Remoção de Fundo

  1. [Moeslund:2006:CVIU] Moeslund, Thomas B. and Hilton, Adrian and Krüger, Volker. A Survey of Advances in Vision-Based Human Motion Capture and Analysis. vol. 104, n. 2, pp. 90--126, 2006.
  2. [Ren:2003:PRL] Ren, Ying and Chua, Chin-Seng and Ho, Yeong-Khing. Statistical background modeling for non-stationary camera. vol. 24, pp. 183--196, 2003.
  3. [Cucchiara:2003:PAMI] Rita Cucchiara and Costantino Grana and Massimo Piccardi and Andrea Prati. Detecting Moving Objects, Ghosts, and Shadows in Video Streams. vol. 25, pp. 1337-1342, 2003.
  4. [Elgammal:2000:ECCV] Elgammal, Ahmed M. and Harwood, David and Davis, Larry S.. Non-parametric Model for Background Subtraction. pp. 751--767, 2000.
  5. [Mittal:2000:CVPR] Anurag Mittal and Dan Huttenlocher. Scene Modeling for Wide Area Surveillance and Image Synthesis. 2000.
  6. [Stauffer:1999:CVPR] Chris Stauffer and W.E.L. Grimson. Adaptive Background Mixture Models for Real-Time Tracking. 1999.
  7. [Friedman:1997:UAI] Friedman, Nir and Russell, Stuart. Image Segmentation in Video Sequences: A Probabilistic Approach. Conference on Uncertainty in artificial intelligence, pp. 175--181, 1997.
  8. [Rowe:1996:IVC] Simon Rowe and Andrew Blake. Statistical Mosaics for Tracking. vol. 14, n. 8, pp. 549 - 564, 1996.
  9. [Koller:1994:ICPR] Koller, D. and Weber, J. and Huang, T. and Malik, J. and Ogasawara, G. and Rao, B. and Russell, S.. Towards Robust Automatic Traffic Scene Analysis in Real-Time. pp. 126--131, 1994.

