Fast image segmentation based on multi-resolution analysis and wavelets
An efficient algorithm for image segmentation based on a multi-resolution application of a wavelets transform and feature distribution is presented. The original feature space is transformed into a lower resolution with a wavelets transform to derive fast computation of the optimum threshold value in a feature space. Based on this lower resolution version of the given feature space, a single feature value or multiple feature values are determined as the optimum threshold values. The optimum feature values, which are in the lower resolution, are projected onto the original feature space. In this step a refinement procedure may be added to detect the optimum threshold value. Experimental results for the proposed algorithm indicate feasibility and reliability for fast image segmentation.
image segmentation multi-resolution analysis wavelets transform feature space multi-threshold