Practical computer vision: Example techniques and challenges

IBM Journal of Research and Development

Volumn 55, Issue 5, 2011, Pages

  Pankanti, S ^a   Brown, L ^a   Connell, J ^a   Datta, A ^a   Fan, Q ^a   Feris, R S ^a   Haas, N ^a   Li, Y ^a   Ratha, N ^a   Trinh, H ^a  

^a IBM T J WATSON RESEARCH CENTER   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER VISION SYSTEM; DRIVER ASSISTANCE; LIVING ORGANISMS; PERSON IDENTIFICATION; SCENE UNDERSTANDING; VIDEO SURVEILLANCE; VISUAL WORLD;

AUTOMOBILE DRIVERS; BIOLOGY; BIOMETRICS; OBJECT RECOGNITION; PROBLEM SOLVING; RETAIL STORES; SECURITY SYSTEMS;

COMPUTER VISION;

EID: 81555199693     PISSN: 00188646     EISSN: 21518556     Source Type: Journal    
DOI: 10.1147/JRD.2011.2165676     Document Type: Article

References (34)

1. The Operational Riskdata eXchange. [Online]. Available: http://www.orx.org/
2. Bank for International Settlements, 2006. Basel II: International Convergence of Capital Measurement and Capital Standards: A Revised FrameworkVComprehensive Version, "IS, "asel, Switzerland, 2006. [Online]. Available: http://www.bis.org/publ/bcbs128.pdf
3. H. B. Newcombe, J. M. Kennedy, S. Axford, and A. James, "Automatic linkage of vital records," Science, vol. 130, no. 3381, pp. 954-959, Oct. 1959.
4. I. P. Fellegi and A. B. Sunter, "A theory for record linkage," J. Amer. Stat. Assoc., vol. 64, no. 328, pp. 1183-1210, Dec. 1969.
5. S. Sarawagi and A. Bhamidipaty, "Interactive deduplication using active learning," in Proc. 8th Intl. Conf. Knowl. Disc. Data Min. (SIGKDD), 2002, pp. 269-278.
6. A. K. Elmagarmid, P. G. Ipeirotis, and V. S. Verykios, "Duplicate record detection: A survey," IEEE Trans. Knowl. Data Eng., vol. 19, no. 1, pp. 1-16, Jan. 2007. (Pubitemid 44955773)
7. E. Ukkonen, "Approximate string matching with q-grams and maximal matches," Theor. Comput. Sci., vol. 92, no. 1, pp. 191-211, Jan. 1992.
8. M. Bilenko, R. J. Mooney, W. W. Cohen, P. Ravikumar, and S. E. Fienberg, "Adaptive name matching in information integration," IEEE Intell. Syst., vol. 18, no. 5, pp. 16-23, Sep./Oct. 2003.
9. M. A. Hernandez and S. J. Stolfo, "Real-world data is dirty: Data cleansing and the merge/purge problem," Data Min. Knowl. Discov., vol. 2, no. 1, pp. 9-37, 1998. (Pubitemid 128696797)
10. V. S. Verykios, A. K. Elmagarmid, and E. N. Houstis, "Automating the approximate record matching process," Inf. Sci., vol. 126, no. 1-4, pp. 83-98, Jul. 2000.
11. J. R. M. Hosking and J. R. Wallis, Regional Frequency Analysis: An Approach Based onL-Moments. Cambridge, U.K.: Cambridge Univ. Press, 1997.
12. F. Mosteller, "On pooling data," J. Amer. Stat. Assoc., vol. 43, no. 242, pp. 231-242, Jun. 1948.
13. D. V. Huntsberger, "A generalization of a preliminary testing procedure for pooling data," Ann. Math. Stat., vol. 26, no. 4, pp. 734-743, Dec. 1955.
14. J. Kiefer, "K-sample analogues of the Kolmogorov-Smirnov and Cramer-V. Mises tests," Ann. Math. Stat., vol. 30, no. 2, pp. 420-447, Jun. 1959.
