Two-dimensional Principal Component Analysis Research Articles

A Nuclide Recognition Method for Nuclear Robot System

In the real energy spectrum attenuation environment, many traditional nuclide identification methods for nuclear robot systems have problems such as using only part of the energy spectrum curve, being susceptible to noise, and having low recognition accuracy. Proposes an energy spectrum nuclide recognition method based on S-transform (ST) and Mahalanobis distance-based support vector machine (MSVM). Regarding the energy spectrum curve as a non-stationary signal, combined with the widely used S transformation method in signal transformation, the energy spectrum data is two-dimensional, Then use two-dimensional principal component analysis(2D-PCA) to reduce the dimension of the two-dimensional energy spectrum data for feature extraction, and design a support vector machine (SVM) classifier based on Mahalanobis distance to realize the identification of energy spectrum nuclides. Finally, experiments are carried out with simulated nuclide energy spectrum data based on Geant4. The experimental results show that this method effectively improves the accuracy of energy spectrum nuclide recognition by using full spectrum information. At the same time, experiments are carried out on the nuclide energy spectrum data of different detection distances obtained by the NaI detector in the real environment, and it is verified that the algorithm proposed in this paper also has a good recognition performance for the nuclide energy spectrum collected in the real environment.

Generalized two-dimensional principal component analysis and two artificial neural network models to detect traveling ionospheric disturbances

Generalized two-dimensional principal component analysis and two artificial neural network models to detect traveling ionospheric disturbances

Integration of Two-Dimensional Kernel Principal Component Analysis Plus Two-Dimensional Linear Discriminant Analysis with Convolutional Neural Network for Finger Vein Recognition

High complexity and low recognition rate are two common problems with the current finger vein recognition methods. To solve these problems, this paper integrates two-dimensional kernel principal component analysis (K2DPCA) plus two-dimensional linear discriminant analysis (2DLDA) (K2DPCA+2DLDA) into convolutional neural network (CNN) to recognize finger veins. Considering the row and column correlations of the finger vein image matrix and the classes of finger vein images, the authors adopted K2DPCA and 2DLDA separately for dimensionality reduction and extraction of nonlinear features in row and column directions, producing a dimensionally reduced compressed image without row or column correlation. Taking the dimensionally reduced compressed image as the input, the CNN was introduced to learn higher-level features, making finger vein recognition more accurate and robust. The public dataset of Finger Vein USM (FV-USM) Database was adopted for experimental verification. The results show that the proposed approach effectively overcome the common defects of original image feature extraction: the insufficient feature description, and the redundancy of information. When the training reached 120 epochs, the model basically realized stable convergence, with the loss approaching zero and the recognition rate reaching 97.3%. Compared with two-directional two-dimensional Fisher principal component analysis ((2D)2FPCA), our strategy, which integrates K2DPCA+2DLDA with CNN, achieved a very high recognition rate of finger vein images.

Water Ice Detection Research in Utopia Planitia Based on Simulation of Mars Rover Full-Polarimetric Subsurface Penetrating Radar

The probe of China’s first Mars exploration mission, Tianwen-1, has been successfully launched. It will carry out scientific exploration on the topography, soil characteristics, water ice, climate, ionosphere, and physical fields of Mars. Different from other rovers landing on the moon and Mars, the Zhurong rover is equipped with a full polarimetric subsurface penetrating radar (FP-SPR) system for the first time. The radar’s mission is to depict the shallow subsurface structure of Mars and search for possible water ice. Therefore, in this paper, a 3D realistic structure model is established and numerically simulated based on the possible subsurface structure of Utopia Planitia (the landing area). Influencing factors such as topographical fluctuations, rocks, water ice, and the variation of dielectric constant of different layers are added to the model. The analysis of the acquired FP-SPR data set shows that the two-dimensional principal component analysis (2D-PCA) method can extract effective reflected signals from the radar data with noise interference and improve the data quality. These clearly imaged targets may be water ice blocks, so the application of 2D-PCA to FP-SPR data increases the imaging quality of suspected water ice targets. The results of this paper are the basis for future processing of the measured FP-SPR data on Mars, which will help to identify more details of subsurface structures.

