Choosing how to encode a real-world problem as a machine learning task is an important design decision in machine learning. The task of glacier calving front modeling has often been approached as a semantic segmentation task. Recent studies have shown that combining segmentation with edge detection can improve the accuracy of calving front detectors. Building on this observation, we completely rephrase the task as a contour tracing problem and propose a model for explicit contour detection that does not incorporate any dense predictions as intermediate steps. The proposed approach, called ``Charting Outlines by Recurrent Adaptation'' (COBRA), combines Convolutional Neural Networks (CNNs) for feature extraction and active contour models for the delineation. By training and evaluating on several large-scale datasets of Greenland's outlet glaciers, we show that this approach indeed outperforms the aforementioned methods based on segmentation and edge-detection. Finally, we demonstrate that explicit contour detection has benefits over pixel-wise methods when quantifying the models' prediction uncertainties. The project page containing the code and animated model predictions can be found at \url{https://khdlr.github.io/COBRA/}.

翻译：将现实世界问题编码为机器学习任务的方式是机器学习中的重要设计决策。冰川崩解前缘建模任务通常被当作语义分割任务来处理。近期研究表明，将分割与边缘检测相结合可提升崩解前缘检测器的精度。基于这一观察，我们彻底将该任务重新定义为轮廓追踪问题，并提出一种无需任何中间密集预测步骤的显式轮廓检测模型。所提出的方法名为"通过递归适应绘制轮廓"（COBRA），该方法融合了用于特征提取的卷积神经网络（CNN）与用于勾勒的主动轮廓模型。通过在格陵兰岛多个出海口冰川的大规模数据集上进行训练与评估，我们证明该方法确实优于前述基于分割与边缘检测的方法。最后，我们证明在量化模型预测不确定性时，显式轮廓检测相比逐像素方法具有优势。包含代码与动画模型预测的项目页面可访问网址 \url{https://khdlr.github.io/COBRA/}。