Intelligent Approaches for Vectorizing Image Outlines

Abstract

Two computing approaches, based on linear and conic splines, are proposed here in reviewed and extended 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

Data fitting by G1 rational cubic Bézier curves using harmony search

“…To support this claim, a comparison with recent alternative curve fitting based on soft computing techniques has been carried out. There were a few of soft computing method used in this data fitting problem, such as curve fitting by Bsplines using GA by Ga´lvez et al [4], Sarfraz [19] used cubic spline by SA and Yahya [15][16] proposed an approach of curve fitting by PSO. Here a comparison between HS, GA and PSO that used on the similar data points.…”

“…Meanwhile, a few researchers had used metaheuristic method recently to curve fit outline images or a set of data points such as Sarfraz [18] that used simulated annealing to curve fit extracting outlines of images with a generalized cubic spline, the simulated annealing is used to optimize the shape parameter and another paper [19] also used simulated annealing as the mechanism to globally optimizes the shape parameters in the description of the conic splines but in the case of poor approximation, the insertions of intermediate points are made as long as the desired approximation is achieved. Yahya [16] proposed particle swarm optimization to optimize the control points and weight which were then used in conic equations.…”