提出一种基于二维经验模态分解(2D EMD)的像素匹配方法，并结合五步非等步算法应用于在线三维面形测量中。将一正弦条纹投影到在线匀速运动的待测物体上，物体运动产生等效相移，在一个条纹周期范围内任意采集五帧变形条纹图。采用2D EMD方法对变形条纹图进行分解，可以得到与待测物体形貌变化一致的模态图像，通过大津法对其进行二值化处理，并截取可靠性较高的区域作为像素匹配模板进行像素匹配，使各帧条纹图中物点一一对应并计算出相应的等效相移量。再利用五步非等步相移算法解相可得到截断相位，采用相位展开算法得到连续相位，利用相位高度映射公式可恢复出被测物体的三维面形。计算机模拟与实验验证了该方法的有效性。

A new pixel matching method is proposed based on two-dimensional empirical mode decomposition (2D EMD). It combines with five steps of unequal step algorithm and is applied to the on-line three-dimensional shape measurement. A sinusoidal fringe is projected on the measured object moving with a constant speed. The object′s movement can produce the corresponding phase shifting deformed patterns. And five arbitrary deformed patterns are captured by CCD within a duty cycle of the grating pattern. The deformed patterns are decomposed with 2D EMD method. The mode patterns which is consistent with the object′ surface shape are obtained. And then Otsu algorithm is used to do the binarization. The higher reliability of the area is segmented to be regarded as a template to realize the pixel matching. And the equivalent shifted phase deformed patterns in which the points are one-to-one corresponding are calculated. Then, the five unequal steps phase shifting algorithm is used to obtain the wrapped phase, which forms unwrapped phase by the phase unwrapping algorithm. And the surface information of the measured object is retrieved via the phase-height formula. The computer simulations and experiments prove the feasibility of this method.