Hand posture recognition using adaboost with SIFT for human robot interaction

Chieh Chih Wang*, Ko Chih Wang

*此作品的通信作者

研究成果: 書貢獻/報告類型會議論文篇章

59 引文 斯高帕斯(Scopus)

摘要

Hand posture understanding is essential to human robot interaction. The existing hand detection approaches using a Viola-Jones detector have two fundamental issues, the degraded performance due to background noise in training images and the in-plane rotation variant detection. In this paper, a hand posture recognition system using the discrete Adaboost learning algorithm with Lowe's scale invariant feature transform (SIFT) features is proposed to tackle these issues simultaneously. In addition, we apply a sharing feature concept to increase the accuracy of multi-class hand posture recognition. The experimental results demonstrate that the proposed approach successfully recognizes three hand posture classes and can deal with the background noise issues. Our detector is in-plane rotation invariant, and achieves satisfactory multi-view hand detection.

原文英語
主出版物標題Recent Progress in Robotics
主出版物子標題Viable Robotic Service to Human
編輯Sukhan Lee, Il Hong Suh, Kim Mun Sang
頁面317-329
頁數13
DOIs
出版狀態已發佈 - 2008
對外發佈

出版系列

名字Lecture Notes in Control and Information Sciences
370
ISSN(列印)0170-8643

ASJC Scopus subject areas

  • 圖書館與資訊科學

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