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. © Springer-Verlag Berlin Heidelberg 2007.
CITATION STYLE
Imai, A., Shimada, N., & Shirai, Y. (2007). Hand posture estimation in complex backgrounds by considering mis-match of model. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4843 LNCS, pp. 596–607). Springer Verlag. https://doi.org/10.1007/978-3-540-76386-4_56
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