目的 局部图像描述符凭借其优越的特性广泛应用于计算机视觉和图像处理多个领域，如图像匹配、图像分类、图像搜索、从运动恢复结构等。方法 本文提出了一种新的局部特征：最大边缘方向模式（MEOP）。该特征计算中心像素和周围像素间最大强度差值，对其位置和符号进行编码。呈现最大强度差值的像素代表局部领域的最强边缘处，其位置描述了径向方向，差值的符号描述了径向方向的朝向。相对于局部二进制模式，由于MEOP仅编码最大强度差值，所以只要最大强度差值的位置和符号不出现改变，MEOP模式就不会发生改变。所以MEOP模式的鲁棒性较高，抗噪声能力更强。MEOP在描述图像的局部结构特征上和局部二进制模式是完全不一样的，两种模式在表达图像的局部结构方面具有较大的互补性。利用局部旋转不变坐标系计算最大边缘方向模式，采用旋转不变强度序空间分割方法和多支撑域对最大边缘方向模式进行空间汇聚得到一种新的局部图像描述符：最大边缘方向模式直方图（MEOPH）。相对于采用局部二进制模式的MRRID（multisupport region rotation and intensity monotonic invariant descriptor）描述符相比，采用最大边缘方向模式的MEOPH描述符具有不同的统计特性和更优越的性能。结果 在牛津大学仿射不变研究小组的标准测试图像集上对目前的主流局部描述符（SIFT（scale invariant feature transform）、DAISY、CS-LBP（center-symmetric local binary pattern）、HRI-CSLTP（histogram of relative intensities and center-symmetric local ternary patterns）和MRRID）进行了图像匹配实验。标准测试图像集上的实验结果表明，本文MEOPH和MRRID获得了最好的性能，MEOPH在所有测试数据集上的匹配性能都优于SIFT、DAISY、CS-LBP和HRI-CSLTP，在大多数情况下MEOPH的图像匹配效果要比MRRID稍好一些。在标准测试图像集上添加高斯噪声的图像匹配实验中，MEOPH的性能则远远优于MRRID。另外，MEOPH和MRRID具有很大的互补性，在二者联合情况下匹配性能大大增强。结论 所以，MEOPH在稳定性方面的优越性能使其可以适应复杂环境下的局部描述符匹配场合。另外，在辨别性要求很高的局部描述符匹配场合，还可以配合MRRID一起使用。

Objective Owing to their superior characteristics, local image descriptors have been widely used in many computer vision and image processing fields, such as image matching, image classification, image search, and structure from motion.Method This study proposes a new local feature called max edge orientation pattern (MEOP). First, the maximum intensity difference between the center pixel and surrounding ones is calculated. Second, the position and sign of the maximum intensity difference are encoded. The pixel with the maximum intensity difference denotes the strongest edge of the local adjacent region. The position of MEOP describes the radial direction, and the sign describes the arrow of the direction. Compared with the local binary pattern, the maximum edge direction pattern only encodes the maximum intensity difference. Therefore, the maximum edge direction pattern does not change as long as the position and sign of the maximum intensity difference do not change. The robustness of the maximum edge direction pattern is high, and its anti-noise ability is strong. The maximum edge direction pattern differs from the local binary pattern in describing the local structure of the image. Nevertheless, the two patterns are complementary in expressing the local structure of the image. Local rotation invariant coordinates are used to calculate the maximum edge orientation pattern. The rotation-invariant intensity-order space division method and multiple support regions are employed to pool the maximum edge orientation pattern and obtain a new local image descriptor, namely, maximum edge orientation pattern histogram (MEOPH). Compared with the MRRID descriptor using the local binary pattern, the MEOPH descriptor with the maximum edge direction pattern has different statistical properties and superior performance.Results With the standard test image set of the affine invariant research group of University of Oxford, image matching experiments are conducted on current popular descriptors, including SIFT, DAISY, CS-LBP, HRI-CSLTP, and MRRID. Experimental results on standard test image sets show that MEOPH and MRRID demonstrate the best performance. The matching performance of MEOPH is better than that of SIFT, DAISY, CS-LBP, and HRI-CSLTP in all test data sets and is slightly better than that of MRRID in most cases. The matching performance of MEOPH is much better than that of MRRID in the experiments wherein Gaussian noise is added to the standard test image sets. In addition, MEOPH and MRRID complement each other in image matching, and matching performance is significantly enhanced by the combination of the two descriptors.Conclusion The superior performance of MEOPH in terms of stability makes the method suitable for local descriptor matching in complex environments. In the context of high-discrimination requirements in local descriptor matching, MEOPH can be used in conjunction with MRRID.