• Journal of Life Science
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• v.17 no.9 s.89
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• pp.1217-1223
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• 2007

I investigated the reproduction system of nine natural populations of P. discolor in Korea and two Mongolian P. conferta populations. The measurements of 19 quantitative or qualitative morphological characters were taken on each of total individuals directly from their natural habitats. Multivariate principal component analyses (PCA) were conducted to detect differences among populations consid-ering several characters simultaneously of variances using the statistical analysis system. 19 morpho-logical characteristics between Korean Potentilla species and Mongolian Potentilla species showed a slight heterogeneity of variance. The length of internodes (LFL and LSI) and characteristics of root (LLR and NOR) were shown a significant difference between two species (P<0.05). The number of ra-mets in P. conferta decreased with increasing geographic distance from viviparity. However, P. discolor has most ramets at distance intervals $60{\sim}80$ cm. In light conditions, P. discolor was significantly less resilience than P. conferta. In drought conditions, although there was not shown significant difference, P. conferta was less resilience than P. discolor. The core analysis indicates that P. conferta is the more resistant species than P. discolor and usually propagates by clonal growth during several strong envi-ronmental disadvantages such as drought events.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.28 no.8C
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• pp.795-802
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• 2003

It is very important to construct statistical model in order to exactly estimate the signal variance from a noisy image. In order to estimate variance, information of neighboring region is used generally. The size of neighbor region is varied according to the regional characteristics of image. More accurate estimation of edge variance is due to smaller region of neighbor, on the other hands, larger region of neighbor is used to estimate the variance of flat region. By using estimated variance of original image, in general, Wiener filter is constructed, and it is applied to the noisy image. In this paper, we propose a new method for determining the range of neighbors to estimate the variance in wavelet domain. Firstly, a significance map is constructed using the parent-child relationship of wavelet domain. Based on the number of the significant wavelet coefficients, the range of neighbors is determined and then the variance of the original signal is estimated using ML(maximum likelihood method. Experimental results show that the proposed method yields better results than conventional methods for image denoising.