In this paper, we reconsider the clustering problem for image over-segmentation from a new perspective. We propose a novel search algorithm called"active search" which explicitly considers neighbor continuit...In this paper, we reconsider the clustering problem for image over-segmentation from a new perspective. We propose a novel search algorithm called"active search" which explicitly considers neighbor continuity. Based on this search method, we design a back-and-forth traversal strategy and a joint assignment and update step to speed up the algorithm. Compared to earlier methods, such as simple linear iterative clustering(SLIC) and its variants, which use fixed search regions and perform the assignment and the update steps separately, our novel scheme reduces the number of iterations required for convergence,and also provides better boundaries in the oversegmentation results. Extensive evaluation using the Berkeley segmentation benchmark verifies that our method outperforms competing methods under various evaluation metrics. In particular, our method is fastest,achieving approximately 30 fps for a 481 × 321 image on a single CPU core. To facilitate further research, our code is made publicly available.展开更多
针对IEPF(iterative end point fit)算法提取激光雷达数据线特征过程中使用固定分割阀值导致的欠分割和过分割现象,提出一种结合支持向量机(support vector machine,SVM)的分开合并的线特征提取算法。在分开阶段,使用IEPF算法对数据初...针对IEPF(iterative end point fit)算法提取激光雷达数据线特征过程中使用固定分割阀值导致的欠分割和过分割现象,提出一种结合支持向量机(support vector machine,SVM)的分开合并的线特征提取算法。在分开阶段,使用IEPF算法对数据初步分割;在合并阶段,调整阀值尽可能消除欠分割的线段,分别提取过分割的线段间和正常线段间的接近度、共线度、重叠度3个特征作为特征向量,训练SVM模型,将SVM模型应用于实际测试中,对于分类结果为过分割的线段执行合并。实验结果表明,该算法有效消除了绝大部分IEPF算法进行线段提取产生的过分割和欠分割线段。展开更多
基金sponsored by National Natural Science Foundation of China (Nos. 61620106008 and 61572264)Huawei Innovation Research Program (HIRP)
文摘In this paper, we reconsider the clustering problem for image over-segmentation from a new perspective. We propose a novel search algorithm called"active search" which explicitly considers neighbor continuity. Based on this search method, we design a back-and-forth traversal strategy and a joint assignment and update step to speed up the algorithm. Compared to earlier methods, such as simple linear iterative clustering(SLIC) and its variants, which use fixed search regions and perform the assignment and the update steps separately, our novel scheme reduces the number of iterations required for convergence,and also provides better boundaries in the oversegmentation results. Extensive evaluation using the Berkeley segmentation benchmark verifies that our method outperforms competing methods under various evaluation metrics. In particular, our method is fastest,achieving approximately 30 fps for a 481 × 321 image on a single CPU core. To facilitate further research, our code is made publicly available.
文摘针对IEPF(iterative end point fit)算法提取激光雷达数据线特征过程中使用固定分割阀值导致的欠分割和过分割现象,提出一种结合支持向量机(support vector machine,SVM)的分开合并的线特征提取算法。在分开阶段,使用IEPF算法对数据初步分割;在合并阶段,调整阀值尽可能消除欠分割的线段,分别提取过分割的线段间和正常线段间的接近度、共线度、重叠度3个特征作为特征向量,训练SVM模型,将SVM模型应用于实际测试中,对于分类结果为过分割的线段执行合并。实验结果表明,该算法有效消除了绝大部分IEPF算法进行线段提取产生的过分割和欠分割线段。
