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The features of network agricultural information (NAI) are summarized by analyzing Chinese typical, famous agricultural information websites. The features include the storage-scattered of agricultural informations, high frequency of updating, non-uniform data format and a lots of raw information existing. It is pointed out that the features have disadvantages for sharing and exploiting the NAI efficiently. Then a scheme is proposed to try to overcome the disadvantages of the features, which is mainly based on brower/server structure and information extraction technology from webpages. Also the key technologies to implement the scheme are described in the paper. In the end, an example application of the scheme is carried out to demonstrate the concrete steps to develop practical applications based on the scheme. [ABSTRACT FROM AUTHOR]

3-D reconstruction from images sequences has been the center topic of computer vision. Real-time applications call for causal processing of stereo sequences, as they are acquired, covering different regions of the scene. The first step is to compute the current stereo disparity, and recursive map building often requires fusing with the previous estimate. In this paper, the epiflow framework [1], originally proposed for establishing matches among stereo feature pairs is generalized to devise an iterative causal algorithm for stereo disparity map fusion. In the context of disparity fusion, quadruplet correspondence of the epiflow tracking algorithm becomes reminiscent of the "closest point" of the 3-D ICP algorithm. Unlike ICP, the 2-D epiflow framework permits incorporating both photometric and geometrical constraints, estimation of the stereo rig motion as supplementary information, as well as identifying local inconsistencies between the two disparity maps. Experiments with real data validate the proposed approach, and improved converge compared to the ICP algorithm. [ABSTRACT FROM AUTHOR]