  Motivated by the important archaeological application of exploring cultural
heritage objects, in this paper we study the challenging problem of
automatically segmenting curve structures that are very weakly stamped or
carved on an object surface in the form of a highly noisy depth map. Different
from most classical low-level image segmentation methods that are known to be
very sensitive to the noise and occlusions, we propose a new supervised
learning algorithm based on Convolutional Neural Network (CNN) to implicitly
learn and utilize more curve geometry and pattern information for addressing
this challenging problem. More specifically, we first propose a Fully
Convolutional Network (FCN) to estimate the skeleton of curve structures and at
each skeleton pixel, a scale value is estimated to reflect the local curve
width. Then we propose a dense prediction network to refine the estimated curve
skeletons. Based on the estimated scale values, we finally develop an adaptive
thresholding algorithm to achieve the final segmentation of curve structures.
In the experiment, we validate the performance of the proposed method on a
dataset of depth images scanned from unearthed pottery sherds dating to the
Woodland period of Southeastern North America.
