The paper proposes AdaContour, an adaptive contour descriptor that uses a hierarchical representation to accurately model complex object shapes. Existing angle-based contour descriptors, such as Eigen and ESE, suffer from lossy representations for non-starconvex shapes due to their use of a single global inner center and polar coordinate parameterization.
To address this, the paper introduces a novel hierarchical encoding procedure that recursively subdivides the shape until sufficiently regular regions are identified. Each refined region is then encoded by a local contour, allowing AdaContour to better capture irregular boundaries. The paper also utilizes robust subspace recovery techniques and basis-sharing mechanisms to efficiently represent the local contours.
Experiments show that AdaContour outperforms existing angle-based descriptors in accurately representing complex shapes, while retaining computational efficiency. The paper also demonstrates the integration of AdaContour into an object detection framework for instance segmentation, exhibiting favorable performance compared to conventional methods.
The key highlights of the paper are:
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arxiv.org
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