改进的Beamlet与Canny相结合提取复杂图像线特征

传统Beamlet无结构算法在提取图像线特征时不仅存在重叠模糊的缺陷,而且在提取复杂图像线特征时不能有效地检测出目标信息,细节特征更是难以刻画。针对这些问题,提出将改进的Beamlet无结构算法与Canny算子相结合的方法提取复杂图像的线特征。首先,对图像进行Beamlet变换,通过改进Beamlet无结构算法,采用新的能量统计和制定新的划线规则,以保证每个二进方块最多有一条最优基;然后,对图像用Canny算子检测边缘,通过选取较大的Sigma,只检测明显的大边缘;最后,两者结合得到图像的线特征。从检测的线特征的线型连接程度等方面对该算法的性能进行了评价,并与现有的方法进行了比较,实验结果表明,该方法克服了两种方法单独提取线特征时存在的断裂、重叠、模糊和虚假边缘的缺点,有效地提高了复杂图像线特征提取的准确性和连续性。

Complex image line feature extraction based on improved Beamlet transform and the Canny operator

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.