论文标题
行走线：对象轮廓跟踪CNN进行轮廓完成船舶
Walk the Lines: Object Contour Tracing CNN for Contour Completion of Ships
论文作者
论文摘要
我们开发了一种新的轮廓跟踪算法，以增强最新对象轮廓检测器的结果。目的是实现完美封闭的1像素宽且详细的对象轮廓，因为可以使用诸如傅立叶描述符之类的方法分析这种类型的轮廓。卷积神经网络（CNN）很少用于轮廓跟踪。但是，我们发现CNN是针对此任务量身定制的，这就是为什么我们展示行走线（WTL）算法的原因，这是一种经过训练以遵循对象轮廓的标准回归CNN的原因。为了迈出第一步，我们仅在船舶轮廓上训练CNN，但该原理也适用于其他物体。输入数据是最近发布的RefineContournet的图像和关联对象轮廓预测。 WTL获得了一个中心像素，该中心像素定义了一个输入部分和用于旋转此部分的角度。理想情况下，中心像素在轮廓上移动，而角度描述了即将到来的方向轮廓变化。 WTL以一种自我的方式预测其步骤pixelwise。为了获得一个完整的对象轮廓，WTL在不同的图像位置并行运行，并将其单个路径的痕迹求和。与可比的非最大抑制方法相反，我们的方法产生了连接的轮廓，并具有更细节的细节。最后，在关闭的条件下对物体轮廓进行二进制。如果所有程序都可以根据需要工作，则会生产出优质的船舶分割，并显示诸如天线和船舶上层建筑之类的细节，这些细节很容易被其他细分方法省略。
We develop a new contour tracing algorithm to enhance the results of the latest object contour detectors. The goal is to achieve a perfectly closed, 1 pixel wide and detailed object contour, since this type of contour could be analyzed using methods such as Fourier descriptors. Convolutional Neural Networks (CNNs) are rarely used for contour tracing. However, we find CNNs are tailor-made for this task and that's why we present the Walk the Lines (WtL) algorithm, a standard regression CNN trained to follow object contours. To make the first step, we train the CNN only on ship contours, but the principle is also applicable to other objects. Input data are the image and the associated object contour prediction of the recently published RefineContourNet. The WtL gets a center pixel, which defines an input section and an angle for rotating this section. Ideally, the center pixel moves on the contour, while the angle describes upcoming directional contour changes. The WtL predicts its steps pixelwise in a selfrouting way. To obtain a complete object contour the WtL runs in parallel at different image locations and the traces of its individual paths are summed. In contrast to the comparable Non-Maximum Suppression method, our approach produces connected contours with finer details. Finally, the object contour is binarized under the condition of being closed. In case all procedures work as desired, excellent ship segmentations with high IoUs are produced, showing details such as antennas and ship superstructures that are easily omitted by other segmentation methods.
