  The Active Contour Model (ACM) is a standard image analysis technique whose
numerous variants have attracted an enormous amount of research attention
across multiple fields. Incorrectly, however, the ACM's
differential-equation-based formulation and prototypical dependence on user
initialization have been regarded as being largely incompatible with the
recently popular deep learning approaches to image segmentation. This paper
introduces the first tight unification of these two paradigms. In particular,
we devise Deep Convolutional Active Contours (DCAC), a truly end-to-end
trainable image segmentation framework comprising a Convolutional Neural
Network (CNN) and an ACM with learnable parameters. The ACM's Eulerian energy
functional includes per-pixel parameter maps predicted by the backbone CNN,
which also initializes the ACM. Importantly, both the CNN and ACM components
are fully implemented in TensorFlow, and the entire DCAC architecture is
end-to-end automatically differentiable and backpropagation trainable without
user intervention. As a challenging test case, we tackle the problem of
building instance segmentation in aerial images and evaluate DCAC on two
publicly available datasets, Vaihingen and Bing Huts. Our reseults demonstrate
that, for building segmentation, the DCAC establishes a new state-of-the-art
performance by a wide margin.
