基于几何特征的灵武长枣图像分割算法

智能采摘机器人在采摘灵武长枣果实时,其视觉系统采集的目标图像中,目标在自然场景下存在粘连、遮挡、重叠、阴影等问题,造成在图像目标识别时的误识对象问题,这对智能采摘是极其不利的。针对这一问题,提出了一种基于几何特征的灵武长枣图像分割的算法。根据灵武长枣的外形接近椭圆的特征,通过大量统计灵武长枣果实的外形特征数据,建立基于灵武长枣外形的几何模型。通过一系列图像预处理获得二值图像,再利用形态学变换进行连续腐蚀得到目标物的相对质心点位并标记,以确定目标物个数。以标记的质心作为模型的中心,在变换后的二值图像中建立该几何模型,利用所建立模型的边界曲线拟合出灵武长枣图像中目标物的分割线,从而实现灵武长枣图像的分割。实验结果表明,该方法能够简便快捷地解决图像目标物之间的粘连、阴影问题,并能保证高准确率。对于果实粘连较轻的图像,其分割准确率可达到92.31%。

Lingwu Long Jujubes Image Segmentation Algorithm Based on Geometric Features

The main purpose of this paper is to solve the problem of slight adhesion and shadow between objects in image. When the intelligent picking robot is used to pick the fruits of Lingwu Long Jujubes, there are problems of adhesion, occlusion, overlap and shadow in the target images of collected by the visual system in a natural scene, which will lead to misrecognition of objects in the image target recognition and be extremely unfavorable to intelligent picking. In order to solve this problem, a new algorithm for segmentation of Lingwu Long Jujubes image based on geometric features is proposed. According to the elliptical appearance of Lingwu Long Jujubes, a geometric model based on Lingwu Long Jujubes appearance is established through a large number of statistics on the shape characteristics of Lingwu Long Jujubes. The binary image is obtained by a series of image preprocessing, and then the relative centroid position of target object is obtained by continuous corrosion in morphological transformation, and these centroids are marked to determine the number of objects. The marked center is taken as the center of the model, the geometric model is established in the transformed binary image, and the boundary curve of the model is used to fit the segmentation line of the target object in Lingwu Long Jujubes image, so as to realize the segmentation of Lingwu Long Jujubes image. The experimental results show that the method can solve the problem of adhesion and shadow between objects and ensure high accuracy. The segmentation accuracy of the method can reach 92. 31% for the images with slight adhesion.