Augmenting text document by on-line learning of local arrangement of keypoints

Hideaki Uchiyama, Hideo Saito

研究成果: Chapter in Book/Report/Conference proceedingConference contribution

15 被引用数 (Scopus)

抄録

We propose a technique for text document tracking over a large range of viewpoints. Since the popular SIFT or SURF descriptors typically fail on such documents, our method considers instead local arrangement of keypoints. We extends Locally Likely Arrangement Hashing (LLAH), which is limited to fronto-parallel images: We handle a large range of viewpoints by learning the behavior of keypoint patterns when the camera viewpoint changes. Our method starts tracking a document from a nearly frontal view. Then, it undergoes motion, and new configurations of keypoints appear. The database is incrementally updated to reflect these new observations, allowing the system to detect the document under the new viewpoint. We demonstrate the performance and robustness of our method by comparing it with the original LLAH.

本文言語英語
ホスト出版物のタイトルScience and Technology Proceedings - IEEE 2009 International Symposium on Mixed and Augmented Reality, ISMAR 2009
ページ95-98
ページ数4
DOI
出版ステータス出版済み - 12 1 2009
外部発表はい
イベント8th IEEE 2009 International Symposium on Mixed and Augmented Reality, ISMAR 2009 - Science and Technology - Orlando, FL, 米国
継続期間: 10 19 200910 22 2009

出版物シリーズ

名前Science and Technology Proceedings - IEEE 2009 International Symposium on Mixed and Augmented Reality, ISMAR 2009

その他

その他8th IEEE 2009 International Symposium on Mixed and Augmented Reality, ISMAR 2009 - Science and Technology
Country米国
CityOrlando, FL
Period10/19/0910/22/09

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Science Applications

フィンガープリント 「Augmenting text document by on-line learning of local arrangement of keypoints」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。

引用スタイル