摘要
针对葡萄树枝上各个芽的粗细不均匀、形状不规则造成芽点位置难以检测的问题,将骨架提取方法应用到葡萄树特征检测中。在室内环境下采集了葡萄树枝彩色图像,选择对比度较明显的B分量图像进行了预处理。通过均值滤波,消除了噪声;通过阈值分割,去除了铁丝和阴影,并获取了葡萄树枝二值图像。在此基础上,分别采用形态学细化、Zhang细化、Rosenfeld细化算法提取了二值图像中葡萄树枝的骨架,并对比分析了处理结果。研究结果表明,Rosenfeld细化算法能够较好地维持骨架的连通性、中心性,所提取的葡萄树枝骨架最贴近原形状,为进一步检测葡萄树枝的芽点奠定了基础。
In order to solve the problem that the bud point position is hard to detect because of the uneven thickness and the irregular shape of the bud, the skeleton extraction method was applied to the vines feature detection. The color image of grapevine was captured indoor. The blue component of the color image was selected for the image preprocessing. Mean filter was used to remove the noise. Threshold segmentation was used to eliminate the wire and shadow and get the binary image of the grapevine. Then morphologic thinning, Zhang thinning, Rosenfeld thinning were used to extract the skeleton of vines binary image. The thinning algorithms were compared to analyze the effect. The results indicate that the skeleton extracted by Rosenfeld thinning gets close to the original shape with well connectivity and centrality. This algorithm lays the foundation of grapevine bud point detection.
出处
《机电工程》
CAS
2013年第4期501-504,共4页
Journal of Mechanical & Electrical Engineering
基金
浙江省特种装备制造与先进加工技术重点实验室开放基金资助项目(2011EM002)
