Title :
Histogram Thresholding Using Fuzzy and Rough Measures of Association Error
Author :
Sen, Debashis ; Pal, Sankar K.
Author_Institution :
Center for Soft Comput. Res., Indian Stat. Inst., Kolkata
fDate :
4/1/2009 12:00:00 AM
Abstract :
This paper presents a novel histogram thresholding methodology using fuzzy and rough set theories. The strength of the proposed methodology lies in the fact that it does not make any prior assumptions about the histogram unlike many existing techniques. For bilevel thresholding, every element of the histogram is associated with one of the two regions by comparing the corresponding errors of association. The regions are considered ambiguous in nature, and, hence, the error measures are based on the fuzziness or roughness of the regions. Multilevel thresholding is carried out using the proposed bilevel thresholding method in a tree structured algorithm. Segmentation, object/background separation, and edge extraction are performed using the proposed methodology. A quantitative index to evaluate image segmentation performance is also proposed using the median of absolute deviation from median measure, which is a robust estimator of scale. Extensive experimental results are given to demonstrate the effectiveness of the proposed methods in terms of both qualitative and quantitative measures.
Keywords :
edge detection; estimation theory; fuzzy set theory; image segmentation; rough set theory; association error fuzzy measures; association error rough measures; background separation; bilevel thresholding; edge extraction; fuzzy set theory; histogram thresholding; image segmentation; multilevel thresholding; object separation; quantitative index; robust estimator; rough set theory; tree structured algorithm; Entropy; Fuzzy sets; Histograms; Image edge detection; Image processing; Image segmentation; Partitioning algorithms; Region 1; Robustness; Set theory; Edge extraction; histogram thresholding; image segmentation; index of fuzziness; rough entropy; segmentation evaluation;
Journal_Title :
Image Processing, IEEE Transactions on
DOI :
10.1109/TIP.2009.2012890