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We introduce a flexible technique for interactive exploration of vector field data through classification derived from userspecified feature templates. Our method is founded on the observation that, while similar features within the vector field may bespatially disparate, they share similar neighborhood characteristics. Users generate feature-based visualizations by interactivelyhighlighting well-accepted and domain specific representative feature points. Feature exploration begins with the computation ofattributes that describe the neighborhood of each sample within the input vector field. Compilation of these attributes forms a representation of the vector field samples in the attribute space. We project the attribute points onto the canonical 2D plane to enableinteractive exploration of the vector field using a painting interface. The projection encodes the similarities between vector field pointswithin the distances computed between their associated attribute points. The proposed method is performed at interactive rates forenhanced user experience and is completely flexible as showcased by the simultaneous identification of diverse feature types.
vector field, data clustering, feature classification, high-dimensional data, user interaction

L. G. Nonato, C. T. Silva, J. Daniels II and E. W. Anderson, "Interactive Vector Field Feature Identification," in IEEE Transactions on Visualization & Computer Graphics, vol. 16, no. , pp. 1560-1568, 2010.
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