Based on the measurement model of inverse synthetic aperture radar (ISAR) within a small aspect sector,an imaging method was presented with the application of sparse signal processing.This method can form higher resol...Based on the measurement model of inverse synthetic aperture radar (ISAR) within a small aspect sector,an imaging method was presented with the application of sparse signal processing.This method can form higher resolution inverse synthetic aperture radar images from compensating incomplete measured data,and improves the clarity of the images and makes the feature structure much more clear,which is helpful for target recognition.The simulation results indicate that this method can provide clear ISAR images with high contrast under complex motion case.展开更多
A contourlet-transform （CT） based sparse independent component analysis for blind image separation is proposed. The images are first decomposed into sets of local features with various degrees of sparsity, and then ...A contourlet-transform （CT） based sparse independent component analysis for blind image separation is proposed. The images are first decomposed into sets of local features with various degrees of sparsity, and then the intrinsic property is used to select the best （sparsest） subsets of features for further separation. Based on sparse description of the contourlet- transform, the proposed approach is able to yield better performance, including faster convergence and the certain order for the separated signals. Simulation results confirm the validity of the proposed method.展开更多
Under the underdetermined blind sources separation(UBSS) circumstance,it is difficult to estimate the mixing matrix with high-precision because of unknown sparsity of signals.The mixing matrix estimation is proposed b...Under the underdetermined blind sources separation(UBSS) circumstance,it is difficult to estimate the mixing matrix with high-precision because of unknown sparsity of signals.The mixing matrix estimation is proposed based on linear aggregation degree of signal scatter plot without knowing sparsity,and the linear aggregation degree evaluation of observed signals is presented which obeys generalized Gaussian distribution(GGD).Both the GGD shape parameter and the signals' correlation features affect the observation signals sparsity and further affected the directionality of time-frequency scatter plot.So a new mixing matrix estimation method is proposed for different sparsity degrees,which especially focuses on unclear directionality of scatter plot and weak linear aggregation degree.Firstly,the direction of coefficient scatter plot by time-frequency transform is improved and then the single source coefficients in the case of weak linear clustering is processed finally the improved K-means clustering is applied to achieve the estimation of mixing matrix.The proposed algorithm reduces the requirements of signals sparsity and independence,and the mixing matrix can be estimated with high accuracy.The simulation results show the feasibility and effectiveness of the algorithm.展开更多
Background The accurate(quantitative)analysis of 3D face deformation is a problem of increasing interest in many applications.In particular,defining a 3D model of the face deformation into a 2D target image to capture...Background The accurate(quantitative)analysis of 3D face deformation is a problem of increasing interest in many applications.In particular,defining a 3D model of the face deformation into a 2D target image to capture local and asymmetric deformations remains a challenge in existing literature.A measure of such local deformations may be a relevant index for monitoring the rehabilitation exercises of patients suffering from Par-kinson’s or Alzheimer’s disease or those recovering from a stroke.Methods In this paper,a complete framework that allows the construction of a 3D morphable shape model(3DMM)of the face is presented for fitting to a target RGB image.The model has the specific characteristic of being based on localized components of deformation.The fitting transformation is performed from 3D to 2D and guided by the correspondence between landmarks detected in the target image and those manually annotated on the average 3DMM.The fitting also has the distinction of being performed in two steps to disentangle face deformations related to the identity of the target subject from those induced by facial actions.Results The method was experimentally validated using the MICC-3D dataset,which includes 11 subjects.Each subject was imaged in one neutral pose and while performing 18 facial actions that deform the face in localized and asymmetric ways.For each acquisition,3DMM was fit to an RGB frame whereby,from the apex facial action and the neutral frame,the extent of the deformation was computed.The results indicate that the proposed approach can accurately capture face deformation,even localized and asymmetric deformations.Conclusion The proposed framework demonstrated that it is possible to measure deformations of a reconstructed 3D face model to monitor facial actions performed in response to a set of targets.Interestingly,these results were obtained using only RGB targets,without the need for 3D scans captured with costly devices.This paves the way for the use of the proposed tool in remote medical rehabilitation monitoring.展开更多
The independence priori is very often used in the conventional blind source separation （BSS）. Naturally, independent component analysis （ICA） is also employed to perform BSS very often. However, ICA is difficult t...The independence priori is very often used in the conventional blind source separation （BSS）. Naturally, independent component analysis （ICA） is also employed to perform BSS very often. However, ICA is difficult to use in some challenging cases, such as underdetermined BSS or blind separation of dependent sources. Recently, sparse component analysis （SCA） has attained much attention because it is theoretically available for underdetermined BSS and even for blind dependent source separation sometimes. However, SCA has not been developed very sufficiently. Up to now, there are only few existing algorithms and they are also not perfect as well in practice. For example, although Lewicki-Sejnowski＇s natural gradient for SCA is superior to K-mean clustering, it is just an approximation without rigorously theoretical basis. To overcome these problems, a new natural gradient formula is proposed in this paper. This formula is derived directly from the cost function of SCA through matrix theory. Mathematically, it is more rigorous. In addition, a new and robust adaptive BSS algorithm is developed based on the new natural gradient. Simulations illustrate that this natural gradient formula is more robust and reliable than Lewicki-Sejnowski＇s gradient.展开更多
基金Project supported by the National Natural Science Foundation of China
文摘Based on the measurement model of inverse synthetic aperture radar (ISAR) within a small aspect sector,an imaging method was presented with the application of sparse signal processing.This method can form higher resolution inverse synthetic aperture radar images from compensating incomplete measured data,and improves the clarity of the images and makes the feature structure much more clear,which is helpful for target recognition.The simulation results indicate that this method can provide clear ISAR images with high contrast under complex motion case.
