为了更好地进行图像轮廓提取，对基于解析形式的二维参数可变形模板匹配方法中的模板结构、形变方式、离散化方案、内外部能量函数及优化算法等方面进行了研究与改进，并以生物体为原型，提出了一种具有3种生物组织结构的、形变方式可通过模板结构加以明确控制的、新的参数可变形模板匹配算法，该新算法是利用Gaussian函数来扩展外部力的作用域，并采用贪心优化快速算法进行能量函数优化。实验结果表明，这一新的模板匹配算法具有良好的轮廓提取速度、提取精度及稳定性。

Deformable template matching is a kind of useful method for contour extraction and image segmentation in the fields of computer vision and image analysis. In this paper, by utilizing the gradient trajectories of a two dimensional function, a novel analytical form based two dimensional parametric deformable template with three kinds of biological tissue bone, muscle and skin is built, which has more reasonable template structure used to control the template deformation. In order to improve the operational efficiency of the new matching method, a heterogeneous discretization scheme is adopted. Then the new definitions of similarity internal energy function, smoothness internal energy function and external energy function are presented to reduce the sensitivity to initial placement of the template. Gaussian function is used to widen the capturing scope of the external force in the external energy function definition. Finally, greedy optimization fast algorithm is used to obtain globally optimal solution with more acceptable complexity of computation than dynamic programming. The experimental results on real images show that our new matching method is efficient and robust to accurate contour extraction.