Descrição de Características

  1. [Gauglitz:2011:IJCV] Gauglitz, Steffen and Höllerer, Tobias and Turk, Matthew. Evaluation of Interest Point Detectors and Feature Descriptors for Visual Tracking. vol. 94, n. 3, pp. 335--360, 2011.
  2. [Sande:2010:PAMI] van de Sande, K.E.A. and Gevers, T. and Snoek, C.G.M.. Evaluating Color Descriptors for Object and Scene Recognition. vol. 32, n. 9, pp. 1582--1596, 2010.
  3. [Calonder:2010:ECCV] Calonder, Michael and Lepetit, Vincent and Strecha, Christoph and Fua, Pascal. BRIEF: Binary Robust Independent Elementary Features. pp. 778--792, 2010.
  4. [Tombari:2010:ECCV] Tombari, Federico and Salti, Samuele and Di Stefano, Luigi. Unique Signatures of Histograms for Local Surface Description. pp. 356--369, 2010.
  5. [Chandrasekhar:2009:CVPR] Chandrasekhar, V. and Takacs, G. and Chen, D. and Tsai, S. and Grzeszczuk, R. and Girod, B.. CHoG: Compressed Histogram of Gradients A Low Bit-Rate Feature Descriptor. pp. 2504--2511, 2009.
  6. [Heikkila:2009:PR] Heikkilä, Marko and Pietikäinen, Matti and Schmid, Cordelia. Description of Interest Regions with Local Binary Patterns. vol. 42, n. 3, pp. 425--436, 2009.
  7. [Yeffet:2009:ICCV] Lahav Yeffet and Lior Wolf. Local Trinary Patterns for Human Action Recognition. pp. 492--497, 2009.
  8. [Tuytelaars:2008:FTC] Tuytelaars, Tinne and Mikolajczyk, Krystian. Local Invariant Feature Detectors: A Survey. Foundation and Trends in Computer Graphics and Vision, pp. 177--280, 2008.
  9. [Li:2008:Neurocomputing] Li, Jing and Allinson, Nigel M.. A Comprehensive review of Current Local Features for Computer Vision. vol. 71, n. 10-12, pp. 1771--1787, 2008.
  10. [Tangelder:2008:MulTool] Tangelder, Johan W. and Veltkamp, Remco C.. A Survey of Content Based 3D Shape Retrieval Methods. Multimedia Tools and Applications, vol. 39, n. 3, pp. 441--471, 2008.
  11. [Bay:2008:CVIU] Bay, Herbert and Ess, Andreas and Tuytelaars, Tinne and Van Gool, Luc. Speeded-Up Robust Features (SURF). vol. 110, n. 3, pp. 346--359, 2008.
  12. [Tola:2008:CVPR] Engin Tola and Vincent Lepetit and Pascal Fua. A Fast Local Descriptor for Dense Matching. 2008.
  13. [Montoya-Zegarra:2008:ISM] Montoya-Zegarra, Javier A. and Beeck, Jan and Leite, Neucimar and Torres, Ricardo and Falcão, Alexandre. Combining Global with Local Texture Information for Image Retrieval Applications. Proceedings of the IEEE International Symposium on Multimedia, pp. 148--153, 2008.
  14. [Klaser:2008:BMVC] Klaser, Alexander and Marszalek, Marcin and Schmid, Cordelia. A Spatio-Temporal Descriptor Based on 3D-Gradients. 2008.
  15. [Zhang:2007:IJCV] Zhang, J. and Marszalek, M. and Lazebnik, S. and Schmid, C.. Local Features and Kernels for Classification of Texture and Object Categories: A Comprehensive Study. vol. 73, n. 2, pp. 213--238, 2007.
  16. [Scovanner:2007:Multimedia] Scovanner, Paul and Ali, Saad and Shah, Mubarak. A 3-dimensional SIFT Descriptor and its Application to Action Recognition. international conference on Multimedia, pp. 357--360, 2007.
  17. [Shechtman:2007:CVPR] Eli Shechtman and Michal Irani. Matching Local Self-Similarities across Images and Videos. 2007.
  18. [Weijer:2006:ECCV] van de Weijer, Joost and Schmid, Cordelia. Coloring Local Feature Extraction. pp. 334--348, 2006.
  19. [Tuzel:2006:ECCV] Tuzel, Oncel and Porikli, Fatih and Meer, Peter. Region Covariance: A Fast Descriptor for Detection and Classification. pp. 589--600, 2006.
  20. [Dalal:2006:ECCV] Dalal, Navneet and Triggs, Bill and Schmid, Cordelia. Human Detection Using Oriented Histograms of Flow and Appearance. pp. 428--441, 2006.
  21. [Mikolajczyk:2005:PAMI] Mikolajczyk, K. and Schmid, C.. A Performance Evaluation of Local Descriptors. vol. 27, n. 10, pp. 1615--1630, 2005.
  22. [Dalal:2005:CVPR] Dalal, N. and Triggs, B.. Histograms of Oriented Gradients for Human Detection. pp. 886--893, 2005.
  23. [Lazebnik:2005:PAMI] Lazebnik, Svetlana and Schmid, Cordelia and Ponce, Jean. A Sparse Texture Representation Using Local Affine Regions. vol. 27, n. 8, pp. 1265--1278, 2005.
  24. [Serre:2005:CVPR] Serre, Thomas and Wolf, Lior and Poggio, Tomaso. Object Recognition with Features Inspired by Visual Cortex. pp. 994--1000, 2005.
  25. [Ling:2005:ICCV] Ling, Haibin and Jacobs, David W.. Deformation Invariant Image Matching. pp. 1466--1473, 2005.
  26. [Wu:2005:ICCV] Wu, Bo and Nevatia, Ram. Detection of Multiple, Partially Occluded Humans in a Single Image by Bayesian Combination of Edgelet Part Detectors. pp. 90--97, 2005.
