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ICASSP
2009
IEEE

Graphical Models: Statistical inference vs. determination

10 years 1 months ago
Graphical Models: Statistical inference vs. determination
Using discrete Hidden-Markov-Models (HMMs) for recognition requires the quantization of the continuous feature vectors. In handwritten whiteboard note recognition it turns out that the pen-pressure information, which is important for recognition, is not adequately quantized and looses significance. In this paper, the implicit modeling of the pressure information presented in previous work which uses the deterministic knowledge on the actual pressure is generalized using a Graphical Model (GM) representation based on statistical inference. The results of two state-of-the-art toolboxes implementing HMMs and GMs are compared. It can be seen that the statistical inference approach based on GMs is inferior to the implicit modeling of the pressure information. It is shown that a direct implementation of HMMs outperforms the mathematic identical GM representation.
Joachim Schenk, Benedikt Hörnler, Artur Braun
Added 17 Aug 2010
Updated 17 Aug 2010
Type Conference
Year 2009
Where ICASSP
Authors Joachim Schenk, Benedikt Hörnler, Artur Braun, Gerhard Rigoll
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