  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.
