Eigenspace projection methods are widely used for feature extraction from hyperspectral images (HSI) for the classification of land cover. Projection transformation is used to reduce higher ...dimensional feature vectors to lower dimensional vectors for more accurate classification of land cover types. In this paper, a nearest feature line embedding (NFLE) transformation is proposed for the dimension reduction (DR) of an HSI. The NFL measurement is embedded in the transformation during the discriminant analysis phase, instead of the matching phase. Three factors, including class separability, neighborhood structure preservation, and NFL measurement, are considered simultaneously to determine an effective and discriminating transformation in the eigenspaces for land cover classification. Three state-of-the-art classifiers, the nearest-neighbor, support vector machine, and NFL classifiers, were used to classify the reduced features. The proposed NFLE transformation is compared with different feature extraction approaches and evaluated using two benchmark data sets, the MASTER set at Au-Ku and the AVIRIS set at Northwest Tippecanoe County. The experimental results demonstrate that the NFLE approach is effective for DR in land cover classification in the field of Earth remote sensing.
Recently proposed dynamic magnetic resonance (MR) inverse imaging (InI) is a novel parallel imaging reconstruction technique capable of improving the temporal resolution of blood–oxygen ...level-dependent (BOLD) contrast functional MRI (fMRI) to the order of milliseconds at the cost of moderate spatial resolution. Volumetric InI reconstructs spatial information from projection data by solving ill-posed inverse problems using simultaneous acquisitions from a RF coil array. Previously a spatial filtering technique based on linearly constrained minimum variance (LCMV) beamformer was suggested to localize the hemodynamic changes of dynamic InI data with improved spatial resolution and sensitivity. Here we report an advancement of the spatial filtering method, which combines the eigenspace projection of the measured data and the L1-norm minimization of the spatial filters' output noise amplitude, to further improve the detection power of BOLD contrast fMRI data. Using numerical simulation and in vivo data, we demonstrate that this eigenspace linearly constrained minimum amplitude (eLCMA) beamformer can reconstruct spatiotemporal hemodynamic signals with high statistical significance values and high spatial resolution in event-related two-choice reaction time visuomotor experiments.
► MR inverse imaging (InI) achieves TR=100ms with reduced spatial resolution. ► Spatial filtering with minimized output noise amplitude applies to InI. ► eLCMA combines eigenspace projection and L1-norm minimization. ► eLCMA reconstructions have higher statistical significance and spatial resolution.
In this paper, the broadband multipath signal reception problem is addressed and a space-time beamforming technique is presented to fully utilize the energy of multipath signals. First, we apply ...adaptive beamspace-based beamformer to cancel out uncorrelated interferences. Then its weights are utilized to estimate the channel response of desired signal, with which an eigenspace-based space-time beamformer is constructed for exploiting multipath energy. Theoretical analysis demonstrates the proposed technique can maximize the generalized signal-to-interference-plus-noise ratio of beamformer output. Simulation results are provided to illustrate its effectiveness.
In this paper one investigation has been done to find the optimum level of fusion to find a fused image from visual as well as thermal images. Because of the use of face recognition system in ...critical areas like, authenticating an authorized person in highly secured areas, investigation of criminals, online monitoring etc, face recognition system should be very robust and accurate one. This work is an attempt to fuse visual and thermal face images at optimum level to extract the advantages of visual as well as thermal images. In our work, Object Tracking and Classification Beyond Visible Spectrum (OTCBVS) database has been used for the visual and thermal images. Among all the experiments a maximum recognition result obtained is 93%.
The quaternion-valued robust adaptive beamforming (QRAB) problem with electromagnetic vector-sensor arrays is investigated based on the widely linear processing (WLP) model, which can fully exploit ...the second-order statistics of array quaternionic outputs to guarantee a versatile ability to tackle the steering vector mismatch problem in the context of both proper and improper signals. In detail, two QRAB algorithms are presented by adopting the well-known criterions of worst-case performance optimization and principal eigenspace projection. The former one formulates the augmented steering vector as belonging to a quaternion-valued uncertainty set and then involves a constrained optimization problem, which can be transformed into a solvable real-valued convex form; while the latter one just needs to apply the quaternionic eigenvalue decomposition (QEVD) to the augmented covariance matrix with reduced computational complexity. Simulation results verify the effectiveness of the proposed schemes and show their superior performance as compared to the conventional QRAB schemes.
This paper presents a novel approach to handle the challenges of face recognition. In this work thermal face images are considered, which minimizes the affect of illumination changes and occlusion ...due to moustache, beards, adornments etc. The proposed approach registers the training and testing thermal face images in polar coordinate, which is capable to handle complicacies introduced by scaling and rotation. Polar images are projected into eigenspace and finally classified using a multi-layer perceptron. In the experiments we have used Object Tracking and Classification Beyond Visible Spectrum (OTCBVS) database benchmark thermal face images. Experimental results show that the proposed approach significantly improves the verification and identification performance and the success rate is 97.05%.
Here an efficient fusion technique for automatic face recognition has been presented. Fusion of visual and thermal images has been done to take the advantages of thermal images as well as visual ...images. By employing fusion a new image can be obtained, which provides the most detailed, reliable, and discriminating information. In this method fused images are generated using visual and thermal face images in the first step. In the second step, fused images are projected into eigenspace and finally classified using a radial basis function neural network. In the experiments Object Tracking and Classification Beyond Visible Spectrum (OTCBVS) database benchmark for thermal and visual face images have been used. Experimental results show that the proposed approach performs well in recognizing unknown individuals with a maximum success rate of 96%.
This study deals with vibration-based damage detection in a truss bridge model and suggests a novel methodology based on fuzzy clustering and measured frequency response function (FRF) data reduced ...by principal component projection. A six-bay truss bridge model is designed and fabricated in laboratory, various connection damages are simulated by loosening the end connecter bolts, and the environmental effects are taken into account by changing in excitation force levels of a shaker. The FRFs of the healthy and the damaged structure are used as initial data. The FRF data normalization is performed for eliminating the effects caused by the environmental and operational variability. Two data projection algorithms, namely principal component analysis (PCA) and kernel principal component analysis (KPCA) are adopted for data compression and the median values of principal components are defined for damage feature extraction. The fuzzy c-means (FCM) clustering algorithm is used to categorize these features for structural damage detection. The illustrated results show that the proposed method can effectively identify the bridge damages simulated by loosening the bolted joints of the truss bridge structure. It is sensitive to the structural damage but it is non-sensitive to the effect of the environmental and operational variations. This makes it quite generic and permits its potential development for real and complex truss bridges in site.
In this paper, we show how inexact graph matching (that is, the correspondence between sets of vertices of pairs of graphs) can be solved using the renormalization of projections of the vertices (as ...defined in this case by their connectivities) into the joint eigenspace of a pair of graphs and a form of relational clustering. An important feature of this eigenspace renormalization projection clustering (EPC) method is its ability to match graphs with different number of vertices. Shock graph-based shape matching is used to illustrate the model and a more objective method for evaluating the approach using random graphs is explored with encouraging results.
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