illumination Changes , 2008, ECCV.
[62] Minglun Gong,et al. Realtime background subtraction from dynamic scenes , 2009, 2009 IEEE 12th International Conference on Computer Vision.
[63] Nikos Paragios,et al. Scene modeling and change detection in dynamic scenes: A subspace approach , 2009, Comput. Vis. Image Underst..
[64] Takeo Kanade,et al. Background Subtraction for Freely Moving Cameras , 2009, 2009 IEEE 12th International Conference on Computer Vision.
[65] Jian Yang,et al. A model for dynamic object segmentation with kernel density estimation based on gradient features , 2009, Image Vis. Comput..
[66] Shengping Zhang,et al. Dynamic Background Subtraction Based on Local Dependency Histogram , 2008, Int. J. Pattern Recognit. Artif. Intell..
[67] Patrick Pérez,et al. Detection and segmentation of moving objects in complex scenes , 2009, Comput. Vis. Image Underst..
[68] Thierry Bouwmans,et al. Subspace Learning for Background Modeling: A Survey , 2009 .
[69] Hongxun Yao,et al. Local Histogram of Figure/Ground Segmentations for Dynamic Background Subtraction , 2010, EURASIP J. Adv. Signal Process..
[70] Matti Pietikäinen,et al. Modeling pixel process with scale invariant local patterns for background subtraction in complex scenes , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.
[71] Nuno Vasconcelos,et al. Spatiotemporal Saliency in Dynamic Scenes , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[72] Bertrand Vachon,et al. Statistical Background Modeling for Foreground Detection: A Survey , 2010 .
[73] Rui Caseiro,et al. Foreground Segmentation via Background Modeling on Riemannian Manifolds , 2010, 2010 20th International Conference on Pattern Recognition.
[74] Nuno Vasconcelos,et al. Generalized Stauffer–Grimson background subtraction for dynamic scenes , 2011, Machine Vision and Applications.
[75] Rui Caseiro,et al. Background Modelling on Tensor Field for Foreground Segmentation , 2010, BMVC.
[76] Fan Zhang,et al. New Riemannian techniques for directional and tensorial image data , 2010, Pattern Recognit..
[77] Minglun Gong. Foreground segmentation of live videos using locally competing 1SVMs , 2011, CVPR 2011.
[78] Wenbing Tao,et al. Texture segmentation using independent-scale component-wise Riemannian-covariance Gaussian mixture model in KL measure based multi-scale nonlinear structure tensor space , 2011, Pattern Recognit..
[79] Rui Caseiro,et al. A nonparametric Riemannian framework on tensor field with application to foreground segmentation , 2012, Pattern Recognit..