  27. [Dollar:2005:CCCN] Dollar, P. and Rabaud, V. and Cottrell, G. and Belongie, S.. Behavior recognition via sparse spatio-temporal features. International Conference on Computer Communications and Networks, pp. 65--72, 2005.
  28. [Lowe:IJCV:2004] Lowe, D. G.. Distinctive Image Features from Scale-Invariant Keypoints. vol. 60, pp. 91--110, 2004.
  29. [Ke:2004:CVPR] Ke, Yan and Sukthankar, Rahul. PCA-SIFT: A More Distinctive Representation for Local Image Descriptors. pp. 506--513, 2004.
  30. [Levi:2004:CVPR] Levi, Kobi and Weiss, Yair. Learning Object Detection from a Small Number of Examples: The Importance of Good Features. pp. 53--60, 2004.
  31. [Frome:2004:ECCV] Andrea Frome and Daniel Huber and Ravi Kolluri and Thomas Bülow and Jitendra Malik. Recognizing Objects in Range Data Using Regional Point Descriptors. pp. 224--237, 2004.
  32. [Laptev:2004:SCVMA] Ivan Laptev and Tony Lindeberg. Local Descriptors for Spatio-temporal Recognition. First International Workshop on Spatial Coherence for Visual Motion Analysis, pp. 91--103, 2004.
  33. [Lazebnik:2003:CVPR] Svetlana Lazebnik and Cordelia Schmid and Jean Ponce. A Sparse Texture Representation Using Affine-Invariant Regions. pp. 319-326, 2003.
  34. [Koertgen:2003:CESCG] Körtgen, Marcel and Park, G. -J. and Novotni, Marcin and Klein, Reinhard. 3D Shape Matching with 3D Shape Contexts. The 7th Central European Seminar on Computer Graphics, 2003.
  35. [Schaffalitzky:2002:ECCV] Schaffalitzky, Frederik and Zisserman, Andrew. Multi-view Matching for Unordered Image Sets, or How Do I Organize My Holiday Snaps?. pp. 414--431, 2002.
  36. [Belongie:2002:PAMI] Belongie, S. and Malik, J. and Puzicha, J.. Shape Matching and Object Recognition Using Shape Contexts. vol. 24, n. 4, pp. 509--522, 2002.
  37. [Ojala:2002:PAMI] Ojala, Timo and Pietikäinen, Matti and Mäenpää, Topi. Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns. vol. 24, pp. 971--987, 2002.
  38. [Leung:2001:IJCV] Leung, Thomas and Malik, Jitendra. Representing and Recognizing the Visual Appearance of Materials using Three-dimensional Textons. vol. 43, n. 1, pp. 29--44, 2001.
  39. [Berg:2001:CVPR] Alexander C. Berg and Jitendra Malik. Geometric Blur for Template Matching. pp. 607-614, 2001.
  40. [Kim:2000:SigProc] Whoi-Yul Kim and Yong-Sung Kim. A Region-Based Shape Descriptor using Zernike Moments. Signal Processing: Image Communication, vol. 16, n. 1-2, pp. 95--102, 2000.
  41. [Papageorgiou:2000:IJCV] Papageorgiou, Constantine and Poggio, Tomaso. A Trainable System for Object Detection. vol. 38, n. 1, pp. 15--33, 2000.
  42. [Randen:1999:PAMI] T. Randen and J. H. Husoy. Filtering for Texture Classification: A Comparative Study. vol. 21, pp. 291-310, 1999.
  43. [Johnson:1999:PAMI] Johnson, Andrew E. and Hebert, Martial. Using Spin Images for Efficient Object Recognition in Cluttered 3D Scenes. vol. 21, n. 5, pp. 433--449, 1999.
  44. [Schmid:1997:PAMI] Schmid, Cordelia and Mohr, Roger. Local Grayvalue Invariants for Image Retrieval. vol. 19, n. 5, pp. 530--535, 1997.
  45. [Edelman:1997:umpublished] Shimon Edelman and Nathan Intrator and Tomaso Poggio. Complex Cells and Object Recognition. 1997.
  46. [Johnson:1997:CVPR] Johnson, Andrew Edie and Hebert, Martial. Recognizing Objects by Matching Oriented Points. pp. 684--, 1997.
  47. [Chua:1997:IJCV] Chua, Chin Seng and Jarvis, Ray. Point Signatures: A New Representation for 3D Object Recognition. vol. 25, n. 1, pp. 63--85, 1997.
  48. [Lee:1996:PAMI] Lee, Tai Sing. Image Representation Using 2D Gabor Wavelets. vol. 18, n. 10, pp. 959--971, 1996.
  49. [Ojala:1996:PR] Timo Ojala and Matti Pietikäinen and David Harwood. A Comparative Study of Texture Measures with Classification based on Featured Distributions. vol. 29, n. 1, pp. 51-59, 1996.
  50. [Reed:CVGIP:1993] T.R. Reed and J. M. H. Dubuf. A Review of Recent Texture Segmentation and Feature Extraction Techniques. CVGIP: Image Understanding, vol. 57, n. 3, pp. 359--372, 1993.
  51. [Freeman:1991:PAMI] Freeman, William T. and Adelson, Edward H.. The Design and Use of Steerable Filters. vol. 13, n. 9, pp. 891--906, 1991.
  52. [Jain:1991:PR] Jain, Anil K. and Farrokhnia, Farshid. Unsupervised Texture Segmentation using Gabor Filters. vol. 24, n. 12, pp. 1167--1186, 1991.
  53. [Koenderink:1987:BioCyb] Koenderink, J J and van Doom, A J. Representation of Local Geometry in the Visual System. Biological Cybernetics, vol. 55, n. 6, pp. 367--375, 1987.
  54. [Laws:1980:SPIE] Laws, K.I.. Rapid Texture Identification. SPIE, pp. 376-380, 1980.