Insights into enzymatic mimicking activity of silver nanoprisms: spectral monitoring and analysis

Peroxidase-like reaction process involving o-phenylenediamine (OPD) and silver nanoprisms in the presence of hydrogen peroxide (H2O2) was monitored using time-resolved ultraviolet–visible (UV–Vis) absorption spectroscopy. The oxidation of OPD and etching of silver nanoprisms were investigated by analyzing the dynamic spectral data. Two-dimensional correlation spectroscopy (2D-COS) and principal component analysis (PCA) were employed to gain insights into the correlation between catalytic oxidation of OPD and etching of silver nanoprisms. It was found that OPD offered significant protection effect for silver nanoprisms so that morphologies of silver nanoprisms maintained at the beginning period after addition of H2O2. Moreover, silver nanoprisms accelerated the oxidation of OPD by H2O2, demonstrating enzymatic mimicking activity of silver nanoprisms. The combination of time-resolved UV–Vis absorption spectroscopy and spectral calculation methods could be used for exploration of complex reaction systems with spectral variations.

Infrared Faces Image Recognition Using Local Binary Pattern

Face recognition has a broad range of uses for business and law enforcement, such as access control, security monitoring, and video surveillance. This paper proposes an effective algorithm for Infrared face recognition using a Local Binary Pattern (LBP) for extraction of features and a Canonical Correlation Analysis (CCA) for fusion and classification of features. The facial characteristics are extracted using LBP. The extracted characteristics are then converted into different domains for transformation. For dimensionality reduction, the two-Dimensional Principal Component Analysis (2DPCA) approach is used in the proposed algorithm to generate more lightweight, robust and discriminatory features, which are then combined using the CCA classifier. The spatial relationship between adjacent pixels is also maintained by 2DPCA, increasing the overall accuracy of recognition. In addition, the paper introduces a comparative study between infrared facial recognition systems using the proposed technique and previous work. Based on the recognition rate and time usage, the output is evaluated. The analysis of the findings shows that the technique proposed is the most efficient and the least time compared to previous techniques. The experimental results are tested on a dataset acquired by Equinox Corporation. The proposed technique achieves a recognition rate of 99.26% at 0.45 seconds.

Diagnosis Using Brain Tumors Two-Dimensional Principal Component Analysis (2D-PCA) with K-nearest Neighbor (KNN) Classification Algorithm

The rapid development of computer technology has brought more and more benefits to human life. Currently, computers can make decisions by imitating the human brain to be used in the health sector to play a role in solving existing problems. One of the technologies used is digital image processing technology on MRI images of brain tumors. Brain tumor images have various variations and large dimensions; therefore, an appropriate method is needed to recognize images maximally. Dimensional reduction uses the Two-Dimensional Principal Component Analysis (2DPCA) method. The classification process uses the K-Nearest Neighbor (KNN) method by calculating the euclidean distance (Euclidean Distance). From 3 tests with the number of data 200 images, the results of the accuracy of the 1st test were 90.0% with 60 test data and 140 training data, the second test was 85.0% with 80 test data and 120 training data, and the 3rd test is worth 83.0% with 100 test data and 100 training data. Based on the research above, it can be concluded that the highest accuracy is obtained in the 1st test, while the lowest accuracy is on the 3rd test. The more amount of training data compared to the test data, the greater the accuracy value obtained. This research is expected to be a reference for further research so that the results obtained are more optimal.

Feature Extraction and Reconstruction of Medical Images using Two-Dimensional Principal Component Analysis

Two-Dimensional Principal Component Analysis (2DPCA) is a classical technique used to reduce the cost of computation than standard PCA. In 2DPCA, images are treated as vectors and appear image as a matrix which is further computed and results as Eigenvalues consisting of lower dimensionality as compared to PCA. 2DPCA depends on the image matrix which is a computationally most efficient method than PCA used to enhance feature extraction speed and high accuracy. 2DPCA is represented as an image matrix, its Co-Variance matrix is computed with the image matrix directly without converting into a 1D vector, and Eigenvectors are obtained for feature extraction. 2DPCA computes an accurate Co-Variance matrix and finds Eigenvectors most efficiently. K-Nearest Neighbor (KNN) algorithm is used for classification. 2DPCA is the best method to obtain reconstruction accuracy than PCA. The main advantage of 2DPCA is less time required for feature extraction and to provide the highest reconstruction accuracy. For testing and evaluating 2DPCA performance, we are conducted several experiments using different databases such as IRMA, WANG, etc., for different medical images and observed that reconstruction accuracy depends on increasing the number of principal components.

