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Multi-scale parts-based models are particularly effective for recognizing non-segmented graphic symbols, i.e. the symbols interfered by other connecting or intersecting objects in the context. However, treating every symbol part and it features on every scale equally, despite some of them contributing little to the recognition, may lead to unnecessarily high dimensional representation of the symbol...
We present a multi resolution scheme for symbol representation and recognition based on statistical shape features. We define a symbol as a set of shape points, each of which is then described by a pyramid of shape context features. The pyramid is constructed by successively partitioning the image surrounding one shape point into increasingly finer sub-regions and computing the local shape context...
In this paper, we present an approach for symbol representation and recognition in line drawings, integrating both the vector-based structural description and pixel-level statistical features of the symbol. For the former, a vectorial template is defined on the basis of the vectorization model and exploited in segmenting symbols from the line network. For the latter, a Radon-transform-based signature...
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