Complex image line feature extraction based on improved Beamlet transform and the Canny operator
改进的Beamlet与Canny相结合提取复杂图像线特征

Traditional line feature detection methods based on structureless algorithms of the Beamlet transform not only suffer from overlapping and ambiguities, they also can not detect the target information effectively. Moreover, they can not describe the detail information when extracting the line features of a complex image. Therefore, we propose a new line feature extraction algorithm based on an improved Beamlet transform and the Canny operator. First, the Beamlet transform is performed. There is at most one optimal Beamlet in a dyadic square after improving the Beamlet structureless algorithm and using the new drawing rule and the new energy function. Second, the Canny operator for edge detection is used with a larger Sigma in order to detect only obvious edges. Finally, line feature are detected by a combination of both. The algorithm is evaluated under several aspects, such as the continuity of the line feature extraction, the false detection rate and the miss detection rate. Moreover, this method is compared to existing methods. The experimental results show that our proposed method not only overcomes their weakness such as fractureing, overlapping, sambiguities, false edges and so on, but also effectively improves the accuracy and continuity when extracting line feature of complex image.