Segmentation d'images couleur par un opérateur gradient vectoriel ...

QUANTIFICATION ET MORPHOLOGIE DE PHASES DE CLINKER PAR ANALYSE D'IMAGE.
Figure 41. - Histogram of the grey levels of a sample image
The presence of dark crystals (alite, belite) and the bright matrix can explain the bimodality.
The optimal threshold to distinguish the regions is in the valley of the histogram. It is
situated in the upper part of the grey-scale, but depends on the image lightning and cannot be
determined a priori. The threshold value can be determined automatically with the method of
Otsu [Otsu, 1979]. This algorithm maximises the inter-class-variance in the histogram to find out
the optimal detection limit, as seen in Figure 41.
The thresholding creates a binary image (Figure 42b). The bright matrix is white and the
dark crystals are black. Actually, some clear picture elements in the alite may be wrongly counted
as part of the matrix and some dark parts of the matrix are not detected. Thanks to the small size
of such irregularities, it is possible to compensate them by region homogenisation. The applied
filter is based on the mathematical morphology. This filter eliminates the extremely small objects
and fills in some small holes in order to complete the segmentation of the matrix. A median filter
and morphological dilatation and erosion are most adapted to this role. Figure 42 shows a sample
image (a), the binary image after the thresholding (b) and the final segmentation of the matrix (c).
WORLD CEMENT RESEARCH AND DEVELOPMENT 1998 99