Seleção de Características / Redução de Dimensionalidade

  1. [Boulesteix:2007:Bioinformatics] A.-L. Boulesteix and K. Strimmer. Partial Least Squares: A Versatile Tool for the Analysis of High-Dimensional Genomic Data. Briefings in Bioinformatics, vol. 8, n. 1, pp. 32-44, 2007.
  2. [Rosipal:2006:LNCS] R. Rosipal and N. Kramer. Overview and Recent Advances in Partial Least Squares. vol. 3940, pp. 34--51, 2006.
  3. [Guyon:2003:JMLR] Guyon, Isabelle and Elisseeff, André. An introduction to variable and feature selection. Journal of Machine Learning Research, vol. 3, pp. 1157--1182, 2003.

Detecção de Faces

  1. [Huang:2005:ICCV] Chang Huang and Haizhou Ai and Yuan Li and Shihong Lao. Vector Boosting for Rotation Invariant Multi-View Face Detection. 2005.
  2. [Viola:2001:CVPR] Paul Viola and Michael Jones. Rapid Object Detection using a Boosted Cascade of Simple Features. 2001.

Detecção de Pedestres

  1. [Dollar:2012:PAMI] Dollar, Piotr and Wojek, Christian and Schiele, Bernt and Perona, Pietro. Pedestrian Detection: An Evaluation of the State of the Art. vol. 34, n. 4, pp. 743--761, 2012.
  2. [Geronimo:2010:PAMI] Geronimo, David and Lopez, Antonio M. and Sappa, Angel D. and Graf, Thorsten. Survey of Pedestrian Detection for Advanced Driver Assistance Systems. vol. 32, n. 7, pp. 1239--1258, 2010.
  3. [Felzenszwalb:2012:PAMI] Pedro F. Felzenszwalb and Ross B. Girshick and David McAllester and Deva Ramanan. Object Detection with Discriminatively Trained Part-Based Models. vol. 32, pp. 1627--1645, 2010.
  4. [Enzweiler:2009:TPAMI] Enzweiler, Markus and Gavrila, Dariu M.. Monocular Pedestrian Detection: Survey and Experiments. vol. 31, n. 12, pp. 2179--2195, 2009.
  5. [Dollar:2009:CVPR] P. Dollar and C. Wojek and B. Schiele and P. Perona. Pedestrian Detection: A Benchmark. 2009.
  6. [Wojek:2008:DAGM] Wojek, Christian and Dorkó, Gyuri and Schulz, André and Schiele, Bernt. Sliding-Windows for Rapid Object Class Localization: A Parallel Technique. Proceedings of the 30th DAGM symposium on Pattern Recognition, pp. 71--81, 2008.
  7. [Tran:2008:NIPS] Duan Tran and David Forsyth. Configuration Estimates Improve Pedestrian Finding. pp. 1529--1536, 2008.
  8. [Lin:2008:ECCV] Z. Lin and L. S. Davis. A Pose-Invariant Descriptor for Human Detection and Segmentation. 2008.
  9. [Chen:2008:TIP] Yu-Ting Chen and Chu-Song Chen. Fast Human Detection Using a Novel Boosted Cascading Structure With Meta Stages. vol. 17, n. 8, pp. 1452-1464, 2008.
  10. [Wu:2008:CVPR] B. Wu and Nevatia, R.. Optimizing Discrimination-Efficiency Tradeoff in Integrating Heterogeneous Local Features for Object Detection. 2008.
  11. [Gandhi:2007:IEEETTS] Gandhi, T. and Trivedi, M. M.. Pedestrian Protection Systems: Issues, Survey, and Challenges. Trans. Intelligent Transportations Systems, vol. 8, n. 3, pp. 413--430, 2007.
  12. [Zhang:2007:ICCV] Wei Zhang and Gregory Zelinsky and Dimitris Samaras. Real-time Accurate Object Detection using Multiple Resolutions. pp. 1--8, 2007.
  13. [Tuzel:2007:CVPR] Tuzel, O. and Porikli, F. and Meer, P.. Human Detection via Classification on Riemannian Manifolds. 2007.
  14. [Zhu:2006:CVPR] Qiang Zhu and Mei-Chen Yeh and Kwang-Ting Cheng and Avidan, S.. Fast Human Detection Using a Cascade of Histograms of Oriented Gradients. pp. 1491-1498, 2006.
  15. [Seemann:2005:BMVC] Edgar Seemann and Bastian Leibe and Krystian Mikolajczyk and Bernt Schiele. An Evaluation of Local Shape-Based Features for Pedestrian Detection. 2005.
  16. [Leibe:2005:CVPR] Leibe, Bastian and Seemann, Edgar and Schiele, Bernt. Pedestrian Detection in Crowded Scenes. pp. 878--885, 2005.
  17. [Dalal:2005:CVPR] Dalal, N. and Triggs, B.. Histograms of Oriented Gradients for Human Detection. pp. 886--893, 2005.
  18. [Gavrila:2000:ECCV] Gavrila, Dariu. Pedestrian Detection from a Moving Vehicle. pp. 37--49, 2000.
  19. [Dollar:2010:BMVC] P. Dollar and S. Belongie and P. Perona. The Fastest Pedestrian Detector in the West.

