Contour shape alignment is a fundamental but challenging problem in computer
vision, especially when the observations are partial, noisy, and largely
misaligned. Recent ConvNet-based architectures that were proposed to align
image structures tend to fail with contour representation of shapes, mostly due
to the use of proximity-insensitive pixel-wise similarity measures as loss
functions in their training processes. This work presents a novel ConvNet,
"ProAlignNet" that accounts for large scale misalignments and complex
transformations between the contour shapes. It infers the warp parameters in a
multi-scale fashion with progressively increasing complex transformations over
increasing scales. It learns --without supervision-- to align contours,
agnostic to noise and missing parts, by training with a novel loss function
which is derived an upperbound of a proximity-sensitive and local
shape-dependent similarity metric that uses classical Morphological Chamfer
Distance Transform. We evaluate the reliability of these proposals on a
simulated MNIST noisy contours dataset via some basic sanity check experiments.
Next, we demonstrate the effectiveness of the proposed models in two real-world
applications of (i) aligning geo-parcel data to aerial image maps and (ii)
refining coarsely annotated segmentation labels. In both applications, the
proposed models consistently perform superior to state-of-the-art methods.

提出一个新的 ConvNet 模型 ProAlignNet，通过使用局部相关的相似度度量来训练，能够有效地解决非全局对齐的边缘形状对齐问题，在现实场景下的两个应用中得到了优于现有方法的结果表现。