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.关于机器视觉与机器学习的大量资源及书籍 可在线阅读(top@ExBot整理)

2012年11月29日 Machine Learning 暂无评论 阅读 1 次

CVonline: Vision Related Books including Online Books and Book Support Sites


We have tried to list all recent books that we know about that are relevant to computer vision and image processing. The books are listed under:

Online Books

  1. M. Aiello, Spatial Reasoning: Theory and Practice. (2002) ILLC Dissertations series 2002-2, pages 224. ISBN 90-5776-079-7.
  2. D.H. Ballard, C.M. Brown; Computer Vision, Prentice-Hall Inc New Jersey, 1982, ISBN 0-13-165316-4 .
  3. B.G. Batchelor, P.F. Whelan; Intelligent Vision Systems for Industry, Springer-Verlag, 1997, ISBN 3-540-19969-1.
  4. O. Bimber, R. Raskar. Spatial Augmented Reality: Merging Real and Virtual Worlds, A K Peters LTD, 2005, ISBN: 1-56881-230-2.
  5. A. Blake, M. Isard; Active contours, Springer, London, 1998, ISBN 3540762175.
  6. A. Blake, A. Zisserman; Visual reconstruction , MIT Press, 1987, ISBN 0262022710.
  7. S. Behnke; Hierarchical neural networks for image interpretation , Springer, 2003, ISBN 3540407227.
  8. L. F. Costa, R.M. Cesar-Jr; Shape Analysis and Classification: Theory and Practice (if you have ENGnetBase access), Support site, 2nd Edition, CRC Press, 2009, ISBN 978-0849379291.
  9. S. Dance, Z. Q. Liu, T. M. Caelli; Picture Interpretation: A Symbolic Approach, World Scientific, 1995, ISBN 981-02-2402-8. Part 1, Part 2
  10. K. Delac, M. Grgic, M. S. Bartlett;Recent Advances in Face Recognition, IN-TECH, December 2008, ISBN 978-953-7619-34-3.
  11. S. Edelman; (If you have CogNet access): Representation and Recognition in Vision, MIT Press, 1999, ISBN 0-262-05057-9.
  12. M. A. Fischler, O. Firschein; Intelligence: The Eye, the Brain and the Computer, Addison-Wesley, 1987, ISBN: 0201120011
  13. R. B. Fisher; From Surfaces to Objects: Computer Vision and Three Dimensional Scene Analysis , John Wiley and Sons Ltd, 1989, ISBN 0471923443.
  14. R. B. Fisher, S. Perkins, A. Walker, E. Wolfart; Hypermedia Image Processing Reference, John Wiley and Sons, 1996,
  15. L. van Hemmen, J. Cowan, E. Domany (Eds); Models of Neural Networks IVC: Early Vision and Attention, Springer, ISBN 0-387-95105-9, 2001.
  16. D. H. Hubel; Eye, Brain and Vision , Scientific American Library, 1988, ISBN 0-7167-5020-1.
  17. A. K. Jain, R. C. Dubes; Algorithms for Clustering Data, Prentice-Hall , 1988, ISBN 0-13-022278-X.
  18. R. Jain, R. Kasturi, B.G. Schunck; Machine Vision, McGraw-Hill, 1995, ISBN 0-07-113407-7, Reprint 4 or Higher.
  19. P. K.Kaiser; The Joy of Visual Perception , Online book, 1996.
  20. A. Kak, M. Slaney. Principles of Computerized Tomographic Imaging, Society of Industrial and Applied Mathematics, 2001.
  21. R. Lenz; Group Theoretic Methods in Image Processing. Springer-Verlag, LNCS 413, 1990, ISBN: 3-540-52290-5.
  22. L. O'Gorman, R. Kasturi; Document Image Analysis, IEEE Computer Society Press, 1997, ISBN 0-8186-7802-X, Library of Congress Number 97-17283.
  23. D. Mackay; Information Theory, Inference, and Learning Algorithms, Cambridge University Press, 2003. ISBN 0521642981
  24. H.A. Mallot, J.S. Allen; (If you have CogNet access): Computational Vision: Information Processing in Perception and Visual Behavior, MIT Press, 2000, ISBN 0-262-13381-4.
  25. J. E. W. Mayhew, J. P. Frisby; 3D Model Recognition From Stereoscopic Cues, MIT Press, 1991, ISBN 0-262-13243-5.
  26. R. Nevatia; Machine Perception, Prentice-Hall, 1982, ISBN 0-13-541904-2.
  27. I. Overington; Computer Vision, A Unified, Biologically-Inspired Approach, North Holland, 1992, ISBN 0-444-88972-8.
  28. I. Overington; Vision and Acquisition, Pentech Press, London, 1976.
  29. X. Papademetris; Introduction to Programming for Image Analysis with VTK, course notes, 2006.
  30. D. Phillips; Image Processing in C: Analyzing and Enhancing Digital Images, RandD Publications, 1994.
  31. W. H. Press, B. P. Flannery, S. A. Teukolsky , W. T. Vetterling; Numerical Recipes in C, Cambridge University Press, 1993, ISBN 0521431085.
  32. S.W. Smith; The Scientist and Engineer's Guide to Digital Signal Processing, California Technical Publishing, 1997, ISBN 0966017633.
  33. J. L. Starck, F. Murtagh; Astronomical Image and Data Analysis, Springer, 1st edn., 2002, ISBN 3-540-42885-2.
  34. J. L. Starck, F. Murtagh, A. Bijaoui; Image Processing and Data Analysis: The Multiscale Approach, Cambridge University Press, 1998. ISBN-13: 9780521599146 | ISBN-10: 0521599148.
  35. K. Sugihara; Machine Interpretation of Line Drawings, MIT Press, 1986, ISBN 0-262-19254-3.
  36. R. Szeliski; Computer Vision: Algorithms and Applications,draft, 2008.
  37. D. Vernon; Machine Vision : Automated Visual Inspection and Robot Vision, Prentice Hall, 1991, ISBN 0-13-543398-3.
  38. T. Watanabe; (If you have CogNet access): High-Level Motion Processing, MIT Press, 1998, ISBN 0-262-23195-6.
  39. E.W. Weisstein; Mathworld , Online book.
  40. I.T. Young, J.J. Gerbrands, L.J. van Vliet; Image Processing Fundamentals , Online book.