Rastreamento

  1. [Breitenstein:2009:ICCV] Breitenstein, M.D. and Reichlin, F. and Leibe, B. and Koller-Meier, E. and Van Gool, L.. Robust Tracking-by-Detection using a Detector Confidence Particle Filter. pp. 1515--1522, 2009.
  2. [Gupta:2007:ICCV] Abhinav Gupta and Anurag Mittal and Larry S. Davis. COST: An Approach for Camera Selection and Multi-Object Inference Ordering in Dynamic Scenes. 2007.
  3. [Yilmaz:2006:ACMSurveys] Yilmaz, Alper and Javed, Omar and Shah, Mubarak. Object Tracking: A Survey. ACM Computing Surveys, vol. 38, n. 4, 2006.
  4. [Khan:2005:PAMI] Zia Khan and Tucker Balch and Frank Dellaert. MCMC-Based Particle Filtering for Tracking a Variable Number of Interacting Targets. vol. 27, pp. 1805-1918, 2005.
  5. [Zhou:2003:Expo] Shaohua Zhou and R. Chellappa and B. Moghaddam. Adaptive Visual Tracking and Recognition Using Particle Filters. IEEE International Conference on Multimedia and Expo, pp. 349-352, 2003.
  6. [Isard:2008:IJCV] Michael Isard and Andrew Blake. CONDENSATION - Conditional Density Propagation for Visual Tracking. vol. 29, pp. 5--28, 1998.

