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Quaternion-Based Multiscale Analysis for Feature Extraction of Hyperspectral Images
摘要： This paper proposes a new method called multiscale quaternion Weber local descriptor histogram (MQWLDH) for feature extraction of hyperspectral images (HSIs), which is used to model spatial information based on the corresponding spectral features. The proposed method first transforms spectral data into an orthogonal space using principal component analysis, and extracts the first three principal components (PCs) based on the maximum variance theory. Then construct the MQWLDH to extract spatial features based on those first three PCs. The proposed method uses the algebraic structure of quaternions to unify the process of processing the first three PCs, which reduces the computational cost and the dimensionality of the extracted spatial feature vector. Moreover, the constructed quaternion Weber local descriptor effectively characterizes the variations of each pixel neighborhood and detects the edges of HSIs. To capture more intrinsic spatial information contained in homogeneous regions of different sizes and shapes, multiscale feature histograms are constructed. Finally, a feature fusion framework is proposed to fuse spectral and spatial features, so that spectral information can be fully utilized. The experimental results on three HSI data sets demonstrate that the proposed method provides effective features to different classifiers and achieves excellent classification performance.
关键词： multiscale feature histograms.,principal component analysis (PCA),Feature extraction,quaternion Weber local descriptor (QWLD)
更新于2025-09-23 15:22:29
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Low-light Image Enhancement by Principal Component Analysis
摘要： Under extreme low-lighting conditions, images have low contrast, low brightness, and high noise. In this paper, we propose a principal component analysis framework to enhance low-light-level images with decomposed luminance–chrominance components. A multi-scale retinex-based adaptive filter is developed for the luminance component to enhance contrast and brightness significantly. Noise is attenuated by a proposed collaborative filtering employed to both the luminance and chrominance components that reveal every finest detail by preserving the unique features in the image. To evaluate the effectiveness of the proposed algorithm, a simulation model is proposed to generate nighttime images for various levels of contrast and noise. The proposed algorithm can process a wide range of images without introducing ghosting and halo artifacts. The quantitative performance of the algorithm is measured in terms of both full-reference and blind performance metrics. It shows that the proposed method delivers state-of-the-art performance both in terms of objective criteria and visual quality compared to the existing methods.
关键词： denoising,Contrast enhancement,principal component analysis
更新于2025-09-23 15:22:29
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Front-face and right-angle fluorescence spectroscopy for monitoring extra virgin olive oil spectrum evolution
摘要： Fluorescence spectroscopic techniques were applied in a study in order to monitor the evolution of the spectral pattern of extra virgin olive oil (EVOO) samples exposed to indirect light at room temperature. Detailed information was extracted from three-dimensional fluorescence spectra with excitation wavelengths ranging from 230 to 646 nm and emission wavelengths ranging from 250 to 698.5 nm. Relevant emission regions were revealed in FF experiments that were useful to study the variability of the characteristic spectral patterns and to develop fast inspection procedures. Such procedures include simultaneous excitation at wavelengths lower than 400 nm, which was proposed and implemented in a right-angle prototype in order to monitor the evolution of EVOO samples exposed to indirect light. Emission signals at local maxima around 400, 434, 464, 513 and 674 nm were found to be relevant. Ongoing research highlights that hyperspectral images provide spectral patterns of the olives, allowing more precise sorting into categories, which would enable the classification into lots of oils with more homogeneous characteristics for subsequent modelling.