Online Subscription Books

  1. T. Acharya, Image processing: principles and application (subscription site), John Wiley and Sons, 2005, print ISBN: 0471719986, online ISBN: 0471745790.
  2. E. de Aguiar; Animation and Performance Capture Using Digitized Models. Springer, 2010, ISBN: 978-3-642-10315-5.
  3. M. Aiello, I. E. Pratt-Hartmann, J. F. A. K. van Benthem (Eds.), Handbook of Spatial Logics, Springer, 2007, ISBN: 978-1-4020-5586-7.
  4. S. Aja-Fernandez, R. de Luis Garcia, D. Tao, X. Li (Eds); Tensors in Image Processing and Computer Vision, Springer, 2009, ISBN 978-84882-298-6.
  5. I. Amidror; The Theory of the Moiré Phenomenon, 2nd Ed, Springer, 2009, ISBN 978-1-84882-180-4.
  6. G. Aubert, P. Kornprobst; Mathematical problems in image processing: Partial Differential Equations and the Calculus of Variations, support, Springer, Applied Mathematical Sciences, Vol 147, 2006 (2nd ed).
  7. P. Azad; Visual Perception for Manipulation and Imitation in Humanoid Robots, Springer, 2009, ISBN: 978-3-642-04228-7.
  8. E. Bayro Corrochano; Handbook of Geometric Computing with Applications in Pattern Recognition, Computer Vision, Neurocomputing and Robotics, Springer Verlag, 2005, ISBN 3-540-20595-0.
  9. E. Bayro Corrochano, G. Scheuermann (Eds); Geometric Algebra Computing, Springer, 2010, ISBN 978-1-84996-107-3.
  10. G. Bebis, Advances in Visual Computing, Springer LNCS 4291/4292, 2005, ISBN: 3-540-30750-8.
  11. J. Bezdek, Fuzzy Models and Algorithms for Pattern Recognition and Image Processing, Springer, 2005, ISBN:0-387-24515-4.
  12. B. Bhanu, I. Pavlidis; Computer Vision Beyond the Visible Spectrum, Springer Verlag, 2004, ISBN 1-85233-604-8.
  13. J. Bigun; Vision with Direction, Springer, 2006.
  14. S. Biswas; B. C. Lovell, Bezier and Splines in Image Processing and Machine Vision, Springer, 2008, ISBN 978-1-84628-956-9.
  15. R. Boehme; Advanced Statistical Steganalysis, Springer, 2010, ISBN: 978-3-642-14312-0.
  16. A. Bouridane; Imaging for Forensics and Security, Springer, 2009, ISBN 978-0-387-09531-8.
  17. A. M. Bronstein, M. M. Bronstein, R. Kimmel; Numerical Geometry of Non-Rigid Shapes, Springer, 2009, ISBN: 978-0-387-73300-5.
  18. H. Bunke, U.-V. Marti, Hidden Markov models: Applications in Computer Vision (subscription site), World Scientific, 2001, ISBN: 981-02-4564-5.
  19. W. Burger, M. Burge; Digitale Bildverarbeitung: Eine Einfurung mit Java und ImageJ (Support Website), Springer-Verlag, Berlin Heidelberg, 2005, ISBN 3-540-21465-8. English version: Digital Image Processing - An Algorithmic Introduction Using Java, Springer, 2008, ISBN: 978-1-84628-379-6.
  20. F. Camastra, A. Vinciarelli. Machine Learning for Audio, Image and Video Analysis, Springer, 2008, ISBN 978-1-84800-006-3.
  21. F. Cao, J.-L. Lisani, J.-M. Morel, P. Musé, F. Sur; A Theory of Shape Identification, Springer Lecture Notes in Mathematics, Vol. 1948, 2008, ISBN: 978-3-540-68480-0.
  22. S. Chaudhuri, J. Manjunath; Motion-Free Super-Resolution; Springer, 2005. SBN: 0-387-25890-6
  23. R. Cipolla, S. Battiato, G. M. Farinella (Eds.); Computer Vision - Detection, Recognition and Reconstruction, Springer, 2010, ISBN: 978-3-642-12847-9.
  24. K. Daniilidis, R. Klette (Eds). Imaging Beyond the Pinhole Camera. Springer, 2006. ISBN 978-1-4020-4893-7.
  25. R. Davies, C. Twining, C. Taylor, Statistical Models of Shape, Springer, 2008, ISBN: 978-1-84800-137-4.
  26. Z. Deng, U. Neumann (Eds), Data-Driven 3D Facial Animation, Springer, 2008, ISBN: 978-1-84628-907-1 .
  27. E. D. Dickmanns; Dynamic Vision for Perception and Control of Motion, Springer, 2007, ISBN: 978-1-84628-637-7.
  28. A. Duchowski; Eye Tracking Methodology: Theory and Practice, Springer, 2nd. Ed., 2007, ISBN 978-1-84628-608-7.
  29. F. Escolano, P. Suau, B. Bonev; Information Theory in Computer Vision and Pattern Recognition, Springer, 2009, ISBN: 978-1-84882-296-2.
  30. P. Favaro, S. Soatto. 3-D Shape Estimation and Image Restoration. Springer, 2007, ISBN 978-1-84628-176-1.
  31. G. A. Fink. Markov Models for Pattern Recognition, Springer 2008, ISBN: 978-3-540-71766-9.
  32. S. Frintrop, VOCUS: A Visual Attention System for Object Detection and Goal-directed Search, Support site, Springer, 2005, ISBN: 3-540-32759-2.
  33. A. Gagalowicz, Computer Analysis of Images and Patterns, Springer-Verlag, 2005, ISBN: 978-3-540-28969-2.
  34. D. C. Gibbon, Z. Liu; Introduction to Video Search Engines, Springer, 2008, ISBN: 978-3-540-79336-6.
  35. V. Govindaraju, S. Setlur (Eds.); Guide to OCR for Indic Scripts, Springer, 2009, ISBN: 978-1-84800-329-3.
  36. J. A. Gutierrez, B. S. R. Armstrong, Precision Landmark Location for Machine Vision and Photogrammetry, Springer, 2008, ISBN 978-1-84628-912-5.
  37. R. I. Hammoud (Ed.); Augmented Vision Perception in Infrared, 2009, ISBN: 978-1-84800-276-0.
  38. R. I. Hammoud (Ed.); Passive Eye Monitoring, Algorithms, Applications and Experiments, Springer, 2008, ISBN 978-3-540-75411-4.
  39. R. I. Hammoud, B. R. Abidi, M. A. Abidi (Eds.); Face Biometrics for Personal Identification, Springer, 2007, ISBN 978-3-540-49344-0.
  40. Y. Hasegawa; Algebraically Approximate and Noisy Realization of Discrete-Time Systems and Digital Images, Springer, 2009, ISBN: 978-3-642-03216-5.
  41. G. T. Herman; Fundamentals of Computerized Tomography, 2nd ed., Springer, 2009, ISBN: 978-1-85233-617-2.
  42. B. Hoefflinger (Ed), High-Dynamic-Range (HDR) Vision. Springer, 2007. ISBN 978-3-540-44432-9.
  43. R. W. G. Hunt, The reproduction of colour (subscription site), John Wiley & Sons,2004, ISBN: 0-470-02425-9.
  44. A. Hyvdrinen, J. Hurri, P. O. Hoyer; Natural Image Statistics: A Probabilistic Approach to Early Computational Vision, Springer, Series: Computational Imaging and Vision, Vol. 39, ISBN: 978-1-84882-490-4, online: 978-1-84882-491-1, 2009.
  45. A. Inselberg; Parallel Coordinates, Springer, 2009, ISBN: 978-0-387-21507-5.
  46. B. Jahne, Digital Image Processing, Support, Springer Verlag, 2005, ISBN: 3-540-67754-2.
  47. A. K. Jain, P. Flynn, A. A. Ross, Handbook of Biometrics, Springer, 2008, ISBN 978-0-387-71040-2.
  48. B. Jasani, M. Pesaresi, S. Schneiderbauer, G. Zeug (Eds); Remote Sensing from Space, Springer, Springer, 2009, ISBN: 978-1-4020-8483-6.
  49. M. Kamel, Image Analysis and Recognition, Springer, LNCS 3212, 2004, ISBN:978-3-540-23240-7.
  50. M. Kipp, j.C. Martin, P. Paggio, D. Heylen (Eds); Multimodal Corpora, Springer, 2009, ISBN: 978-3-642-04792-3.
  51. B. Kisacanin, Real-Time Vision for Human-Computer Interaction, Springer, 2005, ISBN: 0-387-27697-1.