Reconhecimento de Faces

  1. [Wolf:2011:CVPR] L. Wolf and T. Hassner and I. Maoz. Face Recognition in Unconstrained Videos with Matched Background Similarity. pp. 529--534, 2011.
  2. [Chellappa:2010:Computer] Chellappa, R. and Sinha, P. and Phillips, P.J.. Face Recognition by Computers and Humans. Computer, vol. 43, n. 2, pp. 46--55, 2010.
  3. [Cao:2010:CVPR] Z. Cao and Q. Yin and X. Tang and J. Sun. Face Recognition with Learning-Based Descriptor. pp. 2707 -2714, 2010.
  4. [Zhang:2009:PR] X. Zhang and Y. Gao. Face Recognition Across Pose: A Review. vol. 42, n. 11, pp. 2876--2896, 2009.
  5. [Jafri:2009:JIPS] Rabia Jafri and Hamid R. Arabnia. A Survey of Face Recognition Techniques. Journal of Information Processing Systems, vol. 5, n. 2, pp. 41--68, 2009.
  6. [Kumar:2009:ICCV] N. Kumar and A. C. Berg and P. N. Belhumeur and S. K. Nayar. Attribute and Simile Classifiers for Face Verification. 2009.
  7. [Guillaumin:2009:ICCV] Guillaumin, M. and Verbeek, J. and Schmid, C.. Is that you? Metric Learning Approaches for Face Identification. pp. 498--505, 2009.
  8. [Wright:2009:PAMI] J. Wright and A. Y. Yang and A. Ganesh and S. S. Sastry and Y. Ma. Robust Face Recognition via Sparse Representation. pp. 210--227, 2009.
  9. [Zeng:BTAS:2009] Zhihong Zeng and Tianhong Fang and Shah, S. and Kakadiaris, I.A.. Local Feature Hashing for Face Recognition. 2009.
  10. [Holappa:2008:BTAS] Holappa, J. and Ahonen, T. and Pietikainen, M.. An Optimized Illumination Normalization Method for Face Recognition. 2008.
  11. [Andriluka:2008:CVPR] M. Andriluka and S. Roth and B. Schiele. People-tracking-by-detection and people-detection-by-tracking. 2008.
  12. [Lui:2008:ECCV] Lui, Y.M. and Beveridge, J.R. Grassmann Registration Manifolds for Face Recognition. vol. 2, pp. 44-57, 2008.
  13. [Albiol:2008:PRL] Albiol, A. and Monzo, D. and Martin, A. and Sastre, J. and Albiol, A.. Face Recognition Using HOG-EBGM. vol. 29, pp. 1537--1543, 2008.
  14. [Lui:2008:ECCV] Lui, Y.M. and Beveridge, J.R. Grassmann Registration Manifolds for Face Recognition. pp. 44--57, 2008.
  15. [Abate:2007:PRL] A. F. Abate and M. Nappi and D. Riccio and G. Sabatino. 2D and 3D Face Recognition: A Survey. vol. 28, n. 14, pp. 1885--1906, 2007.
  16. [Zhang:2007:TIP] Zhang, B. and Shan, S.G. and Chen, X.L. and Gao, W.. Histogram of Gabor Phase Patterns (HGPP): A Novel Object Representation Approach for Face Recognition. vol. 16, pp. 57--68, 2007.
  17. [Zou:2007:TIP] Zou, J. and Ji, Q. and Nagy, G.. A Comparative Study of Local Matching Approach for Face Recognition. vol. 16, pp. 2617--2628, 2007.
  18. [Tan:2007:AMFG:2] X. Tan and B. Triggs. Fusing Gabor and LBP Feature Sets for Kernel-Based Face Recognition. pp. 235-249, 2007.
  19. [Parsi:2007:BTAS] K. Parsi, B. and Lawson, E. and Baker, P.. Toward a Human-Like Approach to Face Recognition. 2007.
  20. [Luo:2007:ICASSP] J. Luo and Ma, Y. and Takikawa, E. and Lao, S. and Kawade, M. and Bao-Liang Lu. Person-Specific SIFT Features for Face Recognition. 2007.
  21. [Tolba:2006:IJSP] Tolba, A.S. and El-Baz, A.H. and El-Harby, A.A.. Face Recognition: A Literature Review. vol. 2, pp. 88--103, 2006.
  22. [Tan:2006:PR] Tan, X. and Chen, S. and Zhou, Z. and Zhang, F.. Face Recognition from a Single Image per Person: A Survey. vol. 39, pp. 1725--1745, 2006.
  23. [Liu:2006:PAMI] Liu, C.. Capitalize on Dimensionality Increasing Techniques for Improving Face Recognition Grand Challenge Performance. pp. 725--737, 2006.
  24. [Yuan:2005:CVPRW] Yuan, Q. and Thangali, A. and Sclaroff, S.. Face Identification by a Cascade of Rejection Classifiers. pp. 152--159, 2005.
  25. [Shih:2005:CVPRW] Shih, P. and Liu, C.. Evolving Effective Color Features for Improving FRGC Baseline Performance. pp. 156--163, 2005.
  26. [Delac:2005:IJIST] Delac, K. and Grgic, M. and Grgic, S.. Independent comparative study of PCA, ICA, and LDA on the FERET data set. International Journal of Imaging Systems and Technology, vol. 15, n. 5, pp. 252--260, 2005.
  27. [Phillips:2005:CVPR] Phillips, P. J. and Flynn, P. J. and Scruggs, T. and Bowyer, K. W. and C., Jin and Hoffman, K. and Marques, J. and Min, J. and Worek, W.. Overview of the Face Recognition Grand Challenge. pp. 947--954, 2005.
  28. [Torre:2005:CVPR] F. De la Torre and R. Gross and S. Baker and V. Kumar. Representational Oriented Component Analysis (ROCA) for Face Recognition with One Sample Image per Training Class. vol. 2, pp. 266 - 273, 2005.
  29. [Ekenel:2005:CVPRW] Ekenel, H. K. and Stiefelhagen, R.. A Generic Face Representation Approach for Local Appearance based Face Verification. 2005.
  30. [Hamsici:2005:CVPRW] O. C. Hamsici and A. M. Martynez. Evaluation of the Modelling of Local Areas and Errors of Localization in FRGC05. 2005.
  31. [Zhao:2003:ACMSurveys] Zhao, W. and Chellappa, R. and Phillips, P. J. and Rosenfeld, A.. Face Recognition: A Literature Survey. ACM Computing Surveys, vol. 35, n. 4, pp. 399--458, 2003.
  32. [Phillips:2003:TechRep] P. J. Phillips and P. J. Micheals and R. J. Blackburn and D. M. Tabassi and J. M. Bone. Face Recognition vendor test 2002: Evaluation Report. NIST, 2003.
  33. [Guo:2001:ICCVW] G.-D. Guo and H.-J. Zhang. Boosting for Fast Face Recognition. 2001.
  34. [Phillips:2000:PAMI] P. J. Phillips and H. Moon and S. A. Rizvi and P. J. Rauss. The FERET Evaluation Methodology for Face-Recognition Algorithms. vol. 22, pp. 1090-1104, 2000.
  35. [Wiskott:1997:PAMI] Wiskott, L. and Fellous, J.M. and Kuiger, N. and von der Malsburg, C.. Face Recognition by Elastic Bunch Graph Matching. vol. 19, pp. 775--779, 1997.
  36. [Turk:1991:ER:1326887.1326894] Turk, Matthew and Pentland, Alex. Eigenfaces for Recognition. J. Cognitive Neuroscience, vol. 3, pp. 71--86, 1991.
  37. [Ahonen:2004:ECCV] Ahonen, T. and Hadid, A. and Pietikainen, M.. Face Recognition with Local Binary Patterns. 35, pp. 469--481,
  38. [Liu:2007:AMFG] Liu, J. and Chen, S. and Zhou, Z. and Tan, X.. Single Image Subspace for Face Recognition. pp. 205--219,
  39. [Zhang:2005:ICCV] Zhang, W. and Shan, S. and Gao, W. and Chen, X. and Zhang, H.. Local Gabor Binary Pattern Histogram Sequence (LGBPHS): A Novel Non-Statistical Model for Face Representation and Recognition. pp. 786--791,
  40. [Aggarwal:2005:CVPRW] Aggarwal,G. and Biswas, S. and Chellappa, R.. UMD Experiments with FRGC Data. pp. 172--178,
  41. [Tan:2007:AMFG] Tan, X. and Triggs, B.. Enhanced Local Texture Feature Sets for Face Recognition under Difficult Lighting Conditions. pp. 168--182,
  42. [Wang:2004:FG] Wang, H. and Li, S. Z. and Wang, Y.. Face Recognition under Varying Lighting Conditions using Self Quotient Image. pp. 819--824,
  43. [Mian:2006:ECCV] A. Mian and M. Bennamoun and R. Owens. 2D and 3D Multimodal Hybrid Face Recognition. vol. 3, pp. 344--355,
  44. [Aggarwal:2005:CVPRW] Aggarwal,G. and Biswas, S. and Chellappa, R.. UMD Experiments with FRGC Data. pp. 172--178,

