An object recognition system using stochastic knowledge source and VLSI parallel architecture

The authors present a system for 2D shape recognition using hidden Markov model (HMM) knowledge sources. The shape is represented by a sequence of curvature values. A ring hidden Markov model (RHMM), which incorporates a ring structure and local connectivity, is proposed. The approach solves both the context sensitivity problem and the pattern instantiation problem. Simulation results on aircraft indicate that the proposed system can achieve almost 100% recognition accuracy at a very fast learning speed. It is shown that the RHMM system can be efficiently implemented in a systolic array, permitting real-time processing.<<ETX>>

[1]  Biing-Hwang Juang,et al.  Mixture autoregressive hidden Markov models for speech signals , 1985, IEEE Trans. Acoust. Speech Signal Process..

[2]  Jenq-Neng Hwang,et al.  A systolic neural network architecture for hidden Markov models , 1989, IEEE Trans. Acoust. Speech Signal Process..

[3]  S. Y. Kung,et al.  A hierarchical system for character recognition with stochastic knowledge representation , 1988, IEEE 1988 International Conference on Neural Networks.

[4]  X. Gong,et al.  Textured image recognition using hidden Markov model , 1988, ICASSP-88., International Conference on Acoustics, Speech, and Signal Processing.

[5]  L. Rabiner,et al.  An introduction to hidden Markov models , 1986, IEEE ASSP Magazine.

[6]  Michael Brady,et al.  The Curvature Primal Sketch , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.