Object contour plays an important role in fields such as semantic segmentation and image classification. However, the extraction of contour is a difficult task, especially when the contour is incomplete or unclosed. I...Object contour plays an important role in fields such as semantic segmentation and image classification. However, the extraction of contour is a difficult task, especially when the contour is incomplete or unclosed. In this paper, the existing contour detection approaches are reviewed and roughly divided into three categories： pixel-based, edge-based, and region-based. In addition, since the traditional contour detection approaches have achieved a high degree of sophistication, the deep convolutional neural networks （DCNNs） have good performance in image recognition, therefore, the DCNNs based contour detection approaches are also covered in this paper. Moreover, the future development of contour detection is analyzed and predicted.展开更多
为了应对由复杂场景和目标形变所造成的目标难以检测的问题,提出一种基于图像显著性轮廓的目标检测方法.该方法首先利用全局概率边界算法(Globalized probability of boundary,g Pb)提取图像轮廓,然后利用改进的最大类间方差法(Otsu)自...为了应对由复杂场景和目标形变所造成的目标难以检测的问题,提出一种基于图像显著性轮廓的目标检测方法.该方法首先利用全局概率边界算法(Globalized probability of boundary,g Pb)提取图像轮廓,然后利用改进的最大类间方差法(Otsu)自适应地阈值处理获得图像的显著性轮廓;再通过检测并移除目标不稳定轮廓部分构造目标的鲁棒扇形模型;最后联合轮廓的多种局部及全局特征提出三种相似且基于特征概率密度分布的匹配策略,分别检测目标、目标镜面翻转以及发生旋转的目标.通过对多个数据库的实验分析,该方法能够有效地检测出目标及目标镜面翻转,同时在小偏转角范围有效检测旋转后的目标.展开更多
基金supported by National Natural Science Foundation of China (Nos. 61503378, 61473293, 51405485 and 61403378)the Project of Development in Tianjin for Scientific Research Institutes, and Tianjin Government (No. 16PTYJGX00050)
文摘Object contour plays an important role in fields such as semantic segmentation and image classification. However, the extraction of contour is a difficult task, especially when the contour is incomplete or unclosed. In this paper, the existing contour detection approaches are reviewed and roughly divided into three categories： pixel-based, edge-based, and region-based. In addition, since the traditional contour detection approaches have achieved a high degree of sophistication, the deep convolutional neural networks （DCNNs） have good performance in image recognition, therefore, the DCNNs based contour detection approaches are also covered in this paper. Moreover, the future development of contour detection is analyzed and predicted.
文摘为了应对由复杂场景和目标形变所造成的目标难以检测的问题,提出一种基于图像显著性轮廓的目标检测方法.该方法首先利用全局概率边界算法(Globalized probability of boundary,g Pb)提取图像轮廓,然后利用改进的最大类间方差法(Otsu)自适应地阈值处理获得图像的显著性轮廓;再通过检测并移除目标不稳定轮廓部分构造目标的鲁棒扇形模型;最后联合轮廓的多种局部及全局特征提出三种相似且基于特征概率密度分布的匹配策略,分别检测目标、目标镜面翻转以及发生旋转的目标.通过对多个数据库的实验分析,该方法能够有效地检测出目标及目标镜面翻转,同时在小偏转角范围有效检测旋转后的目标.
