  The surfaces of many cultural heritage objects were embellished with various
patterns, especially curve patterns. In practice, most of the unearthed
cultural heritage objects are highly fragmented, e.g., sherds of potteries or
vessels, and each of them only shows a very small portion of the underlying
full design, with noise and deformations. The goal of this paper is to address
the challenging problem of automatically identifying the underlying full design
of curve patterns from such a sherd. Specifically, we formulate this problem as
template matching: curve structure segmented from the sherd is matched to each
location with each possible orientation of each known full design. In this
paper, we propose a new two-stage matching algorithm, with a different matching
cost in each stage. In Stage 1, we use a traditional template matching, which
is highly computationally efficient, over the whole search space and identify a
small set of candidate matchings. In Stage 2, we derive a new matching cost by
training a dual-source Convolutional Neural Network (CNN) and apply it to
re-rank the candidate matchings identified in Stage 1. We collect 600 pottery
sherds with 98 full designs from the Woodland Period in Southeastern North
America for experiments and the performance of the proposed algorithm is very
competitive.
