Stratified gesture recognition using the normalized longest common subsequence with rough sets

作者：

Highlights：

• An integration of rough set theory to the longest common subsequence to classify dynamic hand gestures is discussed.

• Gesture vocabulary analysis using rough sets reveals the decision attributes that will correctly classify ambiguous gestures.

• A segment matching that reduces considerably the size of LCS matrices to be computed is presented.

摘要

Highlights•An integration of rough set theory to the longest common subsequence to classify dynamic hand gestures is discussed.•Gesture vocabulary analysis using rough sets reveals the decision attributes that will correctly classify ambiguous gestures.•A segment matching that reduces considerably the size of LCS matrices to be computed is presented.

论文关键词：Gesture vocabulary,Rough set theory,Gesture recognition,Segment based longest common subsequence,Dynamic hand gesture recognition

论文评审过程：Received 14 April 2014, Revised 21 October 2014, Accepted 23 October 2014, Available online 4 November 2014.

论文官网地址：https://doi.org/10.1016/j.image.2014.10.008