  52. B. Kisacanin, S. S. Bhattacharyya, S. Chai (Eds.); Embedded Computer Vision Springer, 2009, ISBN: 978-1-84800-303-3.
  53. R. Klette, R. Kozera, L. Noakes, J. Weickert. Geometric Properties for Incomplete Data. Springer, 2006. ISBN 1-4020-3857-7
  54. A. Koschan, M. Pollefeys, M. Abidi (Eds.), 3D Imaging for Safety and Security, Springer, 2007, ISBN: 978-1-4020-6181-3.
  55. W. K. Leow, Image and Video Retrieval, Springer, 2005, ISBN: 978-3-540-27858-0.
  56. M. Leyton; The Structure of Paintings, Springer-Verlag, 2006, ISBN 3-211-35739-4
  57. S. Z. Li, A. Jain; Handbook of Face Recognition (Support Website), Springer-Verlag New York Inc, January 2005, ISBN: 038740595X.
  58. W. J. MacLean (Ed); Spatial Coherence for Visual Motion Analysis, Springer LNCS 3667, 2006, ISBN: 3-540-32533-6.
  59. N. Magnenat-Thalmann, J. J. Zhang, D. D. Feng (Eds); Recent Advances in the 3D Physiological Human, Springer, 2009, ISBN: 978-1-84882-564-2.
  60. D. Maltoni, D. Maio, A. K. Jain, S. Prabhakar; Handbook of Fingerprint Recognition, (Support Website), Springer-Verlag New York Inc., 2nd edition, 2009, ISBN: 978-1-84882-253-5 (Print) 978-1-84882-254-2 (Online).
  61. S. Marinai, H. Fujisawa (Eds.); Machine Learning in Document Analysis and Recognition, Springer, 2008, ISBN: 978-3-540-76279-9.
  62. J. P. Marques de Sa; Pattern Recognition: Concepts, Methods and Applications, Springer, ISBN 3-540-42297-8.
  63. J. S. Marques, Pattern Recognition and Image Analysis, Springer LNCS 3523, 2005, ISBN:978-3-540-26154-4.
  64. L. Middleton, J. Sivaswarmy; Hexagonal Image Processing: A Practical Approach, Springer-Verlag UK, August 2005, ISBN: 1852339144.
  65. R. Miikkulainen, J. A. Bednar, Y. Choe, J. Sirosh; Computational Maps in the Visual Cortex, Support Website, New York: Springer 2005, ISBN: 0-387-22024-0.
  66. H. B. Mitchell, Multi-Sensor Data Fusion - An Introduction, Springer, 2007, ISBN: 978-3-540-71463-7.
  67. M. Nachtegael; D. Van der Weken; E. E. Kerre, W. Philips; Soft Computing in Image Processing, Springer, 2006, ISBN 978-3-540-38232-4.
  68. S. Nikiel; Iterated Function Systems for Real-Time Image Synthesis, Springer, 2007, ISBN 978-1-84628-685-8.
  69. M. Nitzberg, D. Mumford, T. Shiota; Filtering, segmentation, and depth, Springer-Verlag, 1993, ISBN 0387564845.
  70. M.S. Nixon, A.S. Aguado ; Feature Extraction and Image Processing (Support Website), online demos,(online with subscription to Referex in Engineering Village), Newnes, 2002, ISBN 0750650788.
  71. S.J. Osher, N. Paragios; Geometric Level Set Methods in Imaging, Vision and Graphics, Support website, Springer-Verlag New York Inc, 2003.,
  72. N. Paragios, Y. Chen, O. Faugeras; Mathematical Models in Computer Vision: The Handbook, Support website, Springer, 2006. ISBN 0-387-26371-3.
  73. J. Pauli; Learning-Based Robot Vision, Springer-Verlag, 2001, ISBN 3-540-42108-4.
  74. K. D. Paulsen, Alternative Breast Imaging, Springer, 2005, ISBN: 0-387-23363-6.
  75. M. Petrou, P. G. Sevilla, Image Processing: Dealing with Texture (subscription site), John Wiley and Sons, 2006, Online ISBN: 047003534X, ISBN: 0-470-02628-6.
  76. J. Ponce, M. Hebert, C. Schmid, A. Zisserman (Eds). Toward Category-Level Object Recognition, Support website, Springer LNCS, Vol. 4170, 2006. ISBN 978-3-540-68794-8.
  77. L. Quan; Image-Based Modeling, Springer, 2010, ISBN: 978-1-4419-6678-0.
  78. S. Qureshi, Embedded Image Processing on the TMS320C6000 DSP, Support website, Springer 2006, ISBN: 0-387-25280-0.
  79. N. Ratha, R. Bolle; Automatic Fingerprint Recognition Systems, Springer-Verlag New York Inc., June 2003, ISBN: 0387955933.
  80. B. Rosenhahn, R. Klette, D. Metaxas (Eds.); Human Motion Understanding, Modelling, Capture, and Animation Springer, 2008, ISBN: 978-1-4020-6692-4.
  81. W. Rucklidge; Efficient visual recognition using the Hausdorff distance, Springer, 1996, ISBN 3540619933.
  82. D. Scharstein; View Synthesis Using Stereo Vision, Springer-Verlag Berlin and Heidelberg GmbH and Co. K, 1999, ISBN 354066159X.
  83. O. Scherzer, M. Grasmair, H. Grossauer, M. Haltmeier, F. Lenzen; Variational Methods in Imaging, Springer Series: Applied Mathematical Sciences, Vol. 167, 2009, ISBN: 978-0-387-30931-6.
  84. D. Schonfeld, C. Shan, D. Tao, L. Wang (Eds.); Video Search and Mining, Springer, 2010, ISBN 978-3-642-12899-8.
  85. M. E. Schuckers; Computational Methods in Biometric Authentication, Springer, 2010, ISBN 978-1-84996-201-8.
  86. N. Sebe, I. Cohen, A. Garg, T. S. Huang; Machine Learning in Computer Vision, Springer, 2005, ISBN: 1402032749.
  87. A. Senior (Ed); Protecting Privacy in Video Surveillance, Springer, 2009, ISBN: 978-1-84882-300-6.
  88. K. Siddiqi, S. Pizer (eds); Medial Representations, Springer, 2009, ISBN 978-1-4020-8657-1.
  89. S. Singh, Pattern Recognition and Image Analysis, Springer LNCS 3687, 2005, ISBN: 3-540-28833-3.
  90. M.A. Sutton, J.-J. Orteu, H. Schreier; Image Correlation for Shape, Motion and Deformation Measurements, Springer, 2009, ISBN: 978-0-387-78746-6 (Print) 978-0-387-78747-3 (Online).
  91. X-C. Tai, K-A. Lie, T. F. Chan, S. Osher, Image Processing Based on Partial Differential Equations, Springer, 2006, ISBN 978-3-540-33266-4.
  92. G. Taylor, L. Kleeman; Visual Perception and Robotic Manipulation, Springer, 2006, ISBN 3-540-33454-8.
  93. M. Tistarelli, S. Z. Li, R. Chellappa (Eds); Handbook of Remote Biometrics for Surveillance and Security, Series: Advances in Pattern Recognition, Springer, 2009, ISBN: 978-1-84882-384-6.
  94. M. Toennis; Augmented Reality (in German), Springer, 2010, ISBN: 978-3-642-14178-2.
  95. J. Toriwaki, H. Yoshida; Fundamentals of Three-dimensional Digital Image Processing, Springer, 2009, ISBN: 978-1-84800-172-5 (Print) 978-1-84800-173-2 (Online).
  96. M. A. Trieber; An Introduction to Object Recognition, Springer, 2010, ISBN 978-1-84996-234-6.
  97. C. Wohler; 3D Computer Vision, Springer, 2009, ISBN 978-3-642-01731-5.
  98. J. Vince; Geometry for Computer Graphics: Formulae, Examples and Proofs, Springer-Verlag UK, October 2004, ISBN: 1852338342.
  99. N. J. Wade; Perception and Illusion. Historical Perspectives. New York: Springer, 2005.
  100. Z. Wen, T. S. Huang; 3D Face Processing: Modeling, Analysis and Synthesis, Springer International Series in Video Computing, June 1, 2004, ISBN: 1402080476.
  101. N. Zheng, J. Xue; Statistical Learning and Pattern Analysis for Image and Video Processing, Springer, 2009, ISBN: 978-1-84882-311-2.
  102. S. K. Zhou, R. Chellappa, W. Zhao, Unconstrained Face Recognition, Springer, 2006, ISBN: 0-387-26407-8.

