In this paper, we propose an efficient similarity search system PathSOM that combines Self-Organizing Map (SOM) and Pathfinder Networks (PFNET). In the front end of the system, SOM is applied to cluster the original data vectors and construct a visual map of the data. The Pathfinder network then organizes the SOM map units in the form of a graph to yield a framework for an improved search to find the best matching map unit. The ability of PathSOM approach for
efficient searches is demonstrated through well-known data sets.
The POCS method was original developed in 1960's. It is applied in many fields such as: image processing, signal recovery and optics. The POCS method allows us to incorporate into iteration scheme available information about the experimental data and the measurement error as well as priori constraints based on physical reasoning. It is important to note that the POCS-method doesn't lead to a unique `optimum' solution. The next step to projection is to find a optimal method within a `solution space'. Based on synergetic theory founded by Haken in 1970's, this optimal problem can be resolved by synergetic pattern recognition procedure. In our paper, we propose a synergetic pattern recognition approach to accomplish the optimal processing.
This paper presents a novel optical-electronic shape recognition system based on synergetic associative memory. Our shape recognition system is composed of two parts: the first one is feature extraction system; the second is synergetic pattern recognition system. Hough transform is proposed for feature extraction of unrecognized object, with the effects of reducing dimensions and filtering for object distortion and noise, synergetic neural network is proposed for realizing associative memory in order to eliminate spurious states. Then we adopt an approach of optical- electronic realization to our system that can satisfy the demands of real time, high speed and parallelism. In order to realize fast algorithm, we replace the dynamic evolution circuit with adjudge circuit according to the relationship between attention parameters and order parameters, then implement the recognition of some simple images and its validity is proved.
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