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现有的光照正则化处理算法,都是在空间域中进行的,为避免海量图像解压缩的时间消耗,在JPEG图像上直接进行光照正则化处理,提高人脸识别效率,在DCT域上,基于三维辐照度方程,把差图像法推广到了DCT域上,并在DCT域上提出了分量图像法。实验表明:差图像法与分量图像法均能在DCT域中有效地削弱光照方向对人脸识别的负面影响。 相似文献
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Efficiency frontier analysis has been an important approach of evaluating firms’ performance in private and public sectors. There have been many efficiency frontier analysis methods reported in the literature. However, the assumptions made for each of these methods are restrictive. Each of these methodologies has its strength as well as major limitations. This study proposes two non-parametric efficiency frontier analysis sub-algorithms based on (1) Artificial Neural Network (ANN) technique and (2) ANN and Fuzzy C-Means for measuring efficiency as a complementary tool for the common techniques of the efficiency studies in the previous studies. Normal probability plot is used to find the outliers and select from these two methods. The proposed computational algorithms are able to find a stochastic frontier based on a set of input–output observational data and do not require explicit assumptions about the functional structure of the stochastic frontier. In these algorithms, for calculating the efficiency scores, a similar approach to econometric methods has been used. Moreover, the effect of the return to scale of decision-making unit (DMU) on its efficiency is included and the unit used for the correction is selected by notice of its scale (under constant return to scale assumption). Also in the second algorithm, for increasing DMUs’ homogeneousness, Fuzzy C-Means method is used to cluster DMUs. Two examples using real data are presented for illustrative purposes. First example which deals with power generation sector shows the superiority of Algorithm 2 while the second example dealing auto industries of various developed countries shows the superiority of Algorithm 1. Overall, we find that the proposed integrated algorithm based on ANN, Fuzzy C-Means and Normalization approach provides more robust results and identifies more efficient units than the conventional methods since better performance patterns are explored. 相似文献
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Cheng-Lin Liu Author Vitae Kazuki Nakashima Author VitaeAuthor Vitae Hiromichi Fujisawa Author Vitae 《Pattern recognition》2004,37(2):265-279
The performance evaluation of various techniques is important to select the correct options in developing character recognition systems. In our previous works, we have proposed aspect ratio adaptive normalization (ARAN) and have evaluated the performance of state-of-the-art feature extraction and classification techniques. For this time, we will propose some improved normalization functions and direction feature extraction strategies and will compare their performance with existing techniques. We compare ten normalization functions (seven based on dimensions and three based on moments) and eight feature vectors on three distinct data sources. The normalization functions and feature vectors are combined to produce eighty classification accuracies to each dataset. The comparison of normalization functions shows that moment-based functions outperform the dimension-based ones and the aspect ratio mapping is influential. The comparison of feature vectors shows that the improved feature extraction strategies outperform their baseline counterparts. The gradient feature from gray-scale image mostly yields the best performance and the improved NCFE (normalization-cooperated feature extraction) features also perform well. The combined effects of normalization, feature extraction, and classification have yielded very high accuracies on well-known datasets. 相似文献
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谢玉琼 《湖北工业大学学报》1995,10(2):85-89
本文从Fedholm算子的正规化子中,挑选出一个具有许多优美性质的算子,称之为典则正规化子,证明了典则正规则化子的存在性和唯一性,阐明了它具有塑 算子主要特征,因而可以看作是一种广义逆算子,还研究了与之相联系的约束算子和归一化算子,并讨论了它们的若干用途。 相似文献
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