HMM-based Intent Recognition System using 3D Image Reconstruction Data

Abstract

The mirror neuron system in the cerebrum, which are handled by visual information-based imitative learning. When we observe the observer's range of mirror neuron system, we can assume intention of performance through progress of neural activation as specific range, in include of partially hidden range. It is goal of our paper that imitative learning is applied to 3D vision-based intelligent system. We have experiment as stereo camera-based restoration about acquired 3D image our previous research Using Optical flow, unscented Kalman filter. At this point, 3D input image is sequential continuous image as including of partially hidden range. We used Hidden Markov Model to perform the intention recognition about performance as result of restoration-based hidden range. The dynamic inference function about sequential input data have compatible properties such as hand gesture recognition include of hidden range. In this paper, for proposed intention recognition, we already had a simulation about object outline and feature extraction in the previous research, we generated temporal continuous feature vector about feature extraction and when we apply to Hidden Markov Model, make a result of simulation about hand gesture classification according to intention pattern. We got the result of hand gesture classification as value of posterior probability, and proved the accuracy outstandingness through the result.

Robust Video-Based Barcode Recognition via Online Sequential Filtering

“…We consider a temporal model for a sequence of barcode image frames, similar to the popular hidden Markov model (HMM). The HMMs were previously applied to visual pattern recognition problems [6][7][8][9][10]. In our model, we introduce hidden state variables that encode the blur/noise levels of the corresponding frames, and impose smooth dynamics over those hidden states.…”