About 200 skeletonization algorithms have been developed since 1950. It shows that there is a large variety of the problem which arises from the combination of the criteria used whether in its accuracy (size and shape) or speed. In this paper, a new idea to enrich skeletonization problem by reversing the approach is proposed. This new idea has an advantage in the algorithm speed. The proposed approach collects points which are assumed as the center point in vertical and horizontal set which every vertical point is connected to horizontal set through projection. The result of the skeleton extraction has single-pixel width which represents starting shape and is conducted in high speed process.
In most government and business organizations alike, statistical data provides the foundation for strategic planning and for the management of operations. In this context, the use of increasingly abundant statistical data available on the web creates new opportunities for interesting applications and facilitates more informed decision-making. For the majority of end users, however, viable means to explore statistical data sets available on the web are still scarce. Gathering and relating statistical data from multiple sources is hence typically a tedious manual process that requires significant technical expertise. Data that is being published with associated semantics, using standards such as the W3C RDF Data Cube Vocabulary, lays the foundation to overcome such limitations. In this paper, we develop a semantic metadata repository that describes each statistical data set and develop mechanisms for the interconnection of data sets based on their metadata. Finally, we support users in exploring data sets through interactive mashups that facilitate data integration, comparisons, and visualization.
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