Book Support Sites

  1. M. de Berg, O. Cheong, M. van Kreveld, M. Overmars; Computational Geometry: Algorithms and Applications (Support Website), 3rd Edition, Springer-Verlag Berlin and Heidelberg GmbH & Co., March 2008, ISBN: 978-3-540-77973-5.
  2. R. Brunelli; Template Matching Techniques in Computer Vision: Theory and Practice, Wiley, 2009, ISBN: 978-0-470-51706-2.
  3. H. Bunke, P. S-P Wang; Handbook of Character Recognition and Document Image Analysis, World Scientific, 1997, ISBN: 981-02-2270-x.
  4. C.-I. Chang; Hyperspectral Imaging: Techniques for Spectral Detection and Classification, Kluwer Academic, 2004, ISBN:0-306-47483-2.
  5. Y. Chen, J. Z. Wang; Machine Learning and Statistical Modeling Approaches to Image Retrieval, Kluwer Academic Publishers, Dordrecht, June 2004.
  6. E. R. Davies, Image Processing For The Food Industry, World Scientific, 2000, ISBN: 981-02-4022-8 .
  7. E. R. Davies; Machine Vision: Theory Algorithms Practicalities. Third Edition, Morgan Kaufman , 2005, ISBN 0-12-206093-8.
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  11. R.O. Duda, P.E. Hart, D.G. Stork; Pattern Classification (Support Website), John Wiley and Sons, 2001, ISBN 0471056693.
  12. G. Dudek and M. Jenkin; Computational Principles of Mobile Robotics (Support Website),Cambridge University Press, Cambridge England, 2000, ISBN 052156876-5
  13. O. Faugeras, Q.T. Luong, T. Papadopoulo; The Geometry of Multiple Images (Support Website), MIT Press, 2001, ISBN 0262062208.
  14. R. Fisher, K. Dawson-Howe, A. W. Fitzgibbon, C. Robertson, E. Trucco; Dictionary of Computer Vision and Image Processing, (Support Website), John Wiley and Sons, 2005, ISBN 0-470-01526-8.
  15. J. Flusser, T. Suk, B. Zitova. Moment and Moment Invariants in Pattern Recognition, (Support Website), John Wiley and Sons, 2009, ISBN 978-0-470-69987-4.
  16. D. Forsyth and J. Ponce; Computer Vision a Modern Approach (Support Website), Prentice Hall, 2003, ISBN 0-13-085198-1.
  17. A. Ghosh, S. K. Pal, Soft Computing Approach to Pattern Recognition and Image Processing, World Scientific, 2002, ISBN: 981-238-251-8.
  18. S. Gong, S.J. McKenna, A. Psarrou; Dynamic Vision: From Images to Face Recognition (Support Website), Imperial College Press, 2000, ISBN 1860941818.
  19. R.C. Gonzalez, R.E. Woods; Digital Image Processing (2nd edition), Prentice Hall, 2002, ISBN 0201180758.
  20. R.C. Gonzalez, R.E. Woods, S.L. Eddins; Digital Image Processing Using MATLAB, 2nd edition, Prentice Hall, 2009, ISBN 9780982085400.
  21. A. A. Goshtasby; 2-D and 3-D Image Registration for Medical, Remote Sensing, and Industrial Applications, Wiley, 2005.
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  23. M. Grgic, K. Delac, M. Ghanbari; Recent Advances in Multimedia Signal Processing and Communications, Springer, 2009, ISBN 978-3-642-02899-1.
  24. C. Guy, Introduction to the Principles of Medical Imaging, World Scientific, 2005, ISBN: 1860945023.
  25. R. Hartley, A. Zisserman; Multiple View Geometry in Computer Vision (Support Website), Cambridge University Press, 2000, ISBN 0-521-62304-9.
  26. B.K.P. Horn; Robot Vision (Support Website), McGraw Hill, 1986, ISBN 0-07-030349-5.
  27. R. Klette, P. Zamperon; Handbook of Image Processing Operators (Support Website), John Wiley and Sons, 1996, ISBN 0-471-95642-2.
  28. R. Klette, K. Schluns, A. Koschan; Computer Vision: Three-Dimensional Data from Images (Support Website), Springer-Verlag Singapore Pte. Ltd., 2001, ISBN 9813083719.
  29. R. Klette, F. Sloboda, A. Rosenfeld; Advances in Digital and Computational Geometry (Support Website), Springer, 1998,
  30. R. Klette, Azriel Rosenfeld; Digital Geometry Geometric Methods for Digital Picture Analysis (Support Website), Morgan Kaufmann, 2004, ISBN 1558608613.
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  35. Y. Ma, S. Soatto, J.Kosecka, S.S. Sastry; An Invitation to 3-D Vision From Images to Geometric Models (Support Website), Springer-Verlag, 2004, ISBN 0-387-00893-4.
  36. F. Mokhtarian, M. Bober; Curvature Scale Space Representation: Theory, Applications, and MPEG-7 Standardization (Support Website), Springer, Computational Imaging and Vision Series, Vol. 25, 2003, ISBN: 1-4020-1233-0.
  37. F. Nielsen; Visual Computing: Geometry, Graphics, and Vision, Charles River Media, Thomson Delmar Learning, August 2005, ISBN: 1-58450-427-7.
  38. B. D. Ripley; Pattern Recognition and Neural Networks, Cambridge University Press, 2008, ISBN 978-0-521-71770-0.
  39. M. Seul, L. O'Gorman, M.J. Sammon; Practical Algorithms for Image Analysis: Descriptions Examples and Code (2nd edition) (Support Website), Cambridge University Press, 2008, ISBN 0-521-66065-3.
  40. D. Salomon; Data Compression: The Complete Reference (Support Website), Springer-Verlag New York Inc., January 15, 2004, ISBN: 0387406972.
  41. H. Samet; Applications of Spatial Data Structures: Computer Graphics, Image Processing, and GIS, Addison-Wesley, Reading, MA, 1990. ISBN 0-201-50300-0.
  42. H. Samet; The Design and Analysis of Spatial Data Structures, Addison-Wesley, Reading, MA, 1990. ISBN 0-201-50255-0.
  43. J. Shen, Multispectral Image Processing and Pattern Recognition, World Scientific, 2001, ISBN: 9810245939.
  44. P. Soille; Morphological Image Analysis: Principles and Applications, Springer, 2004.
  45. M. Sonka, R. Boyle, V. Hlavac; Image Processing: Analysis and Machine Vision (Support Website), Thomson Learning, Apr 2007 (3rd edition), ISBN 978-0-495-08252-1. See also the companion book: T. Svoboda, J. Kybic, V. Hlavac; Image Processing, Analysis and Machine Vision - A MATLAB Companion, Thomson Learning, September 2007, ISBN 0-495-29595-7
  46. W. Snyder, H. Qi; Machine Vision, Cambridge University Press, 2004, ISBN 052183046X.
  47. C. Steger, M. Ulrich, C. Wiedemann; Machine Vision Algorithms and Applications Wiley-VCH, 2007, ISBN: 978-3-527-40734-7.
  48. J. Z. Wang, Integrated Region-Based Image Retrieval, Kluwer Academic Publishers, Dordrecht, 2001.
  49. P. S. P. Wang, Parallel Image analysis, Tools and Models, World Scientific, 1998, ISBN: 981-02-3458-9.
  50. S. Watanabe; Algebraic Geometry and Statistical Learning Theory, Cambridge University Press, 2009, ISBN=9780521864671.

http://homepages.inf.ed.ac.uk/rbf/CVonline/books.htm#online

 

常用牛人主页链接(计算机视觉、模式识别、机器学习相关方向,陆续更新。。。。)

转自http://www.cnblogs.com/kshenf/archive/2012/02/07/2342034.html

牛人主页(主页有很多论文代码)

Serge Belongie at UC San Diego
Ce Liu at Microsoft Research New England
Vittorio Ferrari at Univ.of Edinburgh
Kristen Grauman at UT Austin
Devi Parikh at  TTI-Chicago (Marr Prize at ICCV2011)
John Wright at Columbia Univ.
Piotr Dollar at CalTech
Boris Babenko at UC San Diego
David Ross at Google/Youtube
David Donoho at Stanford Univ.
 
