在深度地图序列的手势识别中,针对不同的人在不同的时间或同一个人在不同的时间手势也不相同的问题,本文提出了特征加权融合和交叉主题测试法来进行基于深度地图序列的手势识别。首先,对于深度视频序列采用多级时间采样来生成含有相关...在深度地图序列的手势识别中,针对不同的人在不同的时间或同一个人在不同的时间手势也不相同的问题,本文提出了特征加权融合和交叉主题测试法来进行基于深度地图序列的手势识别。首先,对于深度视频序列采用多级时间采样来生成含有相关手势信息的长、中和短3种不同长度的序列;其次,通过计算连续帧的绝对差提取时空信息生成深度运动图;然后,利用梯度方向直方图(histogram of oriented gradien,HOG)和局部二值模式(local binary patterns,LBP)从生成的深度运动图中提取形状和纹理特征,进行局部特征聚集描述符(vector of local aggregation descriptor,VLAD)编码;最后,采用主成分分析(principal component analysis,PCA)降维后将这两种特征进行加权融合和交叉主题测试后送到极限学习机器中进行分类识别。在公开具有挑战性的MSR Gesture 3D动态手势深度数据集上进行实验评估性能,所提的特征加权融合算法和交叉主题测试算法的识别率相较LBP和HOG算法融合的基础上分别提高0.82%和5.17%。实验结果表明,改进的方法具有更好的识别率。展开更多
Automatic detection of a designated building area(DBA)is a research hotspot in the field of target detection using remote sensing images.Target detection is urgently needed for tasks such as illegal building monitorin...Automatic detection of a designated building area(DBA)is a research hotspot in the field of target detection using remote sensing images.Target detection is urgently needed for tasks such as illegal building monitoring,dynamic land use monitoring,antiterrorism efforts,and military reconnaissance.The existing detection methods generally have low efficiency and poor detection accuracy due to the large size and complexity of remote sensing scenes.To address the problems of the current detection methods,this paper presents a DBA detection method that uses hierarchical structural constraints in remote sensing images.Our method was conducted in two main stages.(1)During keypoint generation,we proposed a screening method based on structural pattern descriptors.The local pattern feature of the initial keypoints was described by a multilevel local pattern histogram(MLPH)feature;then,we used one-class support vector machine(OC-SVM)merely to screen those building attribute keypoints.(2)To match the screened keypoints,we proposed a reliable DBA detection method based on matching the local structural similarities of the screened keypoints.We achieved precise keypoint matching by calculating the similarities of the local skeletal structures in the neighboring areas around the roughly matched keypoints to achieve DBA detection.We tested the proposed method on building area sets of different types and at different time phases.The experimental results show that the proposed method is both highly accurate and computationally efficient.展开更多
文摘在深度地图序列的手势识别中,针对不同的人在不同的时间或同一个人在不同的时间手势也不相同的问题,本文提出了特征加权融合和交叉主题测试法来进行基于深度地图序列的手势识别。首先,对于深度视频序列采用多级时间采样来生成含有相关手势信息的长、中和短3种不同长度的序列;其次,通过计算连续帧的绝对差提取时空信息生成深度运动图;然后,利用梯度方向直方图(histogram of oriented gradien,HOG)和局部二值模式(local binary patterns,LBP)从生成的深度运动图中提取形状和纹理特征,进行局部特征聚集描述符(vector of local aggregation descriptor,VLAD)编码;最后,采用主成分分析(principal component analysis,PCA)降维后将这两种特征进行加权融合和交叉主题测试后送到极限学习机器中进行分类识别。在公开具有挑战性的MSR Gesture 3D动态手势深度数据集上进行实验评估性能,所提的特征加权融合算法和交叉主题测试算法的识别率相较LBP和HOG算法融合的基础上分别提高0.82%和5.17%。实验结果表明,改进的方法具有更好的识别率。
基金This work was supported in part by the National Natural Science Foundation of China(Grant No.61601006)the Beijing Natural Science Foundation(Grant No.4192021)the Equipment Pre-Research Foundation(Grant No.61404130312).
文摘Automatic detection of a designated building area(DBA)is a research hotspot in the field of target detection using remote sensing images.Target detection is urgently needed for tasks such as illegal building monitoring,dynamic land use monitoring,antiterrorism efforts,and military reconnaissance.The existing detection methods generally have low efficiency and poor detection accuracy due to the large size and complexity of remote sensing scenes.To address the problems of the current detection methods,this paper presents a DBA detection method that uses hierarchical structural constraints in remote sensing images.Our method was conducted in two main stages.(1)During keypoint generation,we proposed a screening method based on structural pattern descriptors.The local pattern feature of the initial keypoints was described by a multilevel local pattern histogram(MLPH)feature;then,we used one-class support vector machine(OC-SVM)merely to screen those building attribute keypoints.(2)To match the screened keypoints,we proposed a reliable DBA detection method based on matching the local structural similarities of the screened keypoints.We achieved precise keypoint matching by calculating the similarities of the local skeletal structures in the neighboring areas around the roughly matched keypoints to achieve DBA detection.We tested the proposed method on building area sets of different types and at different time phases.The experimental results show that the proposed method is both highly accurate and computationally efficient.
