End-to-End Trainable Deep Active Contour Models for Automated Image Segmentation: Delineating Buildings in Aerial Imagery

• URL: http://arxiv.org/abs/2007.11691v1
• Date: Wed, 22 Jul 2020 21:27:17 GMT
• Title: End-to-End Trainable Deep Active Contour Models for Automated Image Segmentation: Delineating Buildings in Aerial Imagery
• Authors: Ali Hatamizadeh, Debleena Sengupta, Demetri Terzopoulos
• Abstract summary: Trainable Deep Active Contours (TDACs) is an automatic image segmentation framework that unites Convolutional Networks (CNNs) and Active Contour Models (ACMs) TDAC yields fast, accurate, and fully automatic simultaneous delineation of arbitrarily many buildings in the image.
• Score: 12.442780294349049
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The automated segmentation of buildings in remote sensing imagery is a challenging task that requires the accurate delineation of multiple building instances over typically large image areas. Manual methods are often laborious and current deep-learning-based approaches fail to delineate all building instances and do so with adequate accuracy. As a solution, we present Trainable Deep Active Contours (TDACs), an automatic image segmentation framework that intimately unites Convolutional Neural Networks (CNNs) and Active Contour Models (ACMs). The Eulerian energy functional of the ACM component includes per-pixel parameter maps that are predicted by the backbone CNN, which also initializes the ACM. Importantly, both the ACM and CNN components are fully implemented in TensorFlow and the entire TDAC architecture is end-to-end automatically differentiable and backpropagation trainable without user intervention. TDAC yields fast, accurate, and fully automatic simultaneous delineation of arbitrarily many buildings in the image. We validate the model on two publicly available aerial image datasets for building segmentation, and our results demonstrate that TDAC establishes a new state-of-the-art performance.

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