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A modified EM algorithm for hand gesture segmentation in RGB-D data

Research output: Chapter in Book/Report/Conference proceedingConference contribution

This paper proposes a novel method with a modified Expectation-Maximisation (EM) Algorithm to segment hand gestures in the RGB-D data captured by Kinect. With the depth map and RGB image aligned by the genetic algorithm to estimate the key points from both depth and RGB images, a novel approach is proposed to refine the edge of the tracked hand gesture, which is used to segment the RGB image of the hand gestures, by applying a modified EM algorithm based on Bayesian networks. The experimental results demonstrated the modified EM algorithm effectively adjusts the RGB edges of the segmented hand gestures. The proposed methods have potential to improve the performance of hand gesture recognition in Human-Computer Interaction (HCI).
Original languageEnglish
Title of host publicationProceedings of the 2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)
ISBN (Electronic)978-1-4799-2072-3
Publication statusPublished - 8 Sep 2014
Event2014 IEEE International Conference on Fuzzy Systems - Beijing, China
Duration: 6 Jul 201411 Jul 2014

Publication series

ISSN (Print)1098-7584


Conference2014 IEEE International Conference on Fuzzy Systems
Abbreviated titleFUZZ-IEEE 2014


  • A Modified EM Algorithm for Hand Gesture Segmentation in

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    Accepted author manuscript (Post-print), 2.91 MB, PDF document

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