Two-dimensional clustering algorithms for image segmentation
WSEAS Transactions on Computers
Automated two-dimensional K-means clustering algorithm for unsupervised image segmentation
Computers and Electrical Engineering
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When we take pictures with flash, red-eye effect often appears in photographs. Flash light passing through pupil is reflected on the blood vessels, and arrives at a camera lens. This phenomenon makes red-eyes in photographs. Several algorithms have been proposed for removal of red-eyes in digital photographs. This paper proposes a red-eye removal algorithm using inpainting and eye-metric information, which is largely composed of two parts: red-eye detection and red-eye correction. For red-eye detection, face regions are detected first. Next, red-eye regions are segmented in the face regions using multi-cues such as redness, shape, and color information. By region growing, we select regions, which are to be completed with iris texture by an exemplar-based inpainting method. Then, for red-eye correction, pupils are painted with the appropriate radii calculated from the iris size and size ratio. Experimental results with a large number of test photographs with red-eye effect show that the proposed algorithm is effective and the corrected eyes look more natural than those processed by the conventional algorithms.