A summary of recent progresses for segmentation evaluation

Advances in Image and Video Segmentation

Volumn , Issue , 2006, Pages 423-440

  Zhang, Yu Jin ^a  

^a TSINGHUA UNIVERSITY   (China)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 77951237124     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.4018/978-1-59140-753-9.ch020     Document Type: Chapter

References (53)

1. Betanzos, A., Varela, A., & Martínez, C. (2000). Analysis and evaluation of hard and fuzzy clustering segmentation techniques in burned patient images. Image and Vision Computing, 18(13), 1045-1054.
2. Borsotti, M., Campadelli, P., & Schettini, R. (1998). Quantitative evaluation of color image segmentation results. Pattern Recognition Letters, 19(8), 741-747.
3. Carleer, A. P., Debeir, O., & Wolff, E. (2004). Comparison of very high spatial resolution satellite image segmentations. SPIE, 5238, 532-542.
4. Cavallaro, A., Gelasca, E. D., & Ebrahimi, T. (2002). Objective evaluation of segmentation quality using spatio-temporal context. In Proceedings of the International Conference on Image Processing (Vol. 3, pp. 301-304).
5. Chang, K. I., Bowyer, K.W., & Sivagurunath, M. (1999). Evaluation of texture segmentation algorithms. In Proceedings of the Computer Vision and Pattern Recognition (Vol. 1, pp. 294-299).
6. Correia, P. L., Pereira, F. M. B. (2000). Objective evaluation of relative segmentation quality. In Proceedings of the International Conference on Image Processing (Vol. 1, pp. 308-311).
7. Correia, P. L., & Pereira, F. M. B. (2000). Estimation of video object's relevance. In Proceedings of the EUSIPCO (pp. 925-928).
8. Correia, P. L., & Pereira, F. M. B. (2003). Methodologies for objective evaluation of video segmentation quality. SPIE, 5150, 1594-1600.
9. Correia, P. L., & Pereira, F. M. B. (2003). Objective evaluation of video segmentation quality. IEEE Image Processing, 12(2), 186-200.
10. Desurmont, X., Wijnhoven, R., & Jaspers, E., et al. (2005). Performance evaluation of realtime video content analysis systems in the CANDELA project. SPIE, 5671, 200-211.
11. Erdem, C. E., Sankur, B., & Tekalp, A. M. (2004). Performance measures for video object segmentation and tracking. IEEE Image Processing, 13(7), 937-951.
12. Everingham, M., Muller, H., & Thomas, B. (2002). Evaluating image segmentation algorithms using the Pareto Front. In Proceedings of the European Conference on Computer Vision (Vol. 4, pp. 34-48).
13. Gao, X., Boult, T. E., & Coetzee, F., et al. (2000). Error analysis of background adaptation. In Proceedings of the Computer Vision and Pattern Recognition (Vol. 1, pp. 503-510).
14. Hoover, A., Jean-Baptiste, G., & Jiang, X., et al. (1996). An experimental comparison of range image segmentation algorithms. IEEE Pattern Analysis and Machine Intelligence, 18(7), 673-689.
15. Huo, Z., & Giger, M. L. (2000). Evaluation of an automated segmentation method based on performances of an automated classification method. SPIE, 398, 16-21.
16. ITU-T. (1996). Recommendation P.910: Subjective video quality assessment methods for multimedia applications.
17. Kim, K., Chalidabhongse, T. H., & Harwood, D., et al. (2004). Background modeling and subtraction by codebook construction. In Proceedings of the International Conference on Image Processing (Vol. 5, pp. 3061-3064).
18. Ladak, H. M., Ding, M., Wang, Y., et al. (2004). Evaluation of algorithms for segmentation of the prostate boundary from 3D ultrasound images. SPIE, 5370, 1403-1410.
19. Li, N., Li, S., & Chen, C. (2003). Multimetric evaluation protocol for user-assisted video object extraction systems. SPIE, 5150, 20-28.
20. Lievers, W. B., & Pilkey, A. K. (2004). An evaluation of global thresholding techniques for the automatic image segmentation of automotive aluminum sheet alloys. Materials Science and Engineering, A. 381(1-2), 134-142.
21. Liu, J., & Yang, Y.-H. (1994). Multiresolution color image segmentation. IEEE Pattern Analysis and Machine Intelligence, 16(7), 689-700.
22. Liu, W., & Dori, D. (1999). Principles of constructing a performance evaluation protocol for graphics recognition algorithms. Performance characterization and evaluation of computer vision algorithms (pp. 97-106).
23. Mao, S., & Kanungo, T. (2001). Empirical performance evaluation methodology and its application to page segmentation algorithms. IEEE Pattern Analysis and Machine Intelligence, 23(3), 242-256.
24. Marcello, J., Marques, F., & Eugenio, F. (2004). Evaluation of thresholding techniques applied to oceanographic remote sensing imagery. SPIE, 5573, 96-103.
25. th International Conference on Computer Vision (Vol. 2, pp. 416-423).
