This paper proposes a novel method of estimating 3-D hand posture from images observed in complex backgrounds. Conventional methods often cause mistakes by mis-matches of local image features. Our method considers possibility of the mis-match between each posture model appearance and the other model appearances in a Baysian stochastic estimation form by introducing a novel likelihood concept “Mistakenly Matching Likelihood (MML)“. The correct posture model is discriminated from mis-matches by MML-based posture candidate evaluation. The method is applied to hand tracking problem in complex backgrounds and its effectiveness is shown.
Hand Posture Estimation in Complex Backgrounds by Considering Mis-match of Model
Asian Conference on Computer Vision ; 2007 ; Tokyo, Japan November 18, 2007 - November 22, 2007
2007-01-01
12 pages
Article/Chapter (Book)
Electronic Resource
English
Input Image , Gesture Recognition , Edge Image , Complex Background , Posture Parameter Computer Science , Image Processing and Computer Vision , Computer Imaging, Vision, Pattern Recognition and Graphics , Pattern Recognition , Artificial Intelligence , Biometrics , Algorithm Analysis and Problem Complexity
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