An Efficient Human Identification Through Iris Recognition System

As a part of a growing information society, nowadays the issue of security is more crucial than ever. In order to achieve high level of security, the potential of accurately recognize subjects based on their unique measurable physiological or behavioral characteristics has been receiving an increased concern by the research and development community. As biometrics has advanced, iris has been considered a preferred trait because unique pattern texture, lifetime stability, and regular shape contribute to good segmentation and recognition performance. The incredible uniqueness of iris patterns as well as the ability to capture iris images non-invasively has motivated us to develop automated system for iris recognition based on 2-D iris images. The 2DPCA (two-dimensional Principal Component Analysis) and GA (Genetic Algorithm) have been used as feature extraction and feature selection techniques for reducing the dimensionality of iris features without the loss of relevant Information. The Back Propagation Neural Network (BPNN) is implemented using Levenberg–Marquardt’s learning rule for iris recognition. The experimental results illustrated that the 2DPCA-GA achieved a high classification accuracy of 96.40 %.

Fusing structure and color features for cancelable face recognition

Face-based biometric recognition is widely used nowadays, where substantial face images are commonly stored on third-party servers. Since the sensitive information of an individual is contained in facial image such as the age and health condition, it is necessary to protect its privacy and security. This paper investigates a cancelable color face template protection algorithm. To make full use of quaternion representation, the structural information including local variance and gradient is respectively served as the real part. To achieve revocability and ability to redistribute, the strategy of random permutation with binary matrix is adopted. Afterwards, the quaternion-based two-dimensional principal component analysis is employed to extract features. With them, the extreme learning machine can be trained and used for recognition. Experimental results performed on four different color face datasets have demonstrated that the fusion of structural information can greatly improve the accuracy. More importantly, the random permutation not only does not reduce the recognition accuracy, but also guarantees the security and revocation of face template.

Advanced variations of two-dimensional principal component analysis for face recognition

The two-dimensional principal component analysis (2DPCA) has been one of the basic methods of developing artificial intelligent algorithms. To increase the feasibility, we propose a new general ridge regression model for 2DPCA and variations, with extracting low dimensional features under two projection subspaces. A new relaxed 2DPCA under the quaternion framework is proposed to utilize the label (if known) and color information to compute the essential features of generalization ability with optimization algorithms. The 2DPCA-based approaches for face recognition are also improved by weighting each principle component a scatter measure, which increases efficiently the rate of face recognition. In numerical experiments on well-known standard databases, the R2DPCA approach has high generalization ability and achieves a higher recognition rate than the state-of-the-art 2DPCA-like methods, and has better performance than the basic deep learning methods such as CNNs, DBNs, and DNNs in the small-sample case.

Robust 2DPCA by Tℓ₁ Criterion Maximization for Image Recognition

Two-dimensional principal component analysis (2DPCA) has been widely used to extract image features. As opposed to PCA, 2DPCA directly treats 2D matrices to extract image features instead of transforming 2D matrices into vectors. However, the classical 2DPCA based on <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$F$ </tex-math></inline-formula> -norm square is sensitive to noise. To handle this problem, 2DPCAs based on <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\ell _{1}$ </tex-math></inline-formula> -norm, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\ell _{p}$ </tex-math></inline-formula> -norm, and other norms have been studied. In this paper, as a further development, 2DPCA based on <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\text{T}\ell _{1}$ </tex-math></inline-formula> criterion is proposed, referred as 2DPCA- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\text{T}\ell _{1}$ </tex-math></inline-formula> . Notice that, different from some norms used before, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\text{T}\ell _{1}$ </tex-math></inline-formula> criterion is bounded and Lipschitz-continuous. So it can be expected that our 2DPCA- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\text{T}\ell _{1}$ </tex-math></inline-formula> should be more robust. In fact, the experimental results have shown that its performance is superior to that of classical 2DPCA, 2DPCA-L1, 2DPCAL1-S, N-2-DPCA, G2DPCA, and Angle-2DPCA.

Fast Diffusion Kurtosis Mapping of Human Brain at 7 Tesla With Hybrid Principal Component Analyses

