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Distributed Hierarchical Graph Neuron (DHGN) is a single-cycle learning distributed pattern recognition algorithm, which reduces the computational complexity of existing pattern recognition algorithms by distributing the recognition process into smaller clusters. This paper investigates an effect of dividing and distributing simple pattern recognition processes within a computational network. Our...
In this paper, a new classifier combination method is proposed for two-class problems. The boundaries of the classes are extracted directly from the given training set, and a set of linear combination rules are defined based on each sample on the class boundaries. The new approach is tested on two large public datasets, and the experimental results show its good performances. Comparing with combination...
Generalized singular value decomposition (GSVD) has been used for linear discriminant analysis (LDA) to solve the small sample size problem in pattern recognition. However, this algorithm may suffer from the over-fitting problem. In this paper, we propose a novel orthogonalization technique for the LDA/GSVD algorithm to address the over-fitting problem. In this technique, an orthogonalization of the...
Pattern recognition applications such as natural phenomena detection and structural health monitoring have been widely applied using wireless sensor networks. These applications involve large amount of data to be analysed, and thus incur high computational time and complexity. In this paper, we present a parallel associative memory-based pattern recognition algorithm known as distributed hierarchical...
Image registration is an important task in the field of computer vision and pattern recognition. And the applied values is also reflected in the study of remote sensing, medical imaging and the object indentifying of multi-sensor fusion. In this paper, a new subpixel registration methods which based on wavelet analysis was proposed by improving polynomial subdivision algorithm and pixel level registration...
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