大神们:
Roberto Cipolla at Cambridge
David Lowe at Univ. of British Columbia
Mubarak Shah at Univ. of Central Florida
Yi Ma at MSRA
Tinne Tuytelaars at K.U. Leuven
Trevor Darrell at U.C. Berkeley
Michael J. Black at Brown Univ.



重要研究组:
Computer Vision Group at UC Berkeley
Robotics Research Group at Univ. of Oxford
LEAR at INRIA
Computer Vision Lab at ETH Zurich
Computer Vision Lab at Seoul National Univ.
Computer Vision Lab at UC San Diego
Computer Vision Lab at UC Santa Cruz
Computer Vision Lab at Univ. of Southern California
Computer Vision Lab at Univ. of Central Florida
Computer Vision Lab at Columbia Univ.
Motion and Shape Computing Group at George Mason Univ.
Computer Vision Lab. at Vienna Univ. of Tech. 
Visual Perception Lab at Purdue Univ.
 
潜力牛人:
Juergen Gall at ETH Zurich
Matt Flagg at Georgia Tech.
Mathieu Salzmann at TTI-Chicago
Gerg Shakhnarovich at TTI-Chicago
Jianchao Yang at UIUC
Stefan Roth at TU Darmstadt
Peter Kontschieder at Graz Univ. of Tech.
Dominik Alexander Klein at Univ. of Bonn
Yinan Yu at CASIA (PASCAL VOC 2010 Detection Challenge Winner)
Zdenek Kalal at FPFL
Julien Pilet at FPFL
 
(1)googleResearch; http://research.google.com/index.html
(2)MIT博士,汤晓欧学生林达华; http://people.csail.mit.edu/dhlin/index.html
(3)MIT博士后Douglas Lanman; http://web.media.mit.edu/~dlanman/
(4)opencv中文网站; http://www.opencv.org.cn/index.php/%E9%A6%96%E9%A1%B5
(5)Stanford大学vision实验室; http://vision.stanford.edu/research.html
(6)Stanford大学博士崔靖宇; http://www.stanford.edu/~jycui/
(7)UCLA教授朱松纯; http://www.stat.ucla.edu/~sczhu/
(8)中国人工智能网; http://www.chinaai.org/
(9)中国视觉网; http://www.china-vision.net/
(10)中科院自动化所; http://www.ia.cas.cn/
(11)中科院自动化所李子青研究员; http://www.cbsr.ia.ac.cn/users/szli/
(12)中科院计算所山世光研究员; http://www.jdl.ac.cn/user/sgshan/
(13)人脸识别主页; http://www.face-rec.org/
(14)加州大学伯克利分校CV小组; http://www.eecs.berkeley.edu/Research/Projects/CS/vision/(15)南加州大学CV实验室; http://iris.usc.edu/USC-Computer-Vision.html
(16)卡内基梅隆大学CV主页;http://www.cs.cmu.edu/afs/cs/project/cil/ftp/html/vision.html

(17)微软CV研究员Richard Szeliski;http://research.microsoft.com/en-us/um/people/szeliski/
(18)微软亚洲研究院计算机视觉研究组; http://research.microsoft.com/en-us/groups/vc/
(19)微软剑桥研究院ML与CV研究组; http://research.microsoft.com/en-us/groups/mlp/default.aspx

(20)研学论坛; http://bbs.matwav.com/
(21)美国Rutgers大学助理教授刘青山; http://www.research.rutgers.edu/~qsliu/
(22)计算机视觉最新资讯网; http://www.cvchina.info/
(23)运动检测、阴影、跟踪的测试视频下载; http://apps.hi.baidu.com/share/detail/18903287
(24)香港中文大学助理教授王晓刚; http://www.ee.cuhk.edu.hk/~xgwang/
(25)香港中文大学多媒体实验室(汤晓鸥); http://mmlab.ie.cuhk.edu.hk/
(26)U.C. San Diego. computer vision;http://vision.ucsd.edu/content/home
(27)CVonline; http://homepages.inf.ed.ac.uk/rbf/CVonline/
(28)computer vision software; http://peipa.essex.ac.uk/info/software.html
(29)Computer Vision Resource; http://www.cvpapers.com/
(30)computer vision research groups;http://peipa.essex.ac.uk/info/groups.html
(31)computer vision center; http://computervisioncentral.com/cvcnews

(32)浙江大学图像技术研究与应用(ITRA)团队:http://www.dvzju.com/

(33)自动识别网:http://www.autoid-china.com.cn/

(34)清华大学章毓晋教授:http://www.tsinghua.edu.cn/publish/ee/4157/2010/20101217173552339241557/20101217173552339241557_.html

(35)顶级民用机器人研究小组Porf.Gary领导的Willow Garage:http://www.willowgarage.com/

(36)上海交通大学图像处理与模式识别研究所:http://www.pami.sjtu.edu.cn/

(37)上海交通大学计算机视觉实验室刘允才教授:http://www.visionlab.sjtu.edu.cn/

(38)德克萨斯州大学奥斯汀分校助理教授Kristen Grauman :http://www.cs.utexas.edu/~grauman/

(39)清华大学电子工程系智能图文信息处理实验室(丁晓青教授):http://ocrserv.ee.tsinghua.edu.cn/auto/index.asp

(40)北京大学高文教授:http://www.jdl.ac.cn/htm-gaowen/

(41)清华大学艾海舟教授:http://media.cs.tsinghua.edu.cn/cn/aihz

(42)中科院生物识别与安全技术研究中心:http://www.cbsr.ia.ac.cn/china/index%20CH.asp

(43)瑞士巴塞尔大学 Thomas Vetter教授:http://informatik.unibas.ch/personen/vetter_t.html

(44)俄勒冈州立大学 Rob Hess博士:http://blogs.oregonstate.edu/hess/

(45)深圳大学 于仕祺副教授:http://yushiqi.cn/

(46)西安交通大学人工智能与机器人研究所:http://www.aiar.xjtu.edu.cn/

(47)卡内基梅隆大学研究员Robert T. Collins:http://www.cs.cmu.edu/~rcollins/home.html#Background

(48)MIT博士Chris Stauffer:http://people.csail.mit.edu/stauffer/Home/index.php

(49)美国密歇根州立大学生物识别研究组(Anil K. Jain教授):http://www.cse.msu.edu/rgroups/biometrics/

(50)美国伊利诺伊州立大学Thomas S. Huang:http://www.beckman.illinois.edu/directory/t-huang1

(51)武汉大学数字摄影测量与计算机视觉研究中心:http://www.whudpcv.cn/index.asp

(52)瑞士巴塞尔大学Sami Romdhani助理研究员:http://informatik.unibas.ch/personen/romdhani_sami/

(53)CMU大学研究员Yang Wang:http://www.cs.cmu.edu/~wangy/home.html

(54)英国曼彻斯特大学Tim Cootes教授:http://personalpages.manchester.ac.uk/staff/timothy.f.cootes/

