为了提高车道线检测的准确性与鲁棒性,降低光照变化与背景干扰的影响,提出了一种改进的Hough变换耦合密度空间聚类的车道线检测算法。首先,建立车道线模型,将车道边界分解为一系列的小线段,借助最小二乘法来表示车道线中的线段。再利用...为了提高车道线检测的准确性与鲁棒性,降低光照变化与背景干扰的影响,提出了一种改进的Hough变换耦合密度空间聚类的车道线检测算法。首先,建立车道线模型,将车道边界分解为一系列的小线段,借助最小二乘法来表示车道线中的线段。再利用改进的Hough变换对图像中的小线段进行检测。引入具有密度空间聚类方法(density based spatial clustering of applications with noise,DBSCAN),对提取的小线段进行聚类,过滤掉图像中的冗余和噪声,同时保留车道边界的关键信息。随后,利用边缘像素的梯度方向来定义小线段的方向,使得边界同一侧的小线段具有相同的方向,而位于相反车道边界的两个小线段具有相反的方向,通过小线段的方向函数得到车道线段候选簇。最后,根据得到的小线段候选簇,利用消失点来拟合最终车道线。在Caltech数据集与实际道路中进行测试,数据表明:与当前流行的车道线检测算法相比,在光照变化、背景干扰等不良因素下,所以算法呈现出更理想的准确性与稳健,可准确识别正常车道线。展开更多
In this paper, the authors present ConGrap, a novel contour detector for finding closed contours with semantic connections. Based on gradient-based edge detection, a Gradient Map is generated to store the orientation ...In this paper, the authors present ConGrap, a novel contour detector for finding closed contours with semantic connections. Based on gradient-based edge detection, a Gradient Map is generated to store the orientation of every edge pixel. Using the edge image and the generated Gradient Map, ConGrap separates the image into semantic parts and objects. Each edge pixel is mapped to a contour by a three-stage hierarchical analysis of neighbored pixels and ensures the closing of contours. A final post-process of ConGrap extracts the contour borderlines and merges them, if they semantically relate to each other. In contrast to common edge and contour detections, ConGrap not only produces an edge image, but also provides additional information （e.g., the borderline pixel coordinates the bounding box, etc.） for every contour. Additionally, the resulting contour image provides closed contours without discontinuities and merged regions with semantic connections. Consequently, the ConGrap contour image can be seen as an enhanced edge image as well as a kind of segmentation and object recognition.展开更多
文摘为了提高车道线检测的准确性与鲁棒性,降低光照变化与背景干扰的影响,提出了一种改进的Hough变换耦合密度空间聚类的车道线检测算法。首先,建立车道线模型,将车道边界分解为一系列的小线段,借助最小二乘法来表示车道线中的线段。再利用改进的Hough变换对图像中的小线段进行检测。引入具有密度空间聚类方法(density based spatial clustering of applications with noise,DBSCAN),对提取的小线段进行聚类,过滤掉图像中的冗余和噪声,同时保留车道边界的关键信息。随后,利用边缘像素的梯度方向来定义小线段的方向,使得边界同一侧的小线段具有相同的方向,而位于相反车道边界的两个小线段具有相反的方向,通过小线段的方向函数得到车道线段候选簇。最后,根据得到的小线段候选簇,利用消失点来拟合最终车道线。在Caltech数据集与实际道路中进行测试,数据表明:与当前流行的车道线检测算法相比,在光照变化、背景干扰等不良因素下,所以算法呈现出更理想的准确性与稳健,可准确识别正常车道线。
文摘In this paper, the authors present ConGrap, a novel contour detector for finding closed contours with semantic connections. Based on gradient-based edge detection, a Gradient Map is generated to store the orientation of every edge pixel. Using the edge image and the generated Gradient Map, ConGrap separates the image into semantic parts and objects. Each edge pixel is mapped to a contour by a three-stage hierarchical analysis of neighbored pixels and ensures the closing of contours. A final post-process of ConGrap extracts the contour borderlines and merges them, if they semantically relate to each other. In contrast to common edge and contour detections, ConGrap not only produces an edge image, but also provides additional information （e.g., the borderline pixel coordinates the bounding box, etc.） for every contour. Additionally, the resulting contour image provides closed contours without discontinuities and merged regions with semantic connections. Consequently, the ConGrap contour image can be seen as an enhanced edge image as well as a kind of segmentation and object recognition.
