Image-mapped data clustering: An efficient technique for clustering large data sets
Intelligent Data Analysis
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The curvature scale space (CSS) technique has been used in conjunction with Hermite curves for compression and robust reconstruction of 2-D contours at multiple scales. CSS is a powerful contour shape descriptor which is expected to be an MPEG-7 standard. A parametric representation of the input contour is convolved with Gaussian functions in order to obtain multi-scale descriptions of the contour. Curvature can be computed directly at each point of the smoothed contours. As a result, a set of curvature zero-crossing points can be extracted from each smoothed contour. Hermite curves were used since each Hermite curve is defined by two endpoints and the tangent vectors at those points. No points external to the input contour are required for Hermite curves. Hermite endpoints are defined as consecutive curvature zero-crossing points extracted at multiple scales using the CSS method. Hermite tangent vectors can also be determined using the CSS technique. The only data stored are the endpoints and the tangent vectors needed by the Hermite curves in order to arrive at an approximate reconstruction of the original contour.