At present three-dimensional (3D) reconstruction of crop images and binocular vision guidance of agricultural robot are still important hot research contents in the related field, in which the feature extraction and matching of green crops is one of key technologies for monitoring crop growth state or providing 3D guiding information for agricultural robot because they directly affect the accuracy of obtained 3D information of crops. A feature extraction and matching method of the green crops is proposed in this paper. We focus on the crop regions in an image that can provide enough crop information. Firstly, a pre-treatment process of the image in RGB (red, green and blue) space is employed to segment the green crop from the backgrounds. Then the morphology opening operation with an optimal size is used to filter the noises including some isolated points or small areas caused by weeds, small stone, shadows and residue etc. Considering the field application conditions, the SURF is adopted to obtain the featuring points because of its attractive performance including repeatability, distinctiveness and robustness without bad time consumption. And the reason is that its detector and descriptor are scale invariance and rotation invariance with the length of 64 dimensions. There are 2 steps to get the featuring points: the first step is to employ Hessian matrix to detect the featuring points, and then, the non-maxima suppression is used to search the extreme points and the interpolation arithmetic is to position them; in the second step, the featuring points are extracted by using a main direction vector which is the main factor of the invariance property. Finally, the Euclidean distances between each searching points are calculated in the left and right image to measure the similarity of searching point pair. The ratio of the nearest distance to the sub-nearest is used to determine whether the point is the matching one or not. If the ratio is larger than the set threshold, the matching is right otherwise wrong. At the same time, for improving the accuracy of matching, the complete constraint matching is implemented to restrain the wrong matching points. The constraint consists of 3 components: the first is the local epipolar line constraint, which requests the matching points must be located in a certain region; the second is left-right constraint that is thex coordinate of the point in left image must be larger than that of the corresponding point in right image; the third is a point of the left image is allowed to only match sole one of the right image. The experimental results show that each of the 3 constraints makes the accuracy of matching decrease when the ratio rises; but when the complete constraint matching is applied, the accuracy of matching has an inconspicuous variation. After the constraint procedure is implemented, the corresponding pairs will be sorted according to their distances. And the smaller the distance is, the more the probability of correct matching is. Thirteen groups of images under various illumination conditions about celeries, vegetables and cabbages are used to test the algorithm in this paper. The experimental results show that SURF is superior to SIFT and can be used to obtain the 3D information of crops for agricultural machinery vision system. And the mean of the extraction rate of featuring points for SURF and SIFT is 1.2%, 3.3%, respectively; and the mean of the accurate rate of matching of them is 94.8%, 92.4%, respectively; the time consumption is 4.6 s, 4.8 s, respectively.%由于田间环境的复杂性,绿色作物特征提取与匹配仍然是基于双目视觉技术农田作物三维信息获取急需解决的关键技术之一.该文首先在RGB空间进行图像分割滤波处理.然后,采用SURF算法旋转不变性分两步获取绿色作物特征点对:第一步采用Hessian矩阵检测作物特征点,运用非极大值抑制法和插值运算寻找、定位极值点;第二步确定特征点主方向,采用描述算子进行特征点提取.最后,运用最近距离比次近距离法进行特征点匹配,并采用全约束条件滤除错误的匹配点对.同时将SURF和SIFT法进行对比分析,通过对不同光照、土壤的田间条件下芥蓝 、芹菜、白菜13组图像进行试验,结果表明采用SUFR和SIFT法绿色作物特征提取率均值分别为1.2%、3.3%,双目视觉系统左、右作物图像特征正确匹配率的均值分别为94.8%、92.4%,时间消耗均值分别为4.6s、4.8s.采用SURF优越于采用SIFT法,这为进一步进行农业机械3D视觉导航或基于无线传感器网络的田间作物在线三维信息准确获取提供可借鉴思路和方法.
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