ProAlignNet : Unsupervised Learning for Progressively Aligning Noisy Contours
This addresses a fundamental challenge in computer vision for tasks such as mapping and annotation, though it appears incremental as it builds on existing ConvNet architectures with specific improvements for contour data.
The paper tackles the problem of aligning noisy and misaligned contour shapes in computer vision by proposing ProAlignNet, an unsupervised ConvNet that uses a novel loss function and multi-scale inference, achieving superior performance in real-world applications like geo-parcel alignment and segmentation refinement compared to state-of-the-art methods.
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.