针对移动机器人同步定位与地图构建(SLAM)的闭环检测问题,提出改进闭环检测准确率的特征空间全排列向量匹配方法。使用ORB(Oriented FAST and Rotated BRIEF)方法提取图像特征点,创建基于视觉字典树的词袋,初步筛选出候选闭环图像。将...针对移动机器人同步定位与地图构建(SLAM)的闭环检测问题,提出改进闭环检测准确率的特征空间全排列向量匹配方法。使用ORB(Oriented FAST and Rotated BRIEF)方法提取图像特征点,创建基于视觉字典树的词袋,初步筛选出候选闭环图像。将图像分成4块大小均匀的区域,计算各区域视觉单词向量并全排列,作为特征空间信息。比较特征空间信息方法和词袋方法计算出的图像间距离值,选取最小值对应的图像对作为最佳闭环。相比词袋方法,特征空间信息方法可有效地改善图像特征匹配的感知混淆问题,在保证较高效率的同时,提高了闭环检测的准确率。展开更多
Software defect prediction is aimed to find potential defects based on historical data and software features. Software features can reflect the characteristics of software modules. However, some of these features may ...Software defect prediction is aimed to find potential defects based on historical data and software features. Software features can reflect the characteristics of software modules. However, some of these features may be more relevant to the class （defective or non-defective）, but others may be redundant or irrelevant. To fully measure the correlation between different features and the class, we present a feature selection approach based on a similarity measure （SM） for software defect prediction. First, the feature weights are updated according to the similarity of samples in different classes. Second, a feature ranking list is generated by sorting the feature weights in descending order, and all feature subsets are selected from the feature ranking list in sequence. Finally, all feature subsets are evaluated on a k-nearest neighbor （KNN） model and measured by an area under curve （AUC） metric for classification performance. The experiments are conducted on 11 National Aeronautics and Space Administration （NASA） datasets, and the results show that our approach performs better than or is comparable to the compared feature selection approaches in terms of classification performance.展开更多
文摘针对移动机器人同步定位与地图构建(SLAM)的闭环检测问题,提出改进闭环检测准确率的特征空间全排列向量匹配方法。使用ORB(Oriented FAST and Rotated BRIEF)方法提取图像特征点,创建基于视觉字典树的词袋,初步筛选出候选闭环图像。将图像分成4块大小均匀的区域,计算各区域视觉单词向量并全排列,作为特征空间信息。比较特征空间信息方法和词袋方法计算出的图像间距离值,选取最小值对应的图像对作为最佳闭环。相比词袋方法,特征空间信息方法可有效地改善图像特征匹配的感知混淆问题,在保证较高效率的同时,提高了闭环检测的准确率。
基金Project supported by the National Natural Science Foundation of China （Nos. 61673384 and 61502497）, the Guangxi Key Laboratory of Trusted Software （No. kx201530）, the China Postdoctoral Science Foundation （No. 2015M581887）, and the Scientific Research Innovation Project for Graduate Students of Jiangsu Province, China （No. KYLX15 1443）
文摘Software defect prediction is aimed to find potential defects based on historical data and software features. Software features can reflect the characteristics of software modules. However, some of these features may be more relevant to the class （defective or non-defective）, but others may be redundant or irrelevant. To fully measure the correlation between different features and the class, we present a feature selection approach based on a similarity measure （SM） for software defect prediction. First, the feature weights are updated according to the similarity of samples in different classes. Second, a feature ranking list is generated by sorting the feature weights in descending order, and all feature subsets are selected from the feature ranking list in sequence. Finally, all feature subsets are evaluated on a k-nearest neighbor （KNN） model and measured by an area under curve （AUC） metric for classification performance. The experiments are conducted on 11 National Aeronautics and Space Administration （NASA） datasets, and the results show that our approach performs better than or is comparable to the compared feature selection approaches in terms of classification performance.
