基于目标形态特征的工件自动分割方法

为了使交互式工件分割算法满足实时性的要求,提出了一种将工件形态特征与图像分割算法相结合的工件自动分割方法.利用MeanShift算法分割图像提取目标区域;利用形态学开运算消除目标区域的噪声,进而分离相连的目标区域;对目标区域进行边缘检测,计算完整的工件轮廓信息,然后根据外轮廓的面积确定工件区域;利用工件区域的最小外接矩形在图像中标出前景和背景区域,再利用GrabCut算法分别对前景和背景建立高斯混合模型,然后通过mincut/maxflow算法分割前景与背景区域,最终实现工件目标的提取.实验结果表明,对于制造商提供的样本,该方法分割工件的召回率和准确率分别为94.97%和88.48%,具有较强的实用性和良好的实时性.

Automatic Segmentation of Workpiece Based on Target Morphological Features

Since many enterprises produce a huge number of workpiece images every day, and the existing interactive workpiece segmentation algorithm can not meet the real-time requirements, a workpiece segmentation method combining workpiece morphological features and image segmentation algorithm is proposed. This method consists of four steps:firstly, the image is segmented by use of MeanShift algorithm to extract the target region; secondly, the noise in the target region is eliminated using morphological open operation, and then the connected target region can be separated; thirdly, the edge detection of the target region is carried out to calculate the complete workpiece contour information, and then the workpiece region is determined according to the size of the outer contour area; fourthly, the foreground and background regions are labeled by using the minimal contour rectangle of the workpiece area and the Gaussian mixture model is established using GrabCut algorithm for foreground and background respectively, then the foreground and background regions can be segmented by use of mincut/maxflow algorithm, and finally the workpiece object can be extracted. The experimental results show that, for the samples provided by the manufacturer, the recall and accuracy of the proposed method are 94.97% and 88.48% respectively, and the method has strong practicability and good real-time performance.