Rotation-Robust Shape Detection Using Local Self-Similarities
スポンサーリンク
概要
- 論文の詳細を見る
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.
- 信号処理学会の論文
信号処理学会 | 論文
- A study on audio watermarking method based on the cochlear delay characteristics
- Estimation of fundamental frequency of reverberant speech by utilizing complex cepstrum analysis
- 反響音を有する畳み込み形混合過程に対するブラインドソースセパレーションの学習法
- A Model-Concept of the Selective Sound Segregation : A Prototype Model for Selective Segregation of Target Instrument Sound from the Mixed Sound of Various Instruments
- Study of Control Strategy Mimicking Speech Motor Learning for a Physiological Articulatory Model