Shape recognition using orientational and morphological scale-spaces of curvatures

In this study, a scale-invariant representation for closed planar curves (silhouettes) is proposed. The orientations of all points within the Gaussian scale-space of the curve are extracted. This orientation scale-space is used to create the silhouette orientation image in which the positions of each pixel indicate the curve´s pixel positions and scales, whereas the colour represents orientation. The representation is extracted for multiple levels of the morphological scale-space of the silhouette. The proposed representation is invariant to scale and transformable under planar rotation. Using linear and non-linear distance learning methods, experiments on the MPEG7, ETH80 and Kimia shape datasets were conducted, with results indicating an advanced recognition capability.