An analysis based on Elston's model of mixed major locus and polygenic inheritance is extended to include populations of progeny testing such as F3, B1s and B2s families derived from F2 and backcrosses in a cross between two inbred lines. Genetic hypotheses that can be validly tested by the likelihood ratio method in the analysis of a breeding experiment include homogeneity of variances due to environment and/or polygenes with transformable scale effect by Box-Cox power function, random and independent segregation of major genes, invariance of the effects of major genes with population types and additive and dominant models for polygenes. Testing hypotheses in the order suggested here can lead to a gradual simplification of the models and increases the feasibility of the subsequent analysis, but caution must be paid to the possible bias in parameter estimation and hypotheses tests. The procedure is applied to a set of data on plant height of rice with the effects of dwarf genes in crosses among three varieties. Two recessive dwarf genes are shown to be nonallelic and unlinked. One dwarf gene is shown to reduce plant height about 36-56 cm, and another 52-61 cm. The effect of polygenes, estimated as the standard deviation among possible inbred lines derived from these crosses, is about 11.7 cm. Interactions between the dwarf genes and the polygenic background are found, especially for one of the two genes. Both the polygenic effects and the interactions are much smaller than the effects of the major dwarf genes.
A novel methodology base on object-oriented MRF is proposed in order to obtain precise segmentation of high resolution satellite image. Conventional pixel-by-pixel MRF model methods only consider spatial correlation and texture of each pixel fixed square neighborhood. The segmentation method based on pixel-by-pixel MRF model usually suffers from salt and pepper noise. Based on the analysis of problems existing in pixel-by pixel MRF model methods of highresolution remote sensed images, an object-oriented MRF-based segmentation algorithm is proposed. The proposed method is made up of two blocks: (1) Mean-Shift algorithm is employed to obtain the over-segmentation results and the primary processing units are generated based on which the object adjacent graph (OAG) can be constructed.(2) MRF model is easily defined on the OAG, in which special features of pixels are modeled in the feature field model and the neighbor system, potential cliques and energy functions of OAG are exploited in the labeling model. The proposed segmentation method is evaluated on high resolution remote sensed image data-IKONOS. The experimental results show the proposed method can improve the segmentation accuracy while simultaneously obviating "salt and pepper noise" phenomenon and reducing the computational complexity greatly.
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