Ck. Yang et Wh. Tsai, REDUCTION OF COLOR SPACE DIMENSIONALITY BY MOMENT-PRESERVING THRESHOLDING AND ITS APPLICATION FOR EDGE-DETECTION IN COLOR IMAGES, Pattern recognition letters, 17(5), 1996, pp. 481-490
Citations number
26
Categorie Soggetti
Computer Sciences, Special Topics","Computer Science Artificial Intelligence
A method for reduction of color space dimensionality by moment-preserv
ing thresholding and its application in edge detection for color image
s is proposed. An input color image is partitioned into n x n non-over
lapping blocks. A moment-preserving thresholding technique is then app
lied individually to each color plane of each image block. Two sets of
(R, G, B) tristimulus values are obtained from the thresholding resul
ts to form two representative color vectors for each block. The differ
ence vector between these two representative color vectors is used as
an axis onto which all the data in the block are projected to reduce t
he color space to one dimension, A single-spectral image block is so o
btained. Due to the use of analytic formulas in the thresholding step,
the proposed dimensionality reduction method is found faster than the
KL expansion or vector median approaches which are also applicable fo
r dimensionality reduction. An (n + 1)x(n + 1) circular window is sele
cted to sample the resulting single-spectral image, which in turn incl
udes the n x n square block. Some mass moments of the window data are
computed and used for edge detection in the circular window. Due to th
e use of the larger detection window which results in smaller overlapp
ing detection areas, the computation time for the edge detection step
is reduced, compared with other similar approaches using overlapping d
etection windows. Experimental results show that the proposed approach
is effective in reduction of color space dimensionality and edge dete
ction in color images.