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Commenced in January 2007 Frequency: Monthly Edition: International Publications Count: 30526

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Integrating Low and High Level Object Recognition Steps by Probabilistic Networks
In pattern recognition applications the low level segmentation and the high level object recognition are generally considered as two separate steps. The paper presents a method that bridges the gap between the low and the high level object recognition. It is based on a Bayesian network representation and network propagation algorithm. At the low level it uses hierarchical structure of quadratic spline wavelet image bases. The method is demonstrated for a simple circuit diagram component identification problem.
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