Objective

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Bronzes are one of China's cultural treasures. However

most unearthed bronzes are broken and deformed

and restoration is needed to protect them. Recently

digital restoration technologies for cultural relics have attracted considerable attention with the development of 3D laser scanning technologies and the research progress in digital geometry processing. Patterns of the adjacent pieces of a broken bronze should be aligned during restoration to ensure the continuity of the patterns and guarantee high restoration quality. Consequently

the extraction of bronze patterns is a significant step in the restoration process.

Method

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Generally

bronze patterns have apparent sharp edges

which distinguish decoration feature parts from non-decoration feature parts. Therefore

an algorithm for enhancing and extracting the sharp features of bronzes

which aims to extract pattern features

is proposed and implemented in this study. No interactive parameter setting is needed in the proposed algorithm

and the feature points are extracted automatically. First

a weighted projection distance is proposed to eliminate the adverse effect of the nonuniformity of the mesh on feature extraction. The projection distance of a vertex is the absolute value of the dot product between the normal at the vertex and the vector from the vertex to the center point of its one-ring neighborhood vertices. The projection distance of a vertex on sharp edges is always larger than that on non-sharp edges for a uniform triangular mesh model. Thus

it is easy to distinguish the feature point in this case. However

it is difficult to distinguish the feature point for the nonuniform mesh

which is more general in application. Therefore

the weighted projection distance

which means that all edges in the one-ring neighborhood of a vertex should be normalized first before calculating the projection distance

is more adaptive than the traditional projection distance for the triangular meshes in general. Then

reverse bilateral filtering is proposed and utilized to generate an anti-sharpening mask to enhance the weighted projection distance because the feature points are not obvious in the reconstructed mesh model of a real bronze as a result of the digital nature of the scanning process. The anti-sharpening mask is a three-step image enhancement process. First

the original image is filtered and subtracted from the smoothed image to obtain the weight. Second

the mask is added to the original image. Finally

the details of the image are enhanced. Reverse bilateral filtering is developed to filter all weighted projection distances with the intention to smooth all weighted projection distances of the feature points to the maximum extent instead of preserving features in bilateral filtering. The enhanced weighted projection distance is obtained by performing the three-step anti-sharpening mask in image enhancement. Consequently

a large weighted projection distance becomes even larger and a small weighted projection distance becomes even smaller. Finally

Otsu's method is applied to the histogram of the enhanced weighted projection distance to determine the optimal threshold automatically

and the vertices of the mesh model are classified into feature and non-feature point sets with the threshold.

Result

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We compare the extraction results of our algorithm with those of Tran's algorithm with the scanned bronze models

including before and after weighted projection distance enhancement. All experiments show that better extraction results can be achieved with the proposed algorithm than the existing algorithms

and the identified feature points of the proposed algorithm are more continuous than those of the existing algorithms. The time consumed by all of the three models with vertex numbers between 6 000 and 800 000 is less than 10 s. Therefore

the proposed algorithm is effective

and its results are beneficial for the succeeding process.

Conclusion

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The decoration features of bronzes can be extracted automatically and efficiently with the proposed algorithm.