(55)美国罗彻斯特大学教授Jiebo Luo:http://www.cs.rochester.edu/u/jluo/

(56)美国普渡大学机器人视觉实验室:https://engineering.purdue.edu/RVL/Welcome.html

(57)美国宾利州立大学感知、运动与认识实验室:http://vision.cse.psu.edu/home/home.shtml

(58)美国宾夕法尼亚大学GRASP实验室:https://www.grasp.upenn.edu/

(59)美国内达华大学里诺校区CV实验室:http://www.cse.unr.edu/CVL/index.php

(60)美国密西根大学vision实验室:http://www.eecs.umich.edu/vision/index.html

(61)University of Massachusetts(麻省大学),视觉实验室:http://vis-www.cs.umass.edu/index.html

(62)华盛顿大学博士后Iva Kemelmacher:http://www.cs.washington.edu/homes/kemelmi

(63)以色列魏茨曼科技大学Ronen Basri:http://www.wisdom.weizmann.ac.il/~ronen/index.html

(64)瑞士ETH-Zurich大学CV实验室:http://www.vision.ee.ethz.ch/boostingTrackers/index.htm

(65)微软CV研究员张正友:http://research.microsoft.com/en-us/um/people/zhang/

(66)中科院自动化所医学影像研究室:http://www.3dmed.net/

(67)中科院田捷研究员:http://www.3dmed.net/tian/

(68)微软Redmond研究院研究员Simon Baker:http://research.microsoft.com/en-us/people/sbaker/

(69)普林斯顿大学教授李凯:http://www.cs.princeton.edu/~li/
(70)普林斯顿大学博士贾登:http://www.cs.princeton.edu/~jiadeng/
(71)牛津大学教授Andrew Zisserman: http://www.robots.ox.ac.uk/~az/
(72)英国leeds大学研究员Mark Everingham:http://www.comp.leeds.ac.uk/me/
(73)英国爱丁堡大学教授Chris William: http://homepages.inf.ed.ac.uk/ckiw/
(74)微软剑桥研究院研究员John Winn: http://johnwinn.org/
(75)佐治亚理工学院教授Monson H.Hayes:http://savannah.gatech.edu/people/mhayes/index.html
(76)微软亚洲研究院研究员孙剑:http://research.microsoft.com/en-us/people/jiansun/
(77)微软亚洲研究院研究员马毅:http://research.microsoft.com/en-us/people/mayi/
(78)英国哥伦比亚大学教授David Lowe: http://www.cs.ubc.ca/~lowe/
(79)英国爱丁堡大学教授Bob Fisher: http://homepages.inf.ed.ac.uk/rbf/
(80)加州大学圣地亚哥分校教授Serge J.Belongie:http://cseweb.ucsd.edu/~sjb/
(81)威斯康星大学教授Charles R.Dyer: http://pages.cs.wisc.edu/~dyer/
(82)多伦多大学教授Allan.Jepson: http://www.cs.toronto.edu/~jepson/
(83)伦斯勒理工学院教授Qiang Ji: http://www.ecse.rpi.edu/~qji/
(84)CMU研究员Daniel Huber: http://www.ri.cmu.edu/person.html?person_id=123
(85)多伦多大学教授:David J.Fleet: http://www.cs.toronto.edu/~fleet/
(86)伦敦大学玛丽女王学院教授Andrea Cavallaro:http://www.eecs.qmul.ac.uk/~andrea/
(87)多伦多大学教授Kyros Kutulakos: http://www.cs.toronto.edu/~kyros/
(88)杜克大学教授Carlo Tomasi: http://www.cs.duke.edu/~tomasi/
(89)CMU教授Martial Hebert: http://www.cs.cmu.edu/~hebert/
(90)MIT助理教授Antonio Torralba: http://web.mit.edu/torralba/www/
(91)马里兰大学研究员Yasel Yacoob: http://www.umiacs.umd.edu/users/yaser/
(92)康奈尔大学教授Ramin Zabih: http://www.cs.cornell.edu/~rdz/

(93)CMU博士田渊栋: http://www.cs.cmu.edu/~yuandong/
(94)CMU副教授Srinivasa Narasimhan: http://www.cs.cmu.edu/~srinivas/
(95)CMU大学ILIM实验室:http://www.cs.cmu.edu/~ILIM/
(96)哥伦比亚大学教授Sheer K.Nayar: http://www.cs.columbia.edu/~nayar/
(97)三菱电子研究院研究员Fatih Porikli :http://www.porikli.com/
(98)康奈尔大学教授Daniel Huttenlocher:http://www.cs.cornell.edu/~dph/
(99)南京大学教授周志华:http://cs.nju.edu.cn/zhouzh/index.htm
(100)芝加哥丰田技术研究所助理教授Devi Parikh: http://ttic.uchicago.edu/~dparikh/index.html
(101)瑞士联邦理工学院博士后Helmut Grabner: http://www.vision.ee.ethz.ch/~hegrabne/#Short_CV

(102)香港中文大学教授贾佳亚:http://www.cse.cuhk.edu.hk/~leojia/index.html

(103)南洋理工大学副教授吴建鑫:http://c2inet.sce.ntu.edu.sg/Jianxin/index.html

(104)GE研究院研究员李关:http://www.cs.unc.edu/~lguan/

(105)佐治亚理工学院教授Monson Hayes:http://savannah.gatech.edu/people/mhayes/

(106)图片检索国际会议VOC(微软剑桥研究院组织): http://pascallin.ecs.soton.ac.uk/challenges/VOC/

(107)机器视觉开源处理库汇总:http://archive.cnblogs.com/a/2217609/

(108)布朗大学教授Benjamin Kimia: http://www.lems.brown.edu/kimia.html 

 

 

about multi-camera: http://server.cs.ucf.edu/~vision/projects.html

 

about 3D Voxel Coloring   Rob Hess: http://blogs.oregonstate.edu/hess/code/voxels/ 

 

About  the particle filters--condensation filter:http://homepages.inf.ed.ac.uk/rbf/CVonline/LOCAL_COPIES/ISARD1/condensation.html

 

Machine Learning Open Source Software:http://jmlr.csail.mit.edu/mloss/

 

1、动作识别数据库:Recognition of human actions:http://www.nada.kth.se/cvap/actions/

 

2、Datasets for Computer Vision Research:http://www-cvr.ai.uiuc.edu/ponce_grp/data/

 

3、Computer Vision Datasets:http://clickdamage.com/sourcecode/cv_datasets.php

 

4、里面有好多基本算法 matlab:  http://www.mathworks.cn/index.html

 

5、CVPR 2011中关于grassmann 流形文章的源码: http://itee.uq.edu.au/~uqmhara1/code.html

 

  • Matlab Codefor Graph Embedding Discriminant Analysis on Grassmannian Manifolds for Improved Image Set Matching (CVPR), 2011.
  • Matlab Codefor Optimal Local Basis: A Reinforcement Learning Approach for Face Recognition(IJCV), vol. 81, no. 2, pp. 191-204, 2009.