Reidentificação de Pessoas

  1. [Shitrit:2011:ICCV] Shitrit, Horesh Ben and Berclaz, Jerome and Fleuret, Francois and Fua, Pascal. Tracking Multiple People Under Global Appearance Constraints. 2011.
  2. [Cai:2010:VS] Cai, Yinghao and Pietikainen, Matti. Person Re-identification Based on Global Color Context. 2010.
  3. [Hamdoun:2008:ECCV] Hamdoun, Omar and Moutarde, Fabien and Stanciulescu, Bogdan and Steux, Bruno. Interest Points Harvesting in Video Sequences for Efficient Person Identification. 2008.
  4. [Tu:2007:DSS] Tu, Peter Henry and Doretto, Gianfranco and Krahnstoever, Nils O. and Perera, a.G. Amitha and Wheeler, Frederick W. and Liu, Xiaoming and Rittscher, Jens and Sebastian, Thomas B. and Yu, Ting and Harding, Kevin G.. An Intelligent Video Framework for Homeland Protection. Defence and Security Symposium, 2007.
  5. [Gandhi:2007:MVA] Gandhi, Tarak and Trivedi, Mohan Manubhai. Person Tracking and Reidentification : Introducing Panoramic Appearance Map ( PAM ) for Feature Representation. Machine Vision and Applications, vol. 18, n. 3, pp. 207--220, 2007.
  6. [Gheissari:2006:CVPR] Gheissari, Niloofar and Sebastian, Thomas B. and Tu, Peter Henry and Rittscher, Jens and Hartley, Richard. Person Reidentification Using Spatiotemporal Appearance. 2006.