26. Mattana, M. F., Facon, J., & Britto, A. S. (1999). Evaluation by recognition of thresholdingbased segmentation techniques on Brazilian bankchecks. SPIE, 3572, 344-348.
27. Moretti, B., Fadili, J. M., & Ruan, S., et al. (2000). Phantom-based performance evaluation: Application to brain segmentation from magnetic resonance images. Medical Image Analysis, 4(4), 303-316.
28. Niemeijer, M., Staal, J., & Ginneken, B., et al. (2004). Comparative study of retinal vessel segmentation methods on a new publicly available database. SPIE, 5370, 648-656.
29. Oberti, F., Granelli, F., & Regazzoni, C.S. (2000). Minimax based regulation of change detection threshold in video surveillance systems. In G. L. Foresti, P. Mähönen, and C. S.Regazzoni (Eds.). Multimedia video-based surveillance systems (pp. 210-233). Kluwer Academic Publishers.
30. Oberti, F., Stringa, E., & Vernazza, G. (2001). Performance evaluation criterion for characterizing video-surveillance systems. Real-Time Imaging, 7(5), 457-471.
31. Oberti, F., Teschioni, A., & Regazzoni, C. S. (1999). ROC curves for performance evaluation of video sequences processing systems for surveillance applications. In Proceedings of the International Conference on Image Processing (Vol. 2, pp. 949-953).
32. Onoguchi, K. (1998). Shadow elimination method for moving object detection. In Proceedings of the International Conference on Pattern Recognition (Vol. 1, pp. 583-587).
33. Prati, A., Mikic, I., & Trivedi, M. M., et al. (2003). Detecting moving shadows: Algorithms and evaluation. IEEE Pattern Analysis and Machine Intelligence, 25(7), 918-923.
34. Renno, J. R., Orwell, J., & Jones, G. A. (2004). Evaluation of shadow classification techniques for object detection and tracking. In Proceedings of the IEEE International Conference on Image Processing (pp. 143-146).
35. Rosenberger, C., & Chehdi, K. (2000). Genetic fusion: Application to multi-components image segmentation. In Proceedings of the International Conference on Acoustics, Speech, and Signal Processing (Vol. 4, pp. 2223-2226).
36. Rosin, P. L., & Ioannidis, E. (2003). Evaluation of global image thresholding for change detection. Pattern Recognition Letters, 24(14), 2345-2356.
37. Udupa, J. K., LeBlanc, V. R., & Schmidt, H., et al. (2002). A methodology for evaluating image segmentation algorithms. SPIE, 4684, 266-277.
38. Udupa, J., & Zhuge, Y. (2004). Delineation operating characteristic (DOC) curve for assessing the accuracy behavior of image segmentation algorithms. SPIE, 5370, 640-647.
39. Villegas, P., Marichal, X., & Salcedo, A. (1999). Objective evaluation of segmentation masks in video sequences. In Proceedings of the Workshop Image Analysis for Multimedia Interactive Services (pp. 85-88).
40. Wolf, S., & Webster, A. (1997). Subjective and objective measures of scene criticality. In ITU-T Study Groups 9 and 12, and ITU-R Study Group 11, ITU experts meeting on subjective and objective audiovisual quality assessment methods. Geneva, Switzerland, ITU, SG 12 document number JRG010.
41. Xu, Y., Olman, V., & Uberbacher, E. C. (1998). A segmentation algorithm for noisy images: Design and evaluation. Pattern Recognition Letters, 19(13), 1213-1224.
42. Yang, J., & Huang, S. C. (1999). Method for evaluation of different MRI segmentation approaches. IEEE Network Simulator, 46(6), 2259-2265.
43. Yasnoff, W. A., Galbraith, W., & Bacus, J. W. (1979). Error measures for objective assessment of scene segmentation algorithms. AQC, 1, 107-121.
44. Zhang, H., Fritts, J. E., & Goldman, S. (2004). An entropy-based objective evaluation method for image segmentation. SPIE, 5307, 38-49.
45. Zhang, H., Fritts, J. E., & Goldman, S. A. (2005). A co-evaluation framework for improving segmentation evaluation. SPIE, 5809, 420-430.
46. nd International Conference on Signal Processing (Vol. 1, pp. 870-873).
47. Zhang, Y. J. (1996). A survey on evaluation methods for image segmentation. Pattern Recognition, 29(8), 1335-1346.
48. Zhang, Y. J. (1997). Evaluation and comparison of different segmentation algorithms. PRL, 18(10), 963-974.
49. th International Symposium on Signal Processing and Its Applications (pp.148-151).
50. nd ed.) [online].
51. Zhang, Y. J., & Gerbrands, J. J. (1992a). Segmentation evaluation using ultimate measurement accuracy. SPIE, 1657, 449-460.
52. Zhang, Y. J., & Gerbrands, J. J. (1992b). On the design of test images for segmentation evaluation. In Proceedings of the EUSIPCO (Vol. 1, pp. 551-554).
53. Zhang, Y. J., & Gerbrands, J. J. (1994). Objective and quantitative segmentation evaluation and comparison. Signal Processing, 39(3), 43-54.