15. F. W. Scholz and M. A. Stephens, "K-sample Anderson-Darling tests," J. Amer. Stat. Assoc., vol. 82, no. 399, pp. 918-924, Sep. 1987.
16. T. Hastie, J. Friedman, and R. Tibshirani, The Elements of Statistical Learning, 2nd ed. New York: Springer-Verlag, 2009.
17. F. Piacenza, D. Ruspantini, and A. Soprano, "Operational risk class homogeneity," J. Oper. Risk, vol. 1, no. 3, pp. 51-59, 2006.
18. E. W. Cope, "Modeling operational loss severity distributions from consortium data," J. Oper. Risk, vol. 5, no. 4, pp. 35-64, 2010.
19. C. A. Francisco and W. A. Fuller, "Quantile estimation with a complex survey design," Ann. Stat., vol. 19, no. 1, pp. 454-469, Mar. 1991.
20. M. W. Lipsey and D. Wilson, Practical Meta-Analysis. Thousand Oaks, CA: Sage, 2000.
21. J. Ritz, E. Demidenko, and D. Spiegelman, "Multivariate meta-analysis for data consortia, individual patient meta-analysis, and pooling projects," J. Stat. Plan. Inference, vol. 138, no. 7, pp. 1919-1933, Jul. 2008.
22. R. Koenker, Quantile Regression. Cambridge, U.K.: Cambridge Univ. Press, 2005.
23. Privacy-Preserving Data Mining: Models and Algorithms, C. C. Aggarwal and P. S. Yu, Eds. New York: Springer-Verlag, 2008.
24. Ponemon Institute, Annual Study: Global Cost of a Data Breach 2009. [Online]. Available: http://www.securityprivacyandthelaw. com/uploads/file/ Ponemon-COB-2009-GL.pdf
25. R. Agrawal and R. Srikant, "Privacy-preserving data mining," SIGMOD Rec., vol. 29, no. 2, pp. 439-450, Jun. 2000.
26. P. Samarati, "Protecting respondents' identities in microdata release," IEEE Trans. Knowl. Data Eng., vol. 13, no. 6, pp. 1010-1027, Nov./Dec. 2001. (Pubitemid 34035590)
27. A. Machanavajjhala, J. Gehrke, D. Kifer, and M. Venkitasubramaniam, B'-diversity: Privacy beyond-anonymity," in Proc. 22nd IEEE ICDE, 2006, p. 24.
28. N. Li, T. Li, and S. Venkatasubramanian, "t-closeness: Privacy beyond k-anonymity and l-diversity," in Proc. 23rd IEEE ICDE, 2007, pp. 106-115. (Pubitemid 47422014)
29. M. Terrovitis, N. Mamoulis, and P. Kalnis, "Privacy-preserving anonymization of set-valued data," in Proc. VLDB Endow., 2008, vol. 1, no. 1, pp. 115-125.
30. G. Ghinita, Y. Tao, and P. Kalnis, "On the anonymization of sparse high-dimensional data," in Proc. 24th IEEE Int. Conf. Data Eng., 2008, pp. 715-724.
31. G. Loukides, A. Gkoulalas-Divanis, and B. Malin, "COAT: Constraint-based anonymization of transactions," Knowl. Inf. Syst. Special Issue on Context-Aware Data Mining (CADM), vol. 28, no. 2, pp. 251-282, 2011.
32. V. S. Iyengar, "Transforming data to satisfy privacy constraints," in Proc. 8th Int. Conf. Knowl. Disc. Data Min. (SIGKDD), 2002, pp. 279-288.
33. D. Kifer and J. Gehrke, "Injecting utility into anonymized datasets," in Proc. ACM SIGMOD Int. Conf. Manag. Data (SIGMOD), 2006, pp. 217-228. (Pubitemid 46946515)
34. E. Cope and A. Labbi, "Operational loss scaling by exposure indicators: Evidence from the ORX database," J. Oper. Risk, vol. 3, no. 4, pp. 25-46, 2008.