Crop rows detection in maize fields remains a challenging problem due to variation in illumination and weeds interference under field conditions.This study proposed an algorithm for detecting crop rows based on adapti...Crop rows detection in maize fields remains a challenging problem due to variation in illumination and weeds interference under field conditions.This study proposed an algorithm for detecting crop rows based on adaptive multi-region of interest(multi-ROI).First,the image was segmented into crop and soil and divided into several horizontally labeled strips.Feature points were located in the first image strip and initial ROI was determined.Then,the ROI window was shifted upward.For the next image strip,the operations for the previous strip were repeated until multiple ROIs were obtained.Finally,the least square method was carried out to extract navigation lines and detection lines in multi-ROI.The detection accuracy of the method was 95.3%.The average computation time was 240.8 ms.The results suggest that the proposed method has generally favorable performance and can meet the real-time and accuracy requirements for field navigation.展开更多
感兴趣区域在临床医学图像分析中占有重要地位.提出了一种基于单调推进曲线进化的感兴趣区域提取新方法.首先,通过极小化ROI(region of interest)能量函数,推导出区域速度函数项,并与基于边界的速度函数融合,提出融合ROI信息的单调推进S...感兴趣区域在临床医学图像分析中占有重要地位.提出了一种基于单调推进曲线进化的感兴趣区域提取新方法.首先,通过极小化ROI(region of interest)能量函数,推导出区域速度函数项,并与基于边界的速度函数融合,提出融合ROI信息的单调推进Snake模型.ROI信息能够增强曲线深入到对比度低且细窄的区域中的传播能力.其次,提出了多初始化快速推进算法,选择性地种植种子曲线有助于局部区域的生长从而进一步改善分割结果.此外,为提高计算效率,在多尺度空间进行数值求解,其中利用快速解传递方法实现粗一级尺度到细一级尺度解的传递,可以加速收敛.利用医学图像分割实验对该方法进行评估,结果表明:该方法能够快速、精确地提取低对比度和细窄的ROI区域.与现有方法相比,该方法的高效性同时体现在分割结果和计算代价上.展开更多
基金The authors acknowledge that the research was financially supported by the National Key Research and Development Program of China(Grant No.2017YFD0700902)the University Synergy Innovation Program of Anhui Province(Grant No.GXXT-2020-011).
文摘Crop rows detection in maize fields remains a challenging problem due to variation in illumination and weeds interference under field conditions.This study proposed an algorithm for detecting crop rows based on adaptive multi-region of interest(multi-ROI).First,the image was segmented into crop and soil and divided into several horizontally labeled strips.Feature points were located in the first image strip and initial ROI was determined.Then,the ROI window was shifted upward.For the next image strip,the operations for the previous strip were repeated until multiple ROIs were obtained.Finally,the least square method was carried out to extract navigation lines and detection lines in multi-ROI.The detection accuracy of the method was 95.3%.The average computation time was 240.8 ms.The results suggest that the proposed method has generally favorable performance and can meet the real-time and accuracy requirements for field navigation.
文摘感兴趣区域在临床医学图像分析中占有重要地位.提出了一种基于单调推进曲线进化的感兴趣区域提取新方法.首先,通过极小化ROI(region of interest)能量函数,推导出区域速度函数项,并与基于边界的速度函数融合,提出融合ROI信息的单调推进Snake模型.ROI信息能够增强曲线深入到对比度低且细窄的区域中的传播能力.其次,提出了多初始化快速推进算法,选择性地种植种子曲线有助于局部区域的生长从而进一步改善分割结果.此外,为提高计算效率,在多尺度空间进行数值求解,其中利用快速解传递方法实现粗一级尺度到细一级尺度解的传递,可以加速收敛.利用医学图像分割实验对该方法进行评估,结果表明:该方法能够快速、精确地提取低对比度和细窄的ROI区域.与现有方法相比,该方法的高效性同时体现在分割结果和计算代价上.