Estimação de Pose

  1. [Sundaresan:2009:TIP] Sundaresan, Aravind and Chellappa, Rama. Multicamera Tracking of Articulated Human Motion Using Shape and Motion Cues. vol. 18, pp. 2114--2126, 2009.
  2. [Poppe:2007:CVIU] Poppe, Ronald. Vision-Based Human Motion Analysis: An Overview. vol. 108, pp. 4--18, 2007.
  3. [Roberts:2006:IVC] Timothy J. Roberts and Stephen J. McKenna and Ian W. Ricketts. Human Tracking Using 3D Surface Colour Distributions. vol. 24, n. 12, pp. 1332-1342, 2006.
  4. [Howe:2004:CVPRW] Howe, Nicholas R.. Silhouette Lookup for Automatic Pose Tracking. pp. 15--22, 2004.
  5. [Sminchisescu:2003:IJRS] C. Sminchisescu and Bill Triggs. Estimating Articulated Human Motion with Covariance Scaled Sampling. International Journal of Robotics Research, vol. 22, n. 6, pp. 371--391, 2003.
  6. [Wachtert:1997:IEEEWM] Wachtert, S. and Nageltt, H.-H.. Tracking of Persons in Monocular Image Sequences. IEEE Workshop on Motion of Non-Rigid and Articulated Objects, 1997.

Reconhecimento de Ações

  1. [Weinland:2011:CVIU] Weinland, Daniel and Ronfard, Remi and Boyer, Edmond. A survey of vision-based methods for action representation, segmentation and recognition. vol. 115, n. 2, pp. 224--241, 2011.
  2. [Poppe:2010:IVC] Poppe, R.. A Survey on Vision-Based Human Action Recognition. vol. 28, n. 6, pp. 976--990, 2010.
  3. [Junsong:2009:CVPR] Junsong Yuan and Zicheng Liu and Ying Wu. Discriminative Subvolume Search for Efficient Action Detection. pp. 2442-2449, 2009.
  4. [Liu:2009:CVPR] [UCF YouTube Action Dataset] Jingen Liu and Jiebo Luo and Mubarak Shah. Recognizing Realistic Actions from Videos in the Wild. 2009.
  5. [Liu:2008:CVPR] Jingen Liu and Mubarak Shah. Learning human actions via information maximization. 2008.
  6. [Niebles:2008:IJCV] Niebles, Juan Carlos and Wang, Hongcheng and Fei-Fei, Li. Unsupervised Learning of Human Action Categories Using Spatial-Temporal Words. vol. 79, n. 3, pp. 299--318, 2008.
  7. [Yang:2008:CVPR] Liu Yang and Rong Jin and Rahul Sukthankar and Frederic Jurie. Unifying Discriminative Visual Codebook Generation with Classifier Training for Object Category Recognition. 2008.
  8. [Laptev:2008:CVPR] [(Hollywood Human Actions Dataset)] Ivan Laptev and Marcin Marszalek and Cordelia Schmid and Benjamin Rozenfeld. Learning Realistic Human Actions from Movies. 2008.
  9. [Rodriguez:2008:CVPR] [(UCF Sports Action Dataset)] Mikel D. Rodriguez and Javed Ahmed and Mubarak Shah. Action MACH: A Spatio-Temporal Maximum Average Correlation Height Filter for Action Recognition. 2008.
  10. [Moosmann:2006:NIPS] Frank Moosmann and Bill Triggs and Frédéric Jurie. Fast Discriminative Visual Codebooks using Randomized Clustering Forests. 2006.
  11. [Bilenko:2004:ICML] Bilenko, Mikhail and Basu, Sugato and Mooney, Raymond J.. Integrating Constraints and Metric Learning in Semi-Supervised Clustering. 2004.
  12. [Schuldt:2004:ICPR] [(KTH Action Database)] C. Schuldt and I. Laptev and B. Caputo. Recognizing Human Actions: A Local SVM Approach. pp. 32--36, 2004.

Reconhecimento de Atividades

  1. [Aggarwal:2011:ACMSurveys] Aggarwal, J.K. and Ryoo, M.S.. Human Activity Analysis: A Review. ACM Comput. Surv., vol. 43, n. 3, pp. 1--43, 2011.
  2. [Ko:2008:AIPRW] T. Ko. A Survey on Behavior Analysis in Video Surveillance for Homeland Security Applications. IEEE Applied Imagery Pattern Recognition Workshop, pp. 1--8, 2008.
  3. [Turaga:2008:TCSVT] Pavan K. Turaga and Rama Chellappa and V. S. Subrahmanian and Octavian Udrea. Machine Recognition of Human Activities: A Survey. vol. 18, n. 11, pp. 1473--1488, 2008.