针对人工判读道岔尖轨伸缩位移图像存在效率低和误差大的问题,为实现尖轨伸缩位移的实时自动监测,提出1种基于层次积分梯度的尖轨伸缩位移图像自动判读算法。采用逐层逼近目标区域的方式,克服尖轨伸缩位移图像中噪声和不相关信息的干扰,...针对人工判读道岔尖轨伸缩位移图像存在效率低和误差大的问题,为实现尖轨伸缩位移的实时自动监测,提出1种基于层次积分梯度的尖轨伸缩位移图像自动判读算法。采用逐层逼近目标区域的方式,克服尖轨伸缩位移图像中噪声和不相关信息的干扰,以SURF(Speeded Up Robust Features)算子的特征匹配结果为指导,逐步提取图像中的有效区域;利用积分梯度的抗噪特性,根据积分梯度和极值点精确定位刻度尺的特征点位置,结合可信度检验,实现尖轨伸缩位移图像的自动判读。用该算法对监测现场采集的尖轨伸缩位移图像进行判读的结果表明,可在2s内自动判读尖轨伸缩位移图像,总体偏差在0.5mm以内,能够满足目前现场对尖轨伸缩位移实时自动监测的要求。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
文摘针对人工判读道岔尖轨伸缩位移图像存在效率低和误差大的问题,为实现尖轨伸缩位移的实时自动监测,提出1种基于层次积分梯度的尖轨伸缩位移图像自动判读算法。采用逐层逼近目标区域的方式,克服尖轨伸缩位移图像中噪声和不相关信息的干扰,以SURF(Speeded Up Robust Features)算子的特征匹配结果为指导,逐步提取图像中的有效区域;利用积分梯度的抗噪特性,根据积分梯度和极值点精确定位刻度尺的特征点位置,结合可信度检验,实现尖轨伸缩位移图像的自动判读。用该算法对监测现场采集的尖轨伸缩位移图像进行判读的结果表明,可在2s内自动判读尖轨伸缩位移图像,总体偏差在0.5mm以内,能够满足目前现场对尖轨伸缩位移实时自动监测的要求。
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.