关键词： principal component analysis,oil quality,oxidation,oil storage
更新于2025-09-23 15:22:29
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Stroke diagnosis from retinal fundus images using multi texture analysis
摘要： Stroke is a cerebrovascular disease which is one of the significant causes of adult impairment. Research shows that retinal fundus images carry vital information for the prediction of various cardiovascular diseases like Stroke. This work investigates a multi-texture description for the computer aided diagnosis of Stroke from retinal fundus images. Texture of the retinal background is analyzed, thereby eliminating the need for segmentation. Gabor Filter (GF), Local Binary Pattern (LBP) and Histogram of Oriented gradients (HOG) are the texture descriptors implemented in this work. The texture descriptors are applied to the second Eigen channel obtained by Principal Component Analysis (PCA). Extracted features are concatenated to form a multi-texture representation and dimensionality reduction is done by ReliefF feature selection method. The compact feature vector is given to Na?ve Bayes classifier and performance metrics are evaluated. We have evaluated the performance of individual feature descriptors and multiple feature descriptors in retinal fundus images for stroke diagnosis. Multi-texture description outperforms individual texture descriptors by an accuracy of 95.1 %.
关键词： Gabor filter,ReliefF,histogram of oriented gradients,principal component analysis,local binary pattern,Stroke
更新于2025-09-23 15:22:29
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Identification of Maize Kernel Vigor under Different Accelerated Aging Times Using Hyperspectral Imaging
摘要： Seed aging during storage is irreversible, and a rapid, accurate detection method for seed vigor detection during seed aging is of great importance for seed companies and farmers. In this study, an artificial accelerated aging treatment was used to simulate the maize kernel aging process, and hyperspectral imaging at the spectral range of 874–1734 nm was applied as a rapid and accurate technique to identify seed vigor under different accelerated aging time regimes. Hyperspectral images of two varieties of maize processed with eight different aging duration times (0, 12, 24, 36, 48, 72, 96 and 120 h) were acquired. Principal component analysis (PCA) was used to conduct a qualitative analysis on maize kernels under different accelerated aging time conditions. Second-order derivatization was applied to select characteristic wavelengths. Classification models (support vector machine?SVM) based on full spectra and optimal wavelengths were built. The results showed that misclassification in unprocessed maize kernels was rare, while some misclassification occurred in maize kernels after the short aging times of 12 and 24 h. On the whole, classification accuracies of maize kernels after relatively short aging times (0, 12 and 24 h) were higher, ranging from 61% to 100%. Maize kernels with longer aging time (36, 48, 72, 96, 120 h) had lower classification accuracies. According to the results of confusion matrixes of SVM models, the eight categories of each maize variety could be divided into three groups: Group 1 (0 h), Group 2 (12 and 24 h) and Group 3 (36, 48, 72, 96, 120 h). Maize kernels from different categories within one group were more likely to be misclassified with each other, and maize kernels within different groups had fewer misclassified samples. Germination test was conducted to verify the classification models, the results showed that the significant differences of maize kernel vigor revealed by standard germination tests generally matched with the classification accuracies of the SVM models. Hyperspectral imaging analysis for two varieties of maize kernels showed similar results, indicating the possibility of using hyperspectral imaging technique combined with chemometric methods to evaluate seed vigor and seed aging degree.
关键词： hyperspectral imaging technology,standard germination tests,support vector machine model,accelerated aging,principal component analysis,maize kernel
更新于2025-09-23 15:22:29
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A Skeleton-Based Hierarchical Method for Detecting 3-D Pole-Like Objects From Mobile LiDAR Point Clouds
摘要： The pole-like object detection is of signi?cance for robot navigation, autonomous driving, road infrastructure inventory, and detailed 3-D map generation. In this letter, we develop a skeleton-based hierarchical method for automatic detection of pole-like objects from mobile LiDAR point clouds. First, coarse extraction of building facades is adopted for the occlusion analysis. Second, slice-based Euclidean clustering algorithm is implemented to derive a set of pole-like object candidates. Third, skeleton-based principal component analysis shape recognition is presented to robustly locate all possible positions of pole-like objects. Finally, a Voronoi-constrained vertical region growing algorithm is proposed to adaptively producing the individual pole-like objects. Experiments were conducted on the public Paris–Lille-3-D data set. Experimental results demonstrate that the proposed method is robust and ef?cient for extracting the pole-like objects, with average quality of 90.43%. Furthermore, the proposed method outperforms other existing methods, especially for detecting pole-like objects with a large radius.
