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We develop a new contour tracing algorithm to enhance the results of thelatest object contour detectors. The goal is to achieve a perfectly closed, 1pixel wide and detailed object contour, since this type of contour could beanalyzed using methods such as Fourier descriptors. Convolutional NeuralNetworks (CNNs) are rarely used for contour tracing. However, we find CNNs aretailor-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 makethe first step, we train the CNN only on ship contours, but the principle isalso applicable to other objects. Input data are the image and the associatedobject contour prediction of the recently published RefineContourNet. The WtLgets a center pixel, which defines an input section and an angle for rotatingthis section. Ideally, the center pixel moves on the contour, while the angledescribes upcoming directional contour changes. The WtL predicts its stepspixelwise in a selfrouting way. To obtain a complete object contour the WtLruns in parallel at different image locations and the traces of its individualpaths are summed. In contrast to the comparable Non-Maximum Suppression method,our approach produces connected contours with finer details. Finally, theobject contour is binarized under the condition of being closed. In case allprocedures work as desired, excellent ship segmentations with high IoUs areproduced, showing details such as antennas and ship superstructures that areeasily omitted by other segmentation methods.

Walk the Lines: Object Contour Tracing CNN for Contour Completion of Ships