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New shape clustering method based on contour DFT descriptor and modified SOFM neural network 被引量:1
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作者 刘威杨 徐向民 +1 位作者 梅剑寒 王为凯 《Journal of Beijing Institute of Technology》 EI CAS 2014年第1期89-95,共7页
A contour shape descriptor based on discrete Fourier transform (DFT) and a K-means al- gorithm modified self-organizing feature map (SOFM) neural network are established for shape clus- tering. The given shape is ... A contour shape descriptor based on discrete Fourier transform (DFT) and a K-means al- gorithm modified self-organizing feature map (SOFM) neural network are established for shape clus- tering. The given shape is first sampled uniformly in the polar coordinate. Then the discrete series is transformed to frequency domain and constructed to a shape characteristics vector. Firstly, sample set is roughly clustered using SOFM neural network to reduce the scale of samples. K-means algo- rithm is then applied to improve the performance of SOFM neural network and process the accurate clustering. K-means algorithm also increases the controllability of the clustering. The K-means algo- rithm modified SOFM neural network is used to cluster the shape characteristics vectors which is previously constructed. With leaf shapes as an example, the simulation results show that this method is effective to cluster the contour shapes. 展开更多
关键词 contour shape descriptor discrete Fourier transform (DFT) serf-organizing featuremap sofm neural network K-means algorithm
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