基于改进ASM模型的人体特征点定位和建模方法

Feature Point Positioning and Modeling Approach for Human Body Based on Improved ASM

针对传统的Procrustes归一化方法需要多次迭代且计算量大的缺陷,采用标记定位点的方法,以平均形状作为初始化规则模型,只经过一次平移旋转及缩放即可达到训练集对齐的效果;传统ASM算法计算时间较长且易使特征点因灰度模型相似而错误匹配,采用以特征点为中心,选取其周围的矩形区域灰度分布训练灰度模型,并选择24邻域点进行目标搜索。实验结果表明:改进的ASM方法进行人体特征点定位,减少了算法迭代次数,缩短了算法运行时间,提高了定位精度。

Traditional Procrustes normalization needs many iterations which will spend a lot of time. Here training samples alignment was set only after once translation, rotation and scaling operations by marking anchor point and using average body shape as the initialization rules model. Traditional ASM algorithm leads to a long computing time and is easily to make the feature points matching error for gray model's similarity. It was improved by using every feature points as a center point, training gray model though its rounded rectangular gray distribution, and searching target points within its 24 neighborhood points. Experimental results show that the key feature point positioning method for Human body based on this improved ASM reduces the number of iterations, shortens the running time, and improves the positioning accuracy.