角点密度特征下的粘连蘑菇定位算法

在基于机器视觉实现蘑菇自动化采摘过程中，由于蘑菇苗床背景复杂多样，蘑菇群落之间尺度、形状差异大，且相互间存在复杂粘连，造成采摘位置定位困难，针对该问题，提出了以Harris角点为纹理特征的背景过滤算法，实现菌丝、木屑、杂草等干扰因素下的前景目标的准确提取；继而针对粘连蘑菇的尺度差异，提出了一种迭代方法搜索前景距离图中的区域极值点，在此基础上采用基于标记的分水岭算法实现粘连蘑菇的分割；最后利用椭圆拟合对蘑菇边界和中心坐标进行定位.通过实际场景中的蘑菇样本图片进行测试，证明算法定位准确性达到86.3%，平均处理时间为0.711 s，满足实时性要求.

Localization Algorithm Based on Corner Density Detection for Overlapping Mushroom Image

In automatic mushroom picking process, some influencing factors such as the presence of various backgrounds in the image, the huge scale difference among different mushrooms especially in overlapping mushrooms, make it tough to locate mushroom. In order to improve the target location accuracy, a new method of complex background suppression based on Harris corner detection was put forward. In view of the scale differences among overlapping mushrooms, an iterative algorithm for seeking extreme points of distance map was proposed. These extreme points were used as seeds in the Watershed algorithm for the segmentation of overlapping mushrooms. Finally, the segmented image for each overlapping mushroom was processed with the method of elliptical fitting to get the contours and center coordinate of individual mushroom. In order to verify the algorithms proposed in this study, an experiment was conducted over the mushrooms grown in the lab. The test results reveal that the location detection success rate is 86.3%. The average image processing time is 0.711 s that is aligned with the requirement of the automatic mushroom picking system.