基于粒子群算法的B样条曲线拟合

Curve Fitting of B-spline Based on Particle Swarm Optimization

图像边沿的曲线拟合对于目标对象的识别是十分重要的预处理步骤。针对目标边沿含有比较复杂的噪声的图像提出了一种基于多目标粒子群优化的算法,实现了曲线的快速平滑拟合。该算法利用建立辅助存储空间和保持解多样性的策略防止粒子群算法收敛过早;在边沿离散化采样时用分治与递归的搜索策略提高了B样条基函数节点参数选取的灵活度,从而实现了目标区域边沿的多分辨率插值拟合。实验证明该算法能够在较快实现曲线拟合的同时将目标区域边沿噪声去除,并能较好地实现图像三维重建预处理的需求。

Curve fitting plays very important role in preprocess of object recognizing. A particle swarm optimization （PSO） based multi-object optimization algorithm was proposed in this paper to implement the sronothness fitting quickly for image with complicated noise around the target-area. The external repository and strategy of diversity were employed to prevent the PSO from converging too quickly. Moreover, the search policy of split-and-merge made the selection of knots parameter more flexibly in B-spline bases computation while getting the discrete control points set of the target area. Therefore, curve fitting can be achieved by the multi-resolution interpolation. As shown in experiments, this algorithm can get the approximation curve quickly, eliminate the noise from the target-area, and satisfy the requirement of image based 3-D reconstruction as well.