Diffusion kurtosis has become an important magnetic resonance imaging (MRI) modality for non-invasively mapping the microstructural variations in living tissues. Theoretically, the spatial resolution of diffusion kurtosis imaging (DKI) can be significantly improved by acquiring data at ultra-high magnetic fields (UHF, ≤ 7 Tesla) because of the increased signal-to-noise ratios. However, issues such as increased susceptibility artefacts and rapid signal attenuation inherent in UHF-MRI have impeded the adoption of DKI in research and clinics. In this paper, we developed a new image reconstruction algorithm for fast DKI at UHF. By integrating the one-dimensional and two-dimensional principal component analysis and compressed sensing technologies, the new algorithm can reconstruct kurtosis maps from highly undersampled data. The technique was validated using randomly undersampled brain images with a control database of fully sampled DKI acquisitions from healthy human participants. We compared the technique with zero-filling Fourier transform and similar compressed sensing algorithms by evaluating the peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) between images and assessing the reproducibility of results using the Bland-Altman method. We found that our methods can achieve at least a five-fold reduction in data acquisition time at UHF with high image quality. Moreover, the PSNR and SSIM of five diffusion metrics generated by our methods were superior to the other algorithms when the undersampling rate is high and the echo time is short. The proposed method can be valuable for fast functional and dynamic-contrast imaging techniques at 7 Tesla or higher.

Two-Dimensional Stockwell Transform and Deep Convolutional Neural Network for Multi-Class Diagnosis of Pathological Brain.

Since the brain lesion detection and classification is a vital diagnosis task, in this paper, the problem of brain magnetic resonance imaging (MRI) classification is investigated. Recent advantages in machine learning and deep learning allows the researchers to develop the robust computer-aided diagnosis (CAD) tools for classification of brain lesions. Feature extraction is an essential step in any machine learning scheme. Time-frequency analysis methods provide localized information that makes them more attractive for image classification applications. Owing to the advantages of two-dimensional discrete orthonormal Stockwell transform (2D DOST), we propose to use it to extract the efficient features from brain MRIs and obtain the feature matrix. Since there are some irrelevant features, two-directional two-dimensional principal component analysis ((2D)2PCA) is used to reduce the dimension of the feature matrix. Finally, convolution neural networks (CNNs) are designed and trained for MRI classification. Simulation results indicate that the proposed CAD tool outperforms the recently introduced ones and can efficiently diagnose the MRI scans.

AN ADOPTION OF 2D-PCA/ICA BASED POST-PROCESSING DIMENSIONALITY REDUCTION ALGORITHM FOR FACIAL RECOGNITION SYSTEM

Face images undergo considerable amount of variations in pose, facial expression and illumination condition. This large variation in facial appearances of the same individual makes most Existing Face Recognition Systems (E-FRS) lack strong discrimination ability and timely inefficient for face representation due to holistic feature extraction technique used. In this paper, a novel face recognition framework, which is an extension of the standard (PCA) and (ICA) denoted as two-dimensional Principal Component Analysis (2D-PCA) and two-dimensional Independent Component Analysis (2D-ICA) respectively is proposed. The choice of 2D was advantageous as image covariance matrix can be constructed directly using original image matrices. The face images used in this study were acquired from the publicly available ORL and AR Face database. The features belonging to similar class were grouped and correlation calculated in the same order. Each technique was decomposed into different components by employing multi-dimensional grouped empirical mode decomposition using Gaussian function. The nearest neighbor (NN) classifier is used for classification. The results of evaluation showed that the 2D-PCA method using ORL database produced RA of 92.5%, PCA produced RA of 75.00%, ICA produced RA of 77.5%, 2D-ICA produced RA of 96.00%. However, 2D-PCA methods using AR database produced RA of 73.56%, PCA produced RA of 62.41%, ICA produced RA of 66.20%, 2D-ICA method produced RA of 77.45%. This study revealed that the developed face recognition framework algorithm achieves an improvement of 18.5% and 11.25% for the ORL and AR databases respectively as against PCA and ICA feature extraction techniques. Keywords: computer vision, dimensionality reduction techniques, face recognition, pattern recognition

A Visual Saliency-Based Approach for Content-Based Image Retrieval

During the past two decades an enormous amount of visual information has been generated; as a result, content-based image retrieval (CBIR) has received considerable attention. In CBIR the image is used as a query to find the most similar images. One of the biggest challenges in CBIR system is to fill up the “semantic gap,” which is the gap between low-level visual features and the high-level semantic concepts of an image. In this paper, the authors have proposed a saliency-based CBIR system that utilizes the semantic information of image and users search intention. In the proposed model, firstly a significant region is identified with the help of method structured matrix decomposition (SMD) using high-level priors that highlight the prominent area of the image. After that, a two-dimensional principal component analysis (2DPCA) is used as a feature, which is compact and effectively used for fast recognition. Experiment results are validated on different image dataset having an extensive collection of semantic classifications.

