抜粋
This paper tries to recognize 3-dimensional objects by using an evolutional RBF network. Our proposed RBF network has the structure of preparing four RBFs for each hidden layer unit, selecting based on the Euclid distance between an input image and RBF. This structure can be invariant to 2- dimensional rotation by 90 degree. The other rotational invariance can be achieved by the RBF network. In hidden layer units, the number of RBFs, form, and arrangement are determined using real-coded GA. Computer simulations show object recognition can be done using such a method.
| 元の言語 | English |
|---|---|
| ページ（範囲） | 556-562 |
| ページ数 | 7 |
| ジャーナル | Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science) |
| 巻 | 2773 PART 1 |
| 出版物ステータス | Published - 2003 12 1 |
| 外部発表 | Yes |
| イベント | 7th International Conference, KES 2003 - Oxford, United Kingdom 継続期間: 2003 9 3 → 2003 9 5 |
ASJC Scopus subject areas
- Theoretical Computer Science
- Computer Science(all)
フィンガープリント 3-Dimensional object recognition by evolutional RBF network' の研究トピックを掘り下げます。これらはともに一意のフィンガープリントを構成します。
これを引用
Matsuda, H., Mitsukura, Y., Fukumi, M., & Akamatsu, N. (2003). 3-Dimensional object recognition by evolutional RBF network. Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science), 2773 PART 1, 556-562.