Image segmentation algorithm development using ground truth image data sets

Computer Vision and Image Understanding

Volumn 112, Issue 2, 2008, Pages 143-159

  Crevier, Daniel ^a  

^a Ophthalmos Systems Inc   (Canada)

Author keywords

Computer vision; Data engineering tools and techniques; Edge detection; Ground truth images; Image data sets; Image segmentation; Parameter learning; Performance measures; Region growing; Stochastic optimization; Texture

Indexed keywords

ALGORITHMS; BOOLEAN FUNCTIONS; COMPUTER VISION; DIGITAL IMAGE STORAGE; EDGE DETECTION; FEATURE EXTRACTION; IMAGE PROCESSING; IMAGE SEGMENTATION; OPTIMIZATION; PATTERN RECOGNITION; SET THEORY; TEXTURES;

DATA ENGINEERING TOOLS AND TECHNIQUES; GROUND TRUTH IMAGES; IMAGE DATA SETS; PARAMETER LEARNING; PERFORMANCE MEASURES; REGION GROWING; STOCHASTIC OPTIMIZATION;

IMAGE MATCHING;

EID: 54249155178     PISSN: 10773142     EISSN: 1090235X     Source Type: Journal    
DOI: 10.1016/j.cviu.2008.02.002     Document Type: Article

References (24)

1. Abdou I., and Pratt W. Quantitative design and evaluation of enhancement/thresholding edge detectors. Proceedings of the IEEE 67 5 (1979) 753-763
2. Campbell N.W., Mackeown W.P.J., and Thomas B.T. Interpreting image databases by region classification. Pattern Recognition 30 4 (1997) 555-563
3. Glover F., and Laguna M. Tabu Search (1997), Kluwer, Norwell, MA
4. G. Guy, G. Medioni, Inferring perceptual contours from local features, in: Proceedings: Image Understanding Workshop, sponsored by Defense Advanced Projects Agency, April 1993, pp. 881-892.
5. Jain A.K. Fundamentals of Digital Image Processing (1989), Prentice Hall (p. 67)
6. Mackeown W.P.J., Greenway P.G., Thomas B.T., and Wright W.A. Road recognition with a neural network. Engineering Applications of Artificial Intelligence 7 2 (1994) 169-176
7. Martin D., Fowlkes C., Tal D., and Malik J. A database of human segmented natural images and its applications to evaluating segmentation algorithms and measuring ecological statistics. ICCV Vancouver (2001) 416-425
8. Martin D., Fowlkes C., and Malik J. Learning to detect natural image boundaries using local brightness, color, and texture cues. PAMI 26 5 (2004) 530-549
9. Morey C., Scales J., and Van Vleck E.S. A feedback algorithm for determining search parameters for Monte Carlo optimization. Journal of Computational Physics 146 (1998) 263-281
10. Nock R., and Nielsen F. Statistical region merging. PAMI 26 11 (2004) 1452-1458
11. Puzicha J., Rubner Y., Tomasi C., and Buhmann J. Empirical evaluation of dissimilarity measures for color and texture. ICCV (1999)
12. C. Van Rijsbergen, Information Retrieval, second ed., Department of Computer Science, University of Glasgow, 1979.
13. M. Everingham, H. Muller, B. Thomas, Evaluating image segmentation algorithms using the Pareto Front, vol. IV, ECCV, 2002, pp. 34-48.
14. Freixenet J., Munoz X., Raba D., Marti J., and Cuffi X. Yet another survey on image segmentation: region and boundary information integration. ACCV (2002) 408-422
15. V. Mezaris, I. Kompatsiaris, M.G. Strintzis, Still Image Objective Segmentation Evaluation using Ground Truth, in: Fifth COST 276 Workshop, 2003, pp. 9-14.
16. Cardoso J.S., and Corte-Real L. Toward a generic evaluation of image segmentation. IEEE Transactions on Image Processing 14 11 (2005)
17. R. Unnikrishann, C. Pantofaru, M. Hebert, A measure for objective evaluation of image segmentation algorithms, CVPR 2005 June 2005.
18. R. Unnikrishann, M. Hebert, Measures of similarity, in: Seventh IEEE Workshop on Applications of Computer Vision, January 2005, pp. 394-400.
19. Q. Huang, B. Dom, Quantitative methods of evaluating image segmentation, in: Proceedings of the 1995 International conference on Image Processing (ICIP'95).
20. Zhang Y.J. A survey on evaluation methods for image segmentation. Pattern Recognition 29 8 (1996) 1335-1346
21. D. Martin, An Empirical Approach to Grouping and Segmentation, Ph.D. dissertation, University of California, Berkeley, 2002.
22. Yasnoff W.A., Mui J.K., and Bacus J.W. Error measures for scene segmentation. Pattern Recognition 29 (1977) 1335-1346
23. Shi J., and Malik J. Normalized cuts and image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence 22 8 (2000) 888-905
24. Tu Z., and Zhu S.C. Image segmentation by data driven Markov chain Monte Carlo. IEEE Transactions on Pattern Analysis and Machine Intelligence 24 5 (2002) 657-673