On the hierarchical assignment to the foreground of gray-level image subsets

International Journal of Pattern Recognition and Artificial Intelligence

Volumn 20, Issue 6, 2006, Pages 897-912

  Frucci, Maria ^a   Arcelli, Carlo ^a   Sanniti Di Baja, Gabriella ^a  

^a CNR   (Italy)

Author keywords

Binarization; Gray level images; Segmentation

Indexed keywords

ARTIFICIAL INTELLIGENCE; DEMODULATION; HIERARCHICAL SYSTEMS; IMAGE SEGMENTATION; MATHEMATICAL TRANSFORMATIONS; PATTERN RECOGNITION;

BINARIZATION; GRAY LEVEL IMAGES; HIERARCHICAL RANKING; WATERSHED TRANSFORMATION;

IMAGE PROCESSING;

EID: 33749033901     PISSN: 02180014     EISSN: None     Source Type: Journal    
DOI: 10.1142/S0218001406005034     Document Type: Conference Paper

References (15)

1. C. Arcelli and L. Serino, From discs to parts of visual form, Imag. Vis. Comput. 15 (1997) 1-10.
2. S. Beucher and C. Lantuejoul, Use of watersheds in contour detection, Proc. Int. Workshop on Image Processing, Real-Time Edge and Motion Detection/Estimation, Rennes, France (1979).
3. S. Beucher and F. Meyer, The morphological approach of segmentation: the watershed transformation, in Mathematical Morphology in Image Processing, ed. E. Dougherty, (Marcel Dekker, NY, 1993), pp. 433-481.
4. J. C. Bezdek, L. A. Hall and L. P. Clarke, Review of MR, image segmentation techniques using pattern recognition, Med. Phys. 20 (1993) 1033-1048.
5. D. P. Buxhoeveden, A. S. Switala, E. Roy and M. F. Casanova, Quantitative analysis of cell columns in the cerebral cortex, J. Neurosci. Meth. 97 (2000) 7-17.
6. H. D. Cheng, X. H. Jiang, Y. Sun and J. Wang, Color image segmentation: advances and prospects, Patt. Recogn. 34 (2001) 2259-2281.
7. L. Cruz, S. V. Bulyrev, S. Peng, D. L. Roe, B. Urbane, H. E. Stanley and D. L. Rosene, A statistically based density map method for identification and quantification of regional differences in microcolumnarity in the monkey brain, J. Neurosci. Meth. 41(2) (2005) 321-332.
8. M. Frucci, Oversegmentation reduction by flooding regions and digging watershed lines, Int. J. Patt. Recogn. Artif. Intell. 20(1) (2006) 15-38.
9. M. Frucci, C. Arcelli and G. Sanniti di Baja, Detecting and ranking foreground regions in gray-level images, in Brain, Vision and Artificial Intelligence, eds. M. De Gregorio et al., Lecture Notes in Computer Science, Vol. 3704 (Springer Verlag, 2005), pp. 409-418.
10. R. C. Gonzalez and P. Wintz, Digital Image Processing (Addison-Wesley, Reading, MA, 1987).
11. R. M. Haralick and L. G. Shapiro, Image segmentation techniques, Comput. Vis. Graph. Im. Proc. 29 (1985) 100-132.
12. H.-H. Oh, K.-T. Tim and S.-I. Chien, An improved binarization algorithm based on a water flow model for document image with inhomogeneous background, Patt. Recogn. 38 (2005) 2612-2625.
13. N. R. Pal and S. K. Pal, A review on image segmentation techniques, Patt. Recogn. 26 (1993) 1277-1294.
14. D. L. Pham, C. Xu and J. L. Prince, Current methods in medical image segmentation, Ann. Rev. Biomed. Engin. 2 (2000) 315-337.
15. P. K. Sahoo, S. Soltani, A. K. C. Wong and Y. C. Chen, A survey of thresholding techniques, Comput. Vis. Graph. Im. Proc. 41 (1988) 233-260.