Ground-based cloud classification is challenging due to extreme variations in the appearance of clouds under different atmospheric conditions. Texture classification techniques have recently been introduced to deal wi...Ground-based cloud classification is challenging due to extreme variations in the appearance of clouds under different atmospheric conditions. Texture classification techniques have recently been introduced to deal with this issue. A novel texture descriptor, the salient local binary pattern （SLBP）, is proposed for ground-based cloud classification. The SLBP takes advantage of the most frequently occurring patterns （the salient patterns） to capture descriptive information. This feature makes the SLBP robust to noise. Experimental results using ground-based cloud images demonstrate that the proposed method can achieve better results than current state-of-the-art methods.展开更多
To investigate the robustness of face recognition algorithms under the complicated variations of illumination, facial expression and posture, the advantages and disadvantages of seven typical algorithms on extracting ...To investigate the robustness of face recognition algorithms under the complicated variations of illumination, facial expression and posture, the advantages and disadvantages of seven typical algorithms on extracting global and local features are studied through the experiments respectively on the Olivetti Research Laboratory database and the other three databases （the three subsets of illumination, expression and posture that are constructed by selecting images from several existing face databases）. By taking the above experimental results into consideration, two schemes of face recognition which are based on the decision fusion of the twodimensional linear discriminant analysis （2DLDA） and local binary pattern （LBP） are proposed in this paper to heighten the recognition rates. In addition, partitioning a face nonuniformly for its LBP histograms is conducted to improve the performance. Our experimental results have shown the complementarities of the two kinds of features, the 2DLDA and LBP, and have verified the effectiveness of the proposed fusion algorithms.展开更多
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
细胞局部二值模式(cell structured Local Binary Pattern)不能将人体图像的局部信息与全局信息相结合。针对这一不足,在细胞局部二值模式特征的基础上,提出多尺度细胞局部二值模式(Multi-scale cell structured Local Binary Pattern,M...细胞局部二值模式(cell structured Local Binary Pattern)不能将人体图像的局部信息与全局信息相结合。针对这一不足,在细胞局部二值模式特征的基础上,提出多尺度细胞局部二值模式(Multi-scale cell structured Local Binary Pattern,MLBP)特征描述子,联合局部与全局信息,增加检测特征的信息量;另外,在MLBP的基础上进一步提出一个控制因子调节的新算子—可调多尺度细胞局部二值模式(Adjustable Multi-scale cell structured Local Binary Pattern,AMLBP),利用控制因子选择MLBP的最佳表征,提高人体检测的准确率。实验结果表明所提出的两个新特征较前人提出的特征更有效。展开更多
基金Supported by the National Natural Science Foundation of China (61172103, 60933010, and 60835001)
文摘Ground-based cloud classification is challenging due to extreme variations in the appearance of clouds under different atmospheric conditions. Texture classification techniques have recently been introduced to deal with this issue. A novel texture descriptor, the salient local binary pattern （SLBP）, is proposed for ground-based cloud classification. The SLBP takes advantage of the most frequently occurring patterns （the salient patterns） to capture descriptive information. This feature makes the SLBP robust to noise. Experimental results using ground-based cloud images demonstrate that the proposed method can achieve better results than current state-of-the-art methods.
文摘To investigate the robustness of face recognition algorithms under the complicated variations of illumination, facial expression and posture, the advantages and disadvantages of seven typical algorithms on extracting global and local features are studied through the experiments respectively on the Olivetti Research Laboratory database and the other three databases （the three subsets of illumination, expression and posture that are constructed by selecting images from several existing face databases）. By taking the above experimental results into consideration, two schemes of face recognition which are based on the decision fusion of the twodimensional linear discriminant analysis （2DLDA） and local binary pattern （LBP） are proposed in this paper to heighten the recognition rates. In addition, partitioning a face nonuniformly for its LBP histograms is conducted to improve the performance. Our experimental results have shown the complementarities of the two kinds of features, the 2DLDA and LBP, and have verified the effectiveness of the proposed fusion algorithms.
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。
