Fondazione Bruno Kessler - Technologies of Vision

contains material from
Template Matching Techniques in Computer Vision: Theory and Practice
Roberto Brunelli 2009 John Wiley & Sons, Ltd

Bibliography

[1]   M Barreno, AA Cardenas, and JD Tygar. Optimal ROC curve for a combination of classifiers. In Proc. of Advances in Neural Information Processing Systems, volume 20, pages 57–64, 2007.

[2]   DM Blackburn. Evaluating technology properly: Three easy steps to success. Corrections Today, 63:56–60, 2001.

[3]   J Davis and M Goadrich. The relationship between precision-recall and ROC curves. In Proc. of the International Conference on Machine Learning (ICML’06), pages 233–240, 2006.
http://dx.doi.org/10.1145/1143844.1143874.

[4]   B Efron and R Tibshirani. Improvements on cross-validation: The .632+ bootstrap method. J. of the American Statistical Association, 92:548–560, 1997.

[5]   T Fawcett. An introduction to ROC analysis. Pattern Recognition Letters, 27:861–874, 2006.
http://dx.doi.org/10.1016/j.patrec.2005.10.010.

[6]   C Ferri, P Flach, J Hernandez-Orallo, and A Senad. Modifying ROC curves to incoporate predicted probabilities. In Proc. of the ICML 2005 Workshop on ROC Analysis in Machine Learning, 2005.

[7]   P Grother, R Micheals, and PJ Phillips. Face Recognition Vendor Test 2002 Performance Metrics. In Proc. of the 4th International Conference on Audio-and Video-Based Biometric Person Authentication, volume 2688 of Lecture Notes in Computer Science, pages 937–945. Springer, 2003.
http://dx.doi.org/10.1007/3-540-44887-X.

[8]   R Kohavi. A study of cross-validation and bootstrap for accuracy estimation and model. In Proc. of the International Joint Conference on Artificial Intelligence, pages 1137–1145, 1995.

[9]   M Last. The uncertainty principle of cross-validation. In Proceedings of the IEEE International Conference on Granular Computing, pages 275–280, 2006.

[10]   ST Mueller and J Zhang. Upper and lower bounds of area under ROC curves and index of discriminability of classifier performance. In Proc. of the ICML 2006 Workshop on ROC Analysis in Machine Learning, pages 41–46, 2006.

[11]   PJ Phillips, PJ Flynn, T Scruggs, KW Bowyer, C Jin, K Hoffman, J Marques, M Jaesik, and W Worek. Overview of the face recognition grand challenge. In Proc. of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR’05), volume 1, pages 947–954, 2005.
http://dx.doi.org/10.1109/CVPR.2005.268.

[12]   PJ Phillips, PJ Flynn, T Scruggs, KW Bowyer, and W Worek. Preliminary face recognition grand challenge results. In Proc. of the 7th International Conference on Automatic Face and Gesture Recognition (FG’06), pages 15–24, 2006.
http://dx.doi.org/10.1109/FGR.2006.87.

[13]   PJ Phillips, P Grother, R Micheals, Dm Blackburne, E Tabassi, and M Bone. Face Recognition Vendor Test 2002 Evaluation Report. Technical Report NISTIR 6965, National Institute of Standards and Technology, 2003.

[14]   PJ Phillips, M Hyeonjoon, SA Rizvi, and PJ Rauss. The FERET evaluation methodology for face-recognition algorithms. IEEE Trans. on Pattern Analysis and Machine Intelligence, 22:1090–1104, 2000.
http://dx.doi.org/10.1109/34.879790.

[15]   PJ Phillips, A Martin, CL Wilson, and M Przybocki. An introduction to evaluating biometric systems. IEEE Computer, 33:56–63, 2000.
http://dx.doi.org/10.1109/2.820040.

[16]   PJ Phillips, WT Scruggs, AJ O’Toole, PJ Flynn, KW Bowyer, CL Schott, and M Sharpe. FRVT 2006 and ICE 2006 large-scale results. Technical Report NISTIR 7408, National Institute of Standards and Technology, 2007.

[17]   V Popovici, J Thiran, Y Rodriguez, and S Marcel. On performance evaluation of face detection and localization algorithms. In Proc. of the 17th IAPR International Conference on Pattern Recognition (ICPR’04), volume 1, pages 313–317, 2004.
http://dx.doi.org/10.1109/ICPR.2004.1334115.

[18]   F Provost and T Fawcett. Robust classification for imprecise environments. Machine Learning, 42:203–231, 2001.
http://dx.doi.org/10.1023/A:1007601015854.

[19]   Y Rodriguez, F Carinaux, S Bengio, and J Mariethoz. Measuring the performance of face localization systems. Image and Vision Computing, 224:882–893, 2006.
http://dx.doi.org/10.1016/j.imavis.2006.02.012.

[20]   R Unnikrishnan, C Pantofaru, and M Hebert. Toward objective evaluation of image segmentation algorithms. IEEE Trans. on Pattern Analysis and Machine Intelligence, 29:929–944, 2007.
http://dx.doi.org/10.1109/TPAMI.2007.1046.