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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