为了实现复杂背景下的红外小目标检测,提出了一种基于协作稀疏编码(CSC)的红外小目标检测算法。首先通过滑动窗口法提取待测试图像的图像块,并将其转化为列向量作为超完备字典;然后采用CSC模型计算每一个图像块在超完备字典中的系数矩...为了实现复杂背景下的红外小目标检测,提出了一种基于协作稀疏编码(CSC)的红外小目标检测算法。首先通过滑动窗口法提取待测试图像的图像块,并将其转化为列向量作为超完备字典;然后采用CSC模型计算每一个图像块在超完备字典中的系数矩阵以及误差矩阵,其中系数矩阵的L2,1范数代表图像的背景信息,而误差矩阵的L1,2范数代表红外小目标信息;进而利用ADMM(alternating directional method of multiplier)算法解算,得到系数矩阵和误差矩阵;最后通过误差矩阵重建,得到红外小目标的位置。仿真及公开数据实验结果,证实了本文方法的有效性。展开更多
Face recognition has been widely used and developed rapidly in recent years.The methods based on sparse representation have made great breakthroughs,and collaborative representation-based classification(CRC)is the typ...Face recognition has been widely used and developed rapidly in recent years.The methods based on sparse representation have made great breakthroughs,and collaborative representation-based classification(CRC)is the typical representative.However,CRC cannot distinguish similar samples well,leading to a wrong classification easily.As an improved method based on CRC,the two-phase test sample sparse representation(TPTSSR)removes the samples that make little contribution to the representation of the testing sample.Nevertheless,only one removal is not sufficient,since some useless samples may still be retained,along with some useful samples maybe being removed randomly.In this work,a novel classifier,called discriminative sparse parameter(DSP)classifier with iterative removal,is proposed for face recognition.The proposed DSP classifier utilizes sparse parameter to measure the representation ability of training samples straight-forward.Moreover,to avoid some useful samples being removed randomly with only one removal,DSP classifier removes most uncorrelated samples gradually with iterations.Extensive experiments on different typical poses,expressions and noisy face datasets are conducted to assess the performance of the proposed DSP classifier.The experimental results demonstrate that DSP classifier achieves a better recognition rate than the well-known SRC,CRC,RRC,RCR,SRMVS,RFSR and TPTSSR classifiers for face recognition in various situations.展开更多
文摘为了实现复杂背景下的红外小目标检测,提出了一种基于协作稀疏编码(CSC)的红外小目标检测算法。首先通过滑动窗口法提取待测试图像的图像块,并将其转化为列向量作为超完备字典;然后采用CSC模型计算每一个图像块在超完备字典中的系数矩阵以及误差矩阵,其中系数矩阵的L2,1范数代表图像的背景信息,而误差矩阵的L1,2范数代表红外小目标信息;进而利用ADMM(alternating directional method of multiplier)算法解算,得到系数矩阵和误差矩阵;最后通过误差矩阵重建,得到红外小目标的位置。仿真及公开数据实验结果,证实了本文方法的有效性。
基金Project(2019JJ40047)supported by the Hunan Provincial Natural Science Foundation of ChinaProject(kq2014057)supported by the Changsha Municipal Natural Science Foundation,China。
文摘Face recognition has been widely used and developed rapidly in recent years.The methods based on sparse representation have made great breakthroughs,and collaborative representation-based classification(CRC)is the typical representative.However,CRC cannot distinguish similar samples well,leading to a wrong classification easily.As an improved method based on CRC,the two-phase test sample sparse representation(TPTSSR)removes the samples that make little contribution to the representation of the testing sample.Nevertheless,only one removal is not sufficient,since some useless samples may still be retained,along with some useful samples maybe being removed randomly.In this work,a novel classifier,called discriminative sparse parameter(DSP)classifier with iterative removal,is proposed for face recognition.The proposed DSP classifier utilizes sparse parameter to measure the representation ability of training samples straight-forward.Moreover,to avoid some useful samples being removed randomly with only one removal,DSP classifier removes most uncorrelated samples gradually with iterations.Extensive experiments on different typical poses,expressions and noisy face datasets are conducted to assess the performance of the proposed DSP classifier.The experimental results demonstrate that DSP classifier achieves a better recognition rate than the well-known SRC,CRC,RRC,RCR,SRMVS,RFSR and TPTSSR classifiers for face recognition in various situations.