基金Project supported by the National Natural Science Foundation of China （Grant No.60472103）, the Shanghai Excellent Academic Leader Project （Grant No.05XP14027）, and the Shanghai Leading Academic Discipline Project （Grant No.T0102）
文摘A contourlet-transform （CT） based sparse independent component analysis for blind image separation is proposed. The images are first decomposed into sets of local features with various degrees of sparsity, and then the intrinsic property is used to select the best （sparsest） subsets of features for further separation. Based on sparse description of the contourlet- transform, the proposed approach is able to yield better performance, including faster convergence and the certain order for the separated signals. Simulation results confirm the validity of the proposed method.
基金Supported by the National Natural Science Foundation of China(No.51204145)Natural Science Foundation of Hebei Province of China(No.2013203300)
文摘Under the underdetermined blind sources separation(UBSS) circumstance,it is difficult to estimate the mixing matrix with high-precision because of unknown sparsity of signals.The mixing matrix estimation is proposed based on linear aggregation degree of signal scatter plot without knowing sparsity,and the linear aggregation degree evaluation of observed signals is presented which obeys generalized Gaussian distribution(GGD).Both the GGD shape parameter and the signals' correlation features affect the observation signals sparsity and further affected the directionality of time-frequency scatter plot.So a new mixing matrix estimation method is proposed for different sparsity degrees,which especially focuses on unclear directionality of scatter plot and weak linear aggregation degree.Firstly,the direction of coefficient scatter plot by time-frequency transform is improved and then the single source coefficients in the case of weak linear clustering is processed finally the improved K-means clustering is applied to achieve the estimation of mixing matrix.The proposed algorithm reduces the requirements of signals sparsity and independence,and the mixing matrix can be estimated with high accuracy.The simulation results show the feasibility and effectiveness of the algorithm.
文摘Background The accurate(quantitative)analysis of 3D face deformation is a problem of increasing interest in many applications.In particular,defining a 3D model of the face deformation into a 2D target image to capture local and asymmetric deformations remains a challenge in existing literature.A measure of such local deformations may be a relevant index for monitoring the rehabilitation exercises of patients suffering from Par-kinson’s or Alzheimer’s disease or those recovering from a stroke.Methods In this paper,a complete framework that allows the construction of a 3D morphable shape model(3DMM)of the face is presented for fitting to a target RGB image.The model has the specific characteristic of being based on localized components of deformation.The fitting transformation is performed from 3D to 2D and guided by the correspondence between landmarks detected in the target image and those manually annotated on the average 3DMM.The fitting also has the distinction of being performed in two steps to disentangle face deformations related to the identity of the target subject from those induced by facial actions.Results The method was experimentally validated using the MICC-3D dataset,which includes 11 subjects.Each subject was imaged in one neutral pose and while performing 18 facial actions that deform the face in localized and asymmetric ways.For each acquisition,3DMM was fit to an RGB frame whereby,from the apex facial action and the neutral frame,the extent of the deformation was computed.The results indicate that the proposed approach can accurately capture face deformation,even localized and asymmetric deformations.Conclusion The proposed framework demonstrated that it is possible to measure deformations of a reconstructed 3D face model to monitor facial actions performed in response to a set of targets.Interestingly,these results were obtained using only RGB targets,without the need for 3D scans captured with costly devices.This paves the way for the use of the proposed tool in remote medical rehabilitation monitoring.
基金the National Natural Science Foundation of China (Grant Nos. 60505005, 60674033, 60774094 and U0635001)Natural Science Fund of Guangdong Province, China (Grant Nos. 05103553 and 05006508)+1 种基金Postdoctoral Science Foundation for Innovation from South China University of TechnologyChina Postdoctoral Science Foundation (Grant No. 20070410237)
文摘The independence priori is very often used in the conventional blind source separation （BSS）. Naturally, independent component analysis （ICA） is also employed to perform BSS very often. However, ICA is difficult to use in some challenging cases, such as underdetermined BSS or blind separation of dependent sources. Recently, sparse component analysis （SCA） has attained much attention because it is theoretically available for underdetermined BSS and even for blind dependent source separation sometimes. However, SCA has not been developed very sufficiently. Up to now, there are only few existing algorithms and they are also not perfect as well in practice. For example, although Lewicki-Sejnowski＇s natural gradient for SCA is superior to K-mean clustering, it is just an approximation without rigorously theoretical basis. To overcome these problems, a new natural gradient formula is proposed in this paper. This formula is derived directly from the cost function of SCA through matrix theory. Mathematically, it is more rigorous. In addition, a new and robust adaptive BSS algorithm is developed based on the new natural gradient. Simulations illustrate that this natural gradient formula is more robust and reliable than Lewicki-Sejnowski＇s gradient.
