基于可能模型方法 ( possible m odel approach,简称 PMA) ,提出了面向行动的信念更新的概念 ,证明了在信息完备的情境演算系统中 ,一个一阶公式在情境 s下成立当且仅当它属于情境 s下的信念集 .这一结果为有效避免情境演算推理中二阶...
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基于可能模型方法 ( possible m odel approach,简称 PMA) ,提出了面向行动的信念更新的概念 ,证明了在信息完备的情境演算系统中 ,一个一阶公式在情境 s下成立当且仅当它属于情境 s下的信念集 .这一结果为有效避免情境演算推理中二阶归纳公理的使用提供了一条可行的途径 ,也为基于意向驱动的 agent模型的建立以及面向 agent的程序设计语言 AOPL ID( agent-oriented programm ing language with intention driver)的提出提供了必要的理论基础 .
A novel and fast shape classification and regularization algorithm for on-line sketchy graphics recognition is proposed. We divide the on-line graphics recognition process into four stages: preprocessing,shape classif...
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A novel and fast shape classification and regularization algorithm for on-line sketchy graphics recognition is proposed. We divide the on-line graphics recognition process into four stages: preprocessing,shape classification,shape fitting,and regularization. Attraction Force Model is employed to progressively combine the vertices on the input sketchy stroke and reduce the total number of vertices before the type of shape can be determined. After that ,the shape is fitted and gradually rectified to a regular one,thus the regularized shape fits the user intended one *** results show that this algorithm can yield good recognition precision(averagely above 90% )and fine regularization effect but with fast speed. Consequently,it is especially suitable to computational critical environment such as PDAs,which solely depends on a pen-based user interface.
Interpreting background is very important in natural scene images. This paper addresses the automaticclassification of background region by using visual semantic template. The method to create the templates is intro-d...
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Interpreting background is very important in natural scene images. This paper addresses the automaticclassification of background region by using visual semantic template. The method to create the templates is intro-duced first. Then we use these templates to classify background regions, and the results are analyzed. We also usethese templates to locate background objects in images, and to determine whether an image contains certain kind ofbackground. The result is promising in object locating. Some approaches to improve the ability of these templates arealso discussed.
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