Image linear feature extraction based on improved structureless algorithms of beamlet transform
Jiexian Zeng · 2010
Traditional linear feature detection methods based on structureless algorithms of Beamlet transform are mostly used to detect simple line segments and curves,while fail to detect complicated edges in natural images.Wavelet transform has great advantages in point feature detection,meaning that it is good at detecting edge and details.In this paper we improve traditional methods with the help of wavelet.Meanwhile,energy function in traditional algorithm is improved and a new drawing linear feature rule is proposed in order to represent a dyadic square with at most one optimal Beamlet.First,image is decomposed into low frequency and high frequencies with wavelet to highlight edge detail feature;second,the edge image' s transform coefficients are obtained by Beamlet transform.Finally the coefficients are dealt with using the improved energy function and linear features are extracted following the new drawing rule.Experimental results show that without costing obvious extra computing time,our proposed method can extract complete and clear linear features in natural images.