Soil particle size distribution(PSD) is a fundamental physical property affecting other soil properties. Characterizing spatial variability of soil texture is very important in environmental research. The objectives o...Soil particle size distribution(PSD) is a fundamental physical property affecting other soil properties. Characterizing spatial variability of soil texture is very important in environmental research. The objectives of this work were: 1) to partition PSD of 75 soil samples, collected from a flat field in the University of Guilan, Iran, into two scaling domains using a piecewise fractal model to evaluate the relationships between fractal dimensions of scaling domains and soil clay, silt, and sand fractions and 2) to assess the potential of fractal parameters as an index used in a geostatistical approach reflecting the spatial variability of soil texture. Features of PSD of soil samples were studied using fractal geometry, and geostatistical techniques were used to characterize the spatial variability of fractal and soil textural parameters. There were two scaling domains for the PSD of soil samples. The fractal dimensions of these two scaling domains(D_1 and D_2) were then used to characterize different ranges of soil particle sizes and their relationships to the soil textural parameters. There was a positive correlation between D_1 and clay content(R^2= 0.924), a negative correlation between D_1 and silt content(R^2= 0.801), and a negative correlation between D_2 and sand content(R^2= 0.913). The geometric mean diameter of soil particles had a negative correlation with D_1(R^2= 0.569) and D_2(R^2= 0.682). Semivariograms of fractal dimensions and soil textural parameters were calculated and the maps of spatial variation of D_1 and D_2 and soil PSD parameters were provided using ordinary kriging. The results showed that there were also spatial correlations between D_1 and D_2 and particle size fractions.According to the semivariogram models and validation parameters, the fractal parameters had powerful spatial structure and could better describe the spatial variability of soil texture.展开更多
Image texture feature extraction is a classical means for biometric recognition. To extract effective texture feature for matching, we utilize local fractal auto-correlation to construct an effective image texture des...Image texture feature extraction is a classical means for biometric recognition. To extract effective texture feature for matching, we utilize local fractal auto-correlation to construct an effective image texture descriptor. Three main steps are involved in the proposed scheme： （i） using two-dimensional Gabor filter to extract the texture features of biometric images; （ii） calculating the local fractal dimension of Gabor feature under different orientations and scales using fractal auto-correlation algorithm; and （iii） linking the local fractal dimension of Gabor feature under different orientations and scales into a big vector for matching. Experiments and analyses show our proposed scheme is an efficient biometric feature extraction approach.展开更多
文摘Soil particle size distribution(PSD) is a fundamental physical property affecting other soil properties. Characterizing spatial variability of soil texture is very important in environmental research. The objectives of this work were: 1) to partition PSD of 75 soil samples, collected from a flat field in the University of Guilan, Iran, into two scaling domains using a piecewise fractal model to evaluate the relationships between fractal dimensions of scaling domains and soil clay, silt, and sand fractions and 2) to assess the potential of fractal parameters as an index used in a geostatistical approach reflecting the spatial variability of soil texture. Features of PSD of soil samples were studied using fractal geometry, and geostatistical techniques were used to characterize the spatial variability of fractal and soil textural parameters. There were two scaling domains for the PSD of soil samples. The fractal dimensions of these two scaling domains(D_1 and D_2) were then used to characterize different ranges of soil particle sizes and their relationships to the soil textural parameters. There was a positive correlation between D_1 and clay content(R^2= 0.924), a negative correlation between D_1 and silt content(R^2= 0.801), and a negative correlation between D_2 and sand content(R^2= 0.913). The geometric mean diameter of soil particles had a negative correlation with D_1(R^2= 0.569) and D_2(R^2= 0.682). Semivariograms of fractal dimensions and soil textural parameters were calculated and the maps of spatial variation of D_1 and D_2 and soil PSD parameters were provided using ordinary kriging. The results showed that there were also spatial correlations between D_1 and D_2 and particle size fractions.According to the semivariogram models and validation parameters, the fractal parameters had powerful spatial structure and could better describe the spatial variability of soil texture.
基金supported by the National Natural Science Foundation of China(Grant Nos.61262040,61271341,81360230,and 61271007)the Applied Basic Research Projects of Yunnan Province,China(Grant No.KKSY201203062)
文摘Image texture feature extraction is a classical means for biometric recognition. To extract effective texture feature for matching, we utilize local fractal auto-correlation to construct an effective image texture descriptor. Three main steps are involved in the proposed scheme： （i） using two-dimensional Gabor filter to extract the texture features of biometric images; （ii） calculating the local fractal dimension of Gabor feature under different orientations and scales using fractal auto-correlation algorithm; and （iii） linking the local fractal dimension of Gabor feature under different orientations and scales into a big vector for matching. Experiments and analyses show our proposed scheme is an efficient biometric feature extraction approach.
