Abstract—Two approaches, based on linear and conic splines, are proposed here for vectorizing image outlines. Both of the approaches have various phases including extracting outlines of images, detecting corner points from the detected outlines, and curve fitting. Interpolation splines are the bases of the two approached. Linear spline approach is straight forward as it does not have a degree of freedom.in terms of some shape controller in its description. However, the idea of the soft computing approach, namely simulated annealing, has been utilized for conic splines. This idea has been incorporated to optimize the shape parameters in the description of the generalized conic spline. Both of the linear and conic approaches ultimately produce optimal results for the approximate vectorization of the digital contours obtained from the generic shapes. Demonstrations and a comparative study also make the essential parts of the paper.
Index Terms—Vectorization, corner points, generic shapes, curve fitting.
M. Sarfraz is with the Department of Information Science, Kuwait University, Adailiya Campus, Kuwait (e-mail: muhamad.sarfraz@ku.edu.ke;prof.m.sarfraz@gmail.com).
Cite: Muhammad Sarfraz, "Two Approaches for Vectorizing Image Outlines," International Journal of Machine Learning and Computing vol. 2, no. 3, pp. 301-307, 2012.