The classifier judged this item not relevant to AI security, so it is left out of lists, feeds, the API and dataset releases. If that is wrong, report a correction on this page.
Rethinking Rotation-Invariant Recognition of Fine-Grained Shapes From the Perspective of Contour Points
- Published
- Record updated
Summary
The paper reconsiders rotation-invariant shape recognition from the perspective of contour points rather than pixels, arguing that pixel-based methods carry redundant information and leak critical geometric information early. It proposes an anti-noise rotation-invariant convolution module based on contour geometric awareness, which divides the shape contour into local geometric regions (LGA) for finer-grained rotation-invariant coding. A deep network of five cascaded modules shows excellent performance on classification and retrieval experiments and robustness to contour noise and rotation centers.