In this paper, an accurate point matching scheme based on Combined Moment Invariants (CMIs) and their new metric is presented. In general, the matching of the local similarity detection by the combined invariants and conventional distances produces some outliers, which should be deleted firstly through some complex statistics. In order to obtain the more reliable matching results, we construct a new metric for combined NMIs. The whole framework involves two steps: 1) Extraction of Control points (CPs) on the reference image ---the canny edge detector and well-known Harris detector are described to extract the edges and corner points. 2) Searching for the corresponding CPs in a circular of the matched image---is based on local similarity metric with combined NMIs. The framework is fully automatic and simple without any additional steps. It has been successfully applied to register remote sensing images. Experimental results show that the proposed scheme excludes the outliers successfully for their high matching accuracy.
An adaptive weighted data fusion method in multi-sensor system is described. No prior distribution information or historical data is needed, evidence theory and fault-tolerant model are applied to judge the data's quality(precision) , and which is used to determine the weight of each sensor. From the result of numerical examples a conclusion can be drawn that the proposed method performs well on estimate precision and robustness.
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