 

 牛人bolg:

 

1、Hong Kong Polytechnic University :http://www4.comp.polyu.edu.hk/~cslzhang/

 

2、Computer Vision Resources:资源非常丰富,包含有基本算法。https://netfiles.uiuc.edu/jbhuang1/www/resources/vision/index.html

 

3、源代码非常丰富~~  http://homepage.tudelft.nl/19j49/Publications.html

 

CVonline

http://homepages.inf.ed.ac.uk/rbf/CVonline

http://homepages.inf.ed.ac.uk/rbf/CVonline/unfolded.htm

http://homepages.inf.ed.ac.uk/rbf/CVonline/CVentry.htm

 

李子青的大作:

Markov Random Field Modeling in Computer Vision

http://www.cbsr.ia.ac.cn/users/szli/mrf_book/book.html

Handbook of Face Recognition (PDF)

http://www.umiacs.umd.edu/~shaohua/papers/zhou04hfr.pdf
 

 

张正友的有关参数鲁棒估计著作:

Parameter Estimation Techniques:A Tutorial with Application to Conic Fitting

http://research.microsoft.com/~zhang/INRIA/Publis/Tutorial-Estim/Main.html

Andrea Fusiello“计算机视觉中的几何”教程:Elements of Geometric Computer Vision

http://homepages.inf.ed.ac.uk/rbf/CVonline/LOCAL_COPIES/FUSIELLO4/tutorial.html#x1-520007
 

有关马尔可夫蒙特卡罗方法的资料:

An introduction to Markov chain Monte Carlo

http://homepages.inf.ed.ac.uk/rbf/CVonline/LOCAL_COPIES/SENEGAS/mcmc.html

Markov Chain Monte Carlo for Computer Vision--- A tutorial at ICCV05

       http://civs.stat.ucla.edu/MCMC/MCMC_tutorial.htm
 

有关独立成分分析(Independent Component Analysis , ICA)的资料:

An ICA-Page

http://www.cnl.salk.edu/~tony/ica.html

Fast ICA

http://www.cis.hut.fi/projects/ica/fastica/

 

       The Kalman Filter (介绍卡尔曼滤波器的终极网页)

      http://www.cs.unc.edu/~welch/kalman/index.html

 

Cached k-d tree search for ICP algorithms

http://kos.informatik.uni-osnabrueck.de/download/3dim2007/paper.html
 

几个计算机视觉研究工具

Machine Vision Toolbox for Matlab

http://www.petercorke.com/Machine%20Vision%20Toolbox.html


Matlab and Octave Function for Computer Vision and Image Processing

http://www.csse.uwa.edu.au/~pk/research/matlabfns/

 

Bayes Net Toolbox for Matlab

http://www.cs.ubc.ca/~murphyk/Software/BNT/bnt.html


OpenCV (Chinese)

http://www.opencv.org.cn/index.php/%E9%A6%96%E9%A1%B5

 

Gandalf (A Computer Vision and Numerical Algorithm Labrary)

http://gandalf-library.sourceforge.net/

 

CMU Computer Vision Home Page

http://www.cs.cmu.edu/afs/cs/project/cil/ftp/html/vision.html

 

Machine Learning Resource Links

http://www.cse.ust.hk/~ivor/resource.htm

 

The Bayesian Filtering Library

http://www.orocos.org/bfl

 

Optical Flow Algorithm Evaluation (提供了一个动态贝叶斯网络框架,例如递归信息处理与分析、卡尔曼滤波、粒子滤波、序列蒙特卡罗方法等,C++写的)

http://of-eval.sourceforge.net/

 

MATLAB code for ICP algorithm

http://www.usenet.com/newsgroups/comp.graphics.visualization/msg00102.html

 

牛人主页:

朱松纯 (Song-Chun Zhu

http://www.stat.ucla.edu/~sczhu/

 

David Lowe (SIFT) (很帅的一个老头哦 ^ ^)

http://www.cs.ubc.ca/~lowe/

 

Andrea Vedaldi (SIFT)

http://vision.ucla.edu/~vedaldi/index.html

 

Pedro F. Felzenszwalb

http://people.cs.uchicago.edu/~pff/

 

Dougla Dlanman (Brown的一个研究生,在其主页上搜集了大量算法教程和源码)

http://mesh.brown.edu/dlanman/courses.html

 

Jianbo Shi (Ncuts 的始作俑者)

http://www.cis.upenn.edu/~jshi/

 

Active Vision Group (Oxford的一个机器视觉研究团队,特色是SLAM,监视,导航)

http://www.robots.ox.ac.uk/ActiveVision/index.html

 

Juyang Weng(机器学习的专家,Autonomous Mental Development 是其特色

http://www.cse.msu.edu/~weng/

测试图片或视频:

Middlebury College‘s Stereo Vision Data Set

http://cat.middlebury.edu/stereo/data.html

 

 

Intelligent Vehicle:

IVSource

www.ivsoruce.net

Robot Car

http://www.plyojump.com/robot_cars.html

How to Build a Robot: The Computer Vision Part

http://www.societyofrobots.com/programming_computer_vision_tutorial.shtml

 

收集的一般牛人主页(带代码):

 Xiaofei He(machine learning code)

http://people.cs.uchicago.edu/~xiaofei/

 YingNian Wu(active base model code)

http://www.stat.ucla.edu/~ywu/research.html

 布朗大学计算机主页(可找到该校CS牛人博客)

http://www.cs.brown.edu/research/areas.html

Navneet Dalal(Histograms of Oriented Gradients for Human Detection )

http://www.navneetdalal.com/software

Paul Viola(Robust Real-time Object Detection)

http://research.microsoft.com/en-us/um/people/viola/

 

Active LearningRMw平坦软件园

http://active-learning.net/,这里包括了关于Active Learning理论以及应用的一些文章,特别是那篇Survey。
Transfer LearningRMw平坦软件园

http://www.cse.ust.hk/TL/,包括经典的论文以及附带有源码,很方便。
Gaussian ProcessesRMw平坦软件园
RMw平坦软件园

http://www.gaussianprocess.org 包括相关的书籍(有 Carl Edward Rasmussen 的书),相关的程序以及分类的 paper 列表。这也是由 Carl 自己维护的,他应该是将 GP 引入 machine learning 最早的人之一了吧,Hinton 的学生。
Nonparametric Bayesian MethodsRMw平坦软件园

http://www.cs.berkeley.edu/~jordan/npb.html 这个一看就知道是 Jordan 维护的,主要包括 Dirichlet process 以及相关的其他随机过程在 machine learning 里面如何进行建模,如何进行 approximate inference。主要是文章列表。
Probabilistic Graphical ModelRMw平坦软件园

http://www.cs.ubc.ca/~murphyk/Bayes/bnintro.html 是 Kevin Murphy 所维护的关于 Bayesian belief networks 的介绍,含有最基本的概念、相关的文献和软件的链接。罕见的 UCB 出来的不是 Jordan 的学生(老板是 Stuart Russel)。
http://www.cs.berkeley.edu/~jordan/graphical.html 是 Jordan 系关于这个方面的论文汇编。
http://www.inference.phy.cam.ac.uk/hmw26/crf/ 是关于 Conditional Random Fields 方面论文和软件的收集,由 Hanna Wallach 维护。
Compressed SensingRMw平坦软件园

http://www-dsp.rice.edu/cs 这是 Rice 大学维护的论文分类列表、软件链接等。推荐 Emmanuel Candès 所写的tutorial,这人是 David Donoho 的学生。
TensorRMw平坦软件园

http://csmr.ca.sandia.gov/~tgkolda/pubs/index.html 关于 tensor 的一些偏数学的文章。
Deep Belief NetworkRMw平坦软件园

http://www.cs.toronto.edu/~hinton/csc2515/deeprefs.html 是 Geoffrey Hinton 为研究生开设的 machine learning 课程的 DBN 的 reading list。
Kernel MethodsRMw平坦软件园

http://www.cs.berkeley.edu/~jordan/kernels.html 是 Jordan 维护的关于 kernel methods 的文章列表。
Markov LogicRMw平坦软件园

http://ai.cs.washington.edu/pubs 是 UW AI 组的文章,里面关于 Markov logic 的比较多,因为 Pedro Domingos 就是这个组的。

Machine learning theory

http://hunch.net/这个网站主要是一些learning theory的东西比较多,想在machine learning 理论上有所建树的同志们可以去看看

 

 牛人:Iasonas Kokkinos (搞统计模型视觉)

http://vision.mas.ecp.fr/Personnel/iasonas/index.html

 

人工智能与模式识别国际顶级期刊会议目录(包含该领域最权威的期刊和会议)

 

机器学习资源大全(欢迎补充,持续更新)
发信站: 水木社区 (Tue Jan 17 17:15:15 2012), 站内
Harvard机器学习资料(video) [antinucleon]
http://cm.dce.harvard.edu/2011/02/23101/publicationListing.shtml

