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feature optimal
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  特征优化
     On-line Handwriting Signature Verification Technology Based on Feature Optimal Selection and Neural Network Classifier
     基于特征优化选取和神经网络分类的在线手写签名验证术
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  “feature optimal”译为未确定词的双语例句
     Aimed to the problem that original feature is mass and redundancy in pattern recognition, a method of feature optimal based on geneticand simulated annealing algorithm is proposed.
     针对模式识别时原始特征数量大而有冗余的现象,提出了一种基于遗传退火算法的特征选优方法。
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  feature optimal
These differentiators feature optimal asymptotics with respect to input noises and can be used for numerical differentiation as well.
      
Every stage of the operation will be monitored and will feature optimal manual interference.
      


For textured image,this paper suggests a hierarchical segmentation scheme based on wavelet transform performed on the sub band image with the most power.The scheme includes constructing a fine coarse multiresolution wavelet pyramid by energy and segmenting textured image by the inverse process of wavelet decomposition,i.e,the process from coarse to fine resolution.At every resolution,the feature optimal selection and using of Fuzzy C clustering are all helpful to segmentation.Finally,the segmentation information...

For textured image,this paper suggests a hierarchical segmentation scheme based on wavelet transform performed on the sub band image with the most power.The scheme includes constructing a fine coarse multiresolution wavelet pyramid by energy and segmenting textured image by the inverse process of wavelet decomposition,i.e,the process from coarse to fine resolution.At every resolution,the feature optimal selection and using of Fuzzy C clustering are all helpful to segmentation.Finally,the segmentation information at the low level is propagated to the next higher level of resolution.Thus,a fine segmentation result of textured image can be obtained.

本文针对纹理图像的特点,提出了基于主能量通道子波变换的纹理图像多级分割方法。该方法以最大能量为线索,建立起由细到粗的多分辨率子波分解金字塔,分割按照其逆过程,即由粗到细,每个分辨率上分割特征的优化选择、分割方案—模糊C聚类的确定,都有利于纹理图像的分割,最后由低向高传递分割结果,从而得到较精细的分割图像。

Aim Multi-resolution segmentation using wavelet transform is studied. Meth- ods A hierarchical segmentation based on the subband image with the most energy in wavelet domain is suggested.The scheme includes the construction of a fine to coarse multi-resolution wavelet pyramid by energy, and then the segmentation of textural image by the inverse process of wavelet decomposition, i. e., the process from coarse to fine resolution. The segmentation information at a lower level is propagated to the next higher level....

Aim Multi-resolution segmentation using wavelet transform is studied. Meth- ods A hierarchical segmentation based on the subband image with the most energy in wavelet domain is suggested.The scheme includes the construction of a fine to coarse multi-resolution wavelet pyramid by energy, and then the segmentation of textural image by the inverse process of wavelet decomposition, i. e., the process from coarse to fine resolution. The segmentation information at a lower level is propagated to the next higher level. Results Feature optimal selection and the use of Fuz- zy-C clustering are helpful to segmentation at every resolution. The result gets finer with the levels. Conclusion There are almost no texturel grids at the higher levels, so the segmentation accords well with human vision.

目的基于子波变换研究纹理图像的多分辨率分割.方法提出了子波域主能量通道的纹理图像多级分割方法.以最大能量为线索,建立起由细到粗的子波分解金字塔,分割按照其逆过程,即由粗到细,将低分辨率上的分割结果传递到高分辨率上.结果在每个分辨率上优化选择分割特征,用模糊C聚类法确定分割方案,有利于纹理图像的分割,分割结果逐级精细.结论员分辨率上基本无纹理栅格,分割效果符合人眼视觉特性.

Aimed to the problem that original feature is mass and redundancy in pattern recognition, a method of feature optimal based on geneticand simulated annealing algorithm is proposed. This paper firstly describes the genetic algorithm and simulated annealing algorithm, then itintroduces the Boltzmann upgrade mechanism into the traditional genetic algorithm to solve the problem of premature convergence of the processand local minima. Finally, it tells and designs a fitness function and genetic operator. Simulations...

Aimed to the problem that original feature is mass and redundancy in pattern recognition, a method of feature optimal based on geneticand simulated annealing algorithm is proposed. This paper firstly describes the genetic algorithm and simulated annealing algorithm, then itintroduces the Boltzmann upgrade mechanism into the traditional genetic algorithm to solve the problem of premature convergence of the processand local minima. Finally, it tells and designs a fitness function and genetic operator. Simulations results show that this method has goodperformance in both the quality of obtained feature subset and efficiency.

针对模式识别时原始特征数量大而有冗余的现象,提出了一种基于遗传退火算法的特征选优方法。首先对遗传算法和模拟退火做了简要评论,然后在遗传算法中引入模拟退火的Boltzmann更新机制,以克服传统的遗传算法易于过早收敛和易于陷入局部极小的问题。最后阐述、设计了适应度函数和遗传算子。仿真实验表明,该方法在求解的效率和解的质量方面都达到了令人满意的效果。

 
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