A new kind of region pair grey difference classifier was proposed. The regions in pairs associated to form a feature were not necessarily directly-connected, but were selected dedicatedly to the grey transition betwee...A new kind of region pair grey difference classifier was proposed. The regions in pairs associated to form a feature were not necessarily directly-connected, but were selected dedicatedly to the grey transition between regions coinciding with the face pattern structure. Fifteen brighter and darker region pairs were chosen to form the region pair grey difference features with high discriminant capabilities. Instead of using both false acceptance rate and false rejection rate, the mutual information was used as a unified metric for evaluating the classifying performance. The parameters of specified positions, areas and grey difference bias for each single region pair feature were selected by an optimization processing aiming at maximizing the mutual information between the region pair feature and classifying distribution, respectively. An additional region-based feature depicting the correlation between global region grey intensity patterns was also proposed. Compared with the result of Viola-like approach using over 2 000 features, the proposed approach can achieve similar error rates with only 16 features and 1/6 implementation time on controlled illumination images.展开更多
This study examines gender differences in rural laborers‘ employment patterns in the mountainous and upland areas of Sichuan, China. The analysis employs both representative survey data of 400 households and geograph...This study examines gender differences in rural laborers‘ employment patterns in the mountainous and upland areas of Sichuan, China. The analysis employs both representative survey data of 400 households and geographical data. Multinomial logistic regression models are used to analyze the influences of gender, among other factors, on employment decisions of rural males and females, where the factors considered include personal, household, community natural environmental characteristics. Dividing laborers into four categories, we find that the proportions of males who participating in on-farm, pluriactive, and off-farm work, and unemployment were 24.41%, 28.64%, 46.27%, and 0.68% respectively, while that of females who participating in on-farm, pluriactive, and offfarm work, and unemployment were 43.20%, 13.95%, 30.95%, and 11.90% respectively. As to individual characteristics, age(AGE) and physical condition(PHY) effects appeared to be more pronounced for men, while education(EDU) and marital status(MAR)effects appeared to be more pronounced for women. Regarding household and community characteristics, the effects of the presence in the household of children aged 6-(CHI), number of persons in the household(POP), and labor force(LAB), per capitaincome in village(INCV), and the development status of village enterprises(ENT) on women were greater than that on men. In addition, the presence in the household of elderly individuals aged 65+(OLD) and time to reach the nearest township(TIME) are shown to have opposite impacts on men and women. While the presence in the household of pupils(PUP) and per capital gross value of industrial output(GVIO) was found to be irresponsive to men and women taking pluriavtivity and off-farm job. With respect to natural environments characteristics, the effects on men were opposite of those on women. Unemployment of women was found to be particularly responsive to household characteristics. A multinomial regression approach is undertaken to analyze rural males‘ and females‘ decisions of the four employment patterns considered, an approach that reveals considerable heterogeneity that is concealed by the dichotomous approach employed in most previous studies. The study thus contributes to our understanding of rural employment patterns and gender difference in mountainous and upland areas.展开更多
基金Supported by the Joint Research Funds of Dalian University of Technology and Shenyang Automation Institute,Chinese Academy of Sciences
文摘A new kind of region pair grey difference classifier was proposed. The regions in pairs associated to form a feature were not necessarily directly-connected, but were selected dedicatedly to the grey transition between regions coinciding with the face pattern structure. Fifteen brighter and darker region pairs were chosen to form the region pair grey difference features with high discriminant capabilities. Instead of using both false acceptance rate and false rejection rate, the mutual information was used as a unified metric for evaluating the classifying performance. The parameters of specified positions, areas and grey difference bias for each single region pair feature were selected by an optimization processing aiming at maximizing the mutual information between the region pair feature and classifying distribution, respectively. An additional region-based feature depicting the correlation between global region grey intensity patterns was also proposed. Compared with the result of Viola-like approach using over 2 000 features, the proposed approach can achieve similar error rates with only 16 features and 1/6 implementation time on controlled illumination images.
基金supported and funded by the Chinese Academy of Sciences Important Directional Knowledge Innovation Project (Grant No.KZCX2-EW-317)the National Natural Science Foundation of China (Grant No.41101552)the Chinese Academy of Sciences Western Light Project (2013 Yuhui)
文摘This study examines gender differences in rural laborers‘ employment patterns in the mountainous and upland areas of Sichuan, China. The analysis employs both representative survey data of 400 households and geographical data. Multinomial logistic regression models are used to analyze the influences of gender, among other factors, on employment decisions of rural males and females, where the factors considered include personal, household, community natural environmental characteristics. Dividing laborers into four categories, we find that the proportions of males who participating in on-farm, pluriactive, and off-farm work, and unemployment were 24.41%, 28.64%, 46.27%, and 0.68% respectively, while that of females who participating in on-farm, pluriactive, and offfarm work, and unemployment were 43.20%, 13.95%, 30.95%, and 11.90% respectively. As to individual characteristics, age(AGE) and physical condition(PHY) effects appeared to be more pronounced for men, while education(EDU) and marital status(MAR)effects appeared to be more pronounced for women. Regarding household and community characteristics, the effects of the presence in the household of children aged 6-(CHI), number of persons in the household(POP), and labor force(LAB), per capitaincome in village(INCV), and the development status of village enterprises(ENT) on women were greater than that on men. In addition, the presence in the household of elderly individuals aged 65+(OLD) and time to reach the nearest township(TIME) are shown to have opposite impacts on men and women. While the presence in the household of pupils(PUP) and per capital gross value of industrial output(GVIO) was found to be irresponsive to men and women taking pluriavtivity and off-farm job. With respect to natural environments characteristics, the effects on men were opposite of those on women. Unemployment of women was found to be particularly responsive to household characteristics. A multinomial regression approach is undertaken to analyze rural males‘ and females‘ decisions of the four employment patterns considered, an approach that reveals considerable heterogeneity that is concealed by the dichotomous approach employed in most previous studies. The study thus contributes to our understanding of rural employment patterns and gender difference in mountainous and upland areas.
