建水指林寺明代壁画病害区域的自动标定算法

Automatic Calibration Algorithm of Disease Areas of Ming Dynasty Murals in Jianshui Zhilin Temple

针对云南建水指林寺明代壁画的病害区域标定问题,提出一种裂隙与脱落病害的自动标定算法.首先在HSV空间中对壁画图像进行多维梯度检测,提取壁画的纹理和线条特征;然后利用导向滤波抑制壁画中的绘画线条,对滤波后的图像进行自动阈值分割,得到壁画病害区域的初始掩膜;再利用张量投票方法连接初始掩膜中不连续的边缘曲线,通过形态学孔洞填充得到完整的壁画病害区域掩膜;最后将病害掩膜添加到原始壁画图像,实现壁画裂隙与脱落病害区域的自动标定.实验结果表明,所提算法在自制的48幅指林寺壁画数据集上取得了当前最好的标定效果,准确率和召回率分别达到78.9%和69.5%,F-Measure值比最优对比方法提高18个百分点,而且不需要人机交互,具有更高的计算效率.

Aiming at the problem of disease area calibration of the Ming Dynasty Murals in Zhilin Temple,Jianshui,Yunnan Province,this paper proposes an automatic calibration algorithm of crack and flaking disease.Firstly,the algorithm detects the multi-dimensional gradient of the mural image in the HSV space,and extracts the texture and line features of the mural.Secondly,the guided filtering is used to suppress the painting lines in the mural,and the filtered image is segmented by an adaptive threshold to generate the initial mask of the mural disease areas.Thirdly,the discontinuous edge curves in the initial mask are connected by using a tensor voting method,and the complete mural disease area mask is obtained by using morphological hole filling.Finally,the disease mask is added to the original mural image to achieve automatic calibration of the mural cracks and flaking disease areas.Experimental results show that the proposed algorithm achieves the best calibration effect on the self-made 48 Zhilin Temple mural datasets.The precision and recall reach 78.9%and 69.5%respectively.The F-Measure value is 18 percentage points higher than the optimal comparison method.Moreover,the proposed algorithm does not require human-computer interaction,and has higher computational efficiency.