Abstract
A ResNet-based multi-path refinement CNN is used for object contour detection. For this task, we prioritise the effective utilization of the high-level abstraction capability of a ResNet, which leads to state-of-the-art results for edge detection. Keeping our focus in mind, we fuse high, mid and low-level features in that specific order, which differs from many other approaches. The tensor with the highest-levelled features is set as the starting point to combine it layer-by-layer with features of a lower abstraction level until it reaches the lowest level. We train this network on a modified PASCAL VOC 2012 dataset for object contour detection and evaluate on a refined PASCAL-val dataset reaching an excellent performance and an Optimal Dataset Scale (ODS) of 0.752. Furthermore, by fine-training on the BSDS500 dataset, we reach state-of-the-art results for edge-detection with an ODS of 0.824.
Keywords
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Kelm, A.P., Rao, V.S., Zölzer, U. (2019). Object Contour and Edge Detection with RefineContourNet. In: Vento, M., Percannella, G. (eds) Computer Analysis of Images and Patterns. CAIP 2019. Lecture Notes in Computer Science(), vol 11678. Springer, Cham. https://doi.org/10.1007/978-3-030-29888-3_20
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DOI: https://doi.org/10.1007/978-3-030-29888-3_20
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