一种用于描绘冰川崩解前沿的深度活动轮廓模型
A Deep Active Contour Model for Delineating Glacier Calving Fronts
AI总结：
本文将冰川崩解前沿描绘重构为轮廓追踪任务，提出结合CNN与活动轮廓模型的COBRA，在格陵兰冰川数据集上优于分割和边缘检测方法，并改善不确定性量化。
摘要：
选择如何将现实世界问题编码为机器学习任务，是机器学习中的一项重要设计决策。冰川崩解前沿建模任务通常被作为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以提高崩解前沿检测器的精度。基于这一观察，我们将该任务彻底重新表述为轮廓追踪问题，并提出一种用于显式轮廓检测的模型，该模型不包含任何密集预测作为中间步骤。所提出的方法名为“Charting Outlines by Recurrent Adaptation”（COBRA），结合了用于特征提取的卷积神经网络（CNNs）和用于描绘的活动轮廓模型。通过在格陵兰岛溢出冰川的若干大规模数据集上进行训练和评估，我们表明该方法确实优于上述基于分割和边缘检测的方法。最后，我们证明在量化模型的预测不确定性时，显式轮廓检测相比逐像素方法具有优势。包含代码和动画模型预测的项目页面可在 https://khdlr.github.io/COBRA/ 找到。
英文摘要：
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/}.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