Robust and fast image hashing with two-dimensional PCA

Image hashing is a useful technology of many multimedia systems, such as image retrieval, image copy detection, multimedia forensics and image authentication. Most of the existing hashing algorithms do not reach a good classification between robustness and discrimination and some hashing algorithms based on dimensionality reduction have high computational cost. To solve these problems, we propose a robust and fast image hashing based on two-dimensional (2D) principal component analysis (PCA) and saliency map. The saliency map determined by a visual attention model called LC (luminance contrast) method can ensure good robustness of our hashing. Since 2D PCA is a fast and efficient technique of dimensionality reduction, the use of 2D PCA helps to learn a compact and discriminative code and provide a fast speed of our hashing. Extensive experiments are carried out to validate the performances of our hashing. Classification comparison shows that our hashing is better than some state-of-the-art algorithms. Computational time comparison illustrates that our hashing outperforms some compared algorithms based on dimensionality reduction.

Forecasting the Yellow River runoff based on functional data analysis methods

This study examines the runoff prediction of each hydrometric station and each month in the mainstream of the Yellow River in China. From the perspective of functional data, the monthly runoff of each hydrometric station can be regarded as a function of both time and space. A sequence of such functions is formed by collecting the data over the years. We propose a new approach by combining the two-dimensional functional principal component analysis (FPCA) and time series analysis methods to predict the runoff. In the simulation, we compared the proposed method with two others: one based on one-dimensional FPCA and the seasonal auto-regressive integrated moving average (SARIMA) method. The method combining standard two-dimensional FPCA and time series analysis outperforms others in most cases, and is used to predict the runoff of each hydrometric station and each month in the Yellow River in 2018.

Removal Characteristics of Effluent Organic Matter (EfOM) in Pharmaceutical Tailwater by a Combined Coagulation and UV/O3 Process

A novel coagulation combined with UV/O3 process was employed to remove the effluent organic matter (EfOM) from a biotreated pharmaceutical wastewater for harmlessness. The removal behavior of EfOM by UV/O3 process was characterized by synchronous fluorescence spectroscopy (SFS) integrating two-dimensional correlation (2D-COS) and principal component analysis (PCA) technology. The highest dissolved organic carbon (DOC) and ratio of UV254 and DOC (SUVA) removal efficiency reached 55.8% and 68.7% by coagulation-UV/O3 process after 60 min oxidation, respectively. Five main components of pharmaceutical tail wastewater (PTW) were identified by SFS. Spectral analysis revealed that UV/O3 was selective for the removal of different fluorescent components, especially fulvic acid-like fluorescent (FLF) component and humus-like fluorescent (HLF) component. Synchronous fluorescence/UV-visible two-dimensional correlation spectra analysis showed that the degradation of organic matter occurred sequentially in the order of HLF, FLF, microbial humus-like fluorescence component (MHLF), tryptophan-like fluorescent component (TRLF), tyrosine-like fluorescent component (TYLF). The UV/O3 process removed 95.6% of HLF, 80.0% of FLF, 56.0% of TRLF, 50.8% of MHLF and 44.4% of TYLF. Therefore, the coagulation-UV/O3 process was proven to be an attractive way to reduce the environmental risks of PTW.

Evaluation of FORMOSAT-2 and PlanetScope Imagery for Aboveground Oil Palm Biomass Estimation in a Mature Plantation in the Congo Basin

The present study developed methods using remote sensing for estimation of total dry aboveground biomass (AGB) of oil palm in the Congo Basin. To achieve this, stem diameters at breast height (DBH, 1.3 m) and stem heights were measured in an oil palm plantation located in Gabon (Congo Basin, Central Africa). These measurements were used to determine AGB in situ. The remote sensing approach that was used to estimate AGB was textural ordination (FOTO) based upon Fourier transforms that were applied, respectively, to PlanetScope and FORMOSAT-2 satellite images taken from the area. The FOTO method is based on the combined use of two-dimensional (2D) Fast Fourier Transform (FFT) and Principal Component Analysis (PCA). In the context of the present study, it was used to characterize the variation in canopy structure and to estimate the aboveground biomass of mature oil palms. Two types of equations linking FOTO indices to in situ biomass were developed: multiple linear regressions (MLR); and multivariate adaptive spline regressions (MARS). All best models developed yielded significant results, regardless of whether they were derived from PlanetScope or from FORMOSAT-2 images. Coefficients of determination (R2) varied between 0.80 and 0.92 (p ≤ 0.0005); and relative root mean-square-errors (%RMSE) were less than 10.12% in all cases. The best model was obtained using MARS approach with FOTO indices from FORMOSAT-2 (%RMSE = 6.09%).