Multiscale Linear Feature Extraction Based on Beamlet Transform
Beamlet [1.] is an efficient tool for multiscale image analysis. A fast algorithm for discrete beamlet transform [2.] is proposed. It greatly reduces the complexity for computing the coordinates of pixels on beamlets, and concentrates the beamlet transform on summation of the pixel grayscale values. This paper also improves Donoho’s method of using complexity-penalized energy [1.] to extract multiscale linear features. It establishes the two-scale relationship of the maximal beamlet energy in the dyadic square, and presents a threshold-processed maximal beamlet energy algorithm which can avoid the problem of selecting penalty factor. Experimental results prove the efficiency of the method proposed.
KeywordsLine Segment Fast Algorithm Linear Feature Penalty Factor Horizontal Projection
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