自己類似特徴量を用いた回転にロバストな形状検出

Abstract

This paper proposes a rotation-robust detection method of images with resembling shapes using the local self-similarities. In particular, images do not necessarily share common visual properties such as colors, edges, and textures. Although the local self-similarity is effective for shape detection among such images, it lacks the robustness to image rotation, so that it is unable to match images of the same object in different orientations. We combine the center voting method with the self-similarity descriptor in order for giving the robustness to image rotation, where the orientation is assigned to each descriptor. After matching those oriented descriptors across images, the center voting is performed in the groups of the same angular difference between the assigned orientations of matched descriptors. The rotation robustness of the proposed method was proved by demonstrating experimental results.