分享一些资料[antinucleon]
1. ml-class.org
级别相当于S校的CS229A,注重Application,适合大二的学生学习,产生兴趣。但这个理论性差的太多,按Ng的说法是计算机系的学这个只能给个C
2. CS229
see.stanford.edu有SCPD的视频作业等等,我现在正在学习,正常的Advanced Undergraduate/ Graduate课程
3. CMU的Tom Mitchell的Lecture
http://www.cs.cmu.edu/~tom/10701_sp11/lectures.shtml
教育网速度太慢,不清楚如何
4. PGM
pgm-class.org, 明年一月开课,猜测可能和CS229A情况相似

stanford machine learning cs299其他相关课程     [pennyliang]
http://www.newsmth.net/bbscon.php?bid=1327&id=407

水牛大学课件,视频,例子,课件都有
http://www.cedar.buffalo.edu/~srihari/CSE574/

凸优化slides[SPAM]
http://math.nju.edu.cn/~hebma/slides/01C.pdf
http://math.nju.edu.cn/~hebma/slides/07C.pdf

书籍分享[SPAM]
http://115.com/file/bhttr6yy#

Statistics for High-Dimensional Data: Methods, Theory and Applications

By P. Buhlmann and S. van de Geer

http://www.eecs.berkeley.edu/~wainwrig/Papers/WaiJor08_FTML.pdf

Graphical models, exponential families, and variational inference

By M. Wainwright and M. Jordan

数值分析 [Pennyliang]
作  者:(美)索尔 著 吴兆金,王国英,范红军 译
http://book.360buy.com/10064039.html
推荐理由:这本书我看过,通俗易懂,例子简单,章节系统,很全,巨好。

林达华blog书单

前面几篇谈了一些对数学的粗浅看法。其实,如果对某门数学有兴趣,最好的方法就是走进那个世界去学习和体验。
这里说说几本我看过后觉得不错的数学教科书。
1. 线性代数 (Linear Algebra):
我想国内的大学生都会学过这门课程,但是,未必每一位老师都能贯彻它的精要。这门学科对于Learning是必备的基础,对它的透彻掌握是必不可少的。我在科大一年级的时候就学习了这门课,后来到了香港后,又重新把线性代数读了一遍,所读的是
Introduction to Linear Algebra (3rd Ed.)  by Gilbert Strang.
这本书是MIT的线性代数课使用的教材,也是被很多其它大学选用的经典教材。它的难度适中,讲解清晰,重要的是对许多核心的概念讨论得比较 透彻。我个人觉得,学习线性代数,最重要的不是去熟练矩阵运算和解方程的方法——这些在实际工作中MATLAB可以代劳,关键的是要深入理解几个基础而又 重要的概念:子空间(Subspace),正交(Orthogonality),特征值和特征向量(Eigenvalues and eigenvectors),和线性变换(Linear transform)。从我的角度看来,一本线代教科书的质量,就在于它能否给这些根本概念以足够的重视,能否把它们的联系讲清楚。Strang的这本书 在这方面是做得很好的。
而且,这本书有个得天独厚的优势。书的作者长期在MIT讲授线性代数课(18.06),课程的video在MIT的Open courseware网站上有提供。有时间的朋友可以一边看着名师授课的录像,一边对照课本学习或者复习。
http://ocw.mit.edu/OcwWeb/Mathematics/18-06Spring-2005/CourseHome/index.htm
2. 概率和统计 (Probability and Statistics):
概率论和统计的入门教科书很多,我目前也没有特别的推荐。我在这里想介绍的是一本关于多元统计的基础教科书:
Applied Multivariate Statistical Analysis (5th Ed.)  by Richard A. Johnson and Dean W. Wichern
这本书是我在刚接触向量统计的时候用于学习的,我在香港时做研究的基础就是从此打下了。实验室的一些同学也借用这本书学习向量统计。这本书 没有特别追求数学上的深度,而是以通俗易懂的方式讲述主要的基本概念,读起来很舒服,内容也很实用。对于Linear regression, factor analysis, principal component analysis (PCA), and canonical component analysis (CCA)这些Learning中的基本方法也展开了初步的论述。
之后就可以进一步深入学习贝叶斯统计和Graphical models。一本理想的书是
Introduction to Graphical Models (draft version).  by M. Jordan and C. Bishop.
我不知道这本书是不是已经出版了(不要和Learning in Graphical Models混淆,那是个论文集,不适合初学)。这本书从基本的贝叶斯统计模型出发一直深入到复杂的统计网络的估计和推断,深入浅 出,statistical learning的许多重要方面都在此书有清楚论述和详细讲解。MIT内部可以access,至于外面,好像也是有电子版的。
3. 分析 (Analysis):
我想大家基本都在大学就学过微积分或者数学分析,深度和广度则随各个学校而异了。这个领域是很多学科的基础,值得推荐的教科书莫过于
Principles of Mathematical Analysis, by Walter Rudin
有点老,但是绝对经典,深入透彻。缺点就是比较艰深——这是Rudin的书的一贯风格,适合于有一定基础后回头去看。
在分析这个方向,接下来就是泛函分析(Functional Analysis)。
Introductory Functional Analysis with Applications, by Erwin Kreyszig.
适合作为泛函的基础教材,容易切入而不失全面。我特别喜欢它对于谱论和算子理论的特别关注,这对于做learning的研究是特别重要的。 Rudin也有一本关于functional analysis的书,那本书在数学上可能更为深刻,但是不易于上手,所讲内容和learning的切合度不如此书。
在分析这个方向,还有一个重要的学科是测度理论(Measure theory),但是我看过的书里面目前还没有感觉有特别值得介绍的。
4. 拓扑 (Topology):
在我读过的基本拓扑书各有特色,但是综合而言,我最推崇:
Topology (2nd Ed.)  by James Munkres
这本书是Munkres教授长期执教MIT拓扑课的心血所凝。对于一般拓扑学(General topology)有全面介绍,而对于代数拓扑(Algebraic topology)也有适度的探讨。此书不需要特别的数学知识就可以开始学习,由浅入深,从最基本的集合论概念(很多书不屑讲这个)到Nagata- Smirnov Theorem和Tychonoff theorem等较深的定理(很多书避开了这个)都覆盖了。讲述方式思想性很强,对于很多定理,除了给出证明过程和引导你思考其背后的原理脉络,很多令人 赞叹的亮点——我常读得忘却饥饿,不愿释手。很多习题很有水平。
5. 流形理论 (Manifold theory):
对于拓扑和分析有一定把握时,方可开始学习流形理论,否则所学只能流于浮浅。我所使用的书是
Introduction to Smooth Manifolds.  by John M. Lee
虽然书名有introduction这个单词,但是实际上此书涉入很深,除了讲授了基本的manifold, tangent space, bundle, sub-manifold等,还探讨了诸如纲理论(Category theory),德拉姆上同调(De Rham cohomology)和积分流形等一些比较高级的专题。对于李群和李代数也有相当多的讨论。行文通俗而又不失严谨,不过对某些记号方式需要熟悉一下。
虽然李群论是建基于平滑流形的概念之上,不过,也可能从矩阵出发直接学习李群和李代数——这种方法对于急需使用李群论解决问题的朋友可能更加实用。而且,对于一个问题从不同角度看待也利于加深理解。下面一本书就是这个方向的典范:
Lie Groups, Lie Algebras, and Representations: An Elementary Introduction.  by Brian C. Hall
此书从开始即从矩阵切入,从代数而非几何角度引入矩阵李群的概念。并通过定义运算的方式建立exponential mapping,并就此引入李代数。这种方式比起传统的通过“左不变向量场(Left-invariant vector field)“的方式定义李代数更容易为人所接受,也更容易揭示李代数的意义。最后,也有专门的论述把这种新的定义方式和传统方式联系起来。

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