关键词： pole-like object extraction,Voronoi tessellation,Laplacian smoothing,principal component analysis (PCA)
更新于2025-09-23 15:22:29
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Simultaneous detection of different probe molecules using silver nanowires as SERS substrates
摘要： Metallic silver nanowires with high yield were synthesized using a modified seed mediated approach at room temperature. Ribbon-like nanostructures were obtained when the concentration of NaOH was lower and further increase of NaOH transformed it into long nanowires. These nanowires possess high aspect ratio, with length and diameter ~ 6.5 μm and 17 nm respectively. The surface enhanced Raman scattering activity of these nanowires was tested with three different probe molecules viz., crystal violet, malachite green and nile blue chloride using visible (514.4 nm) and near-infrared (784.8 nm) excitation lines. The minimum detection limits for crystal violet and nile blue chloride molecules were found to be down to 10-7 M with good linear responses, as evidenced by values of correlation coefficients, indicating their potential for a variety of applications such as sensing. Principal component analysis was performed with the surface enhanced Raman spectra in order to discriminate the dye molecules and their mixture, simultaneously. The first two principal components, which provided 69.80 and 27.93% of the total data variance, could be conveniently represented as a two dimensional PCA score plot. The score plot showed clear clustering of probe molecules and their mixture. The relative contribution of wavenumbers to each of the two principal components was identified by plotting the PCA loading matrix. These results further promote possibilities of quantification of multiplexed SERS detection and analysis.
关键词： Surface enhanced Raman Scattering,Loading Plot.,Principal component analysis,Score plot,Silver nanowires
更新于2025-09-23 15:22:29
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Dual-wavelength Raman fusion spectroscopy
摘要： Spatially compressed dual-wavelength Raman spectroscopy allows recording the full Raman spectrum using a detection system with limited spectral range. The common approach is to record the spectra with the two excitation lasers consecutively and then concatenate the full spectrum. However, with this approach quantitative analysis for process monitoring is not possible as the investigated object may change between the two acquisitions. In this Note, spectral fusion is proposed as a concept to overcome this problem. The sample is illuminated by the two lasers simultaneously hence leading to an on-chip fusion of the different parts of the Raman spectrum. It is shown that the resulting data are suitable for quantitative evaluation using univariate and multivariate methods. Dual-wavelength Raman fusion spectroscopy offers new opportunities for building highly compact devices for analytical chemistry.
关键词： Raman spectroscopy,principal component analysis,data processing
更新于2025-09-23 15:22:29
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A Label-free Platform for Identification of Exosomes from Different Sources
摘要： Exosomes contain cell- and cell-state-specific cargos of proteins, lipids, and nucleic acids and play significant roles in cell signaling and cell–cell communication. Current research into exosome-based biomarkers has relied largely on analyzing candidate biomarkers, i.e., specific proteins or nucleic acids. However, this approach may miss important biomarkers that are yet to be identified. Alternative approaches are to analyze the entire exosome system, either by “omics” methods or by techniques that provide “fingerprints” of the system without identifying each individual biomolecule component. Here, we describe a platform of the latter type, which is based on surface-enhanced Raman spectroscopy (SERS) in combination with multivariate analysis, and demonstrate the utility of this platform for analyzing exosomes derived from different biological sources. First, we examined whether this analysis could use exosomes isolated from fetal bovine serum using a simple, commercially available isolation kit or necessitates the higher purity achieved by the “gold standard” ultracentrifugation/filtration procedure. Our data demonstrate that the latter method is required for this type of analysis. Having established this requirement, we rigorously analyzed the Raman spectral signature of individual exosomes using a unique, hybrid SERS substrate made of a graphene-covered Au surface containing quasi-periodic array of pyramids. To examine the source of the Raman signal, we used Raman mapping of low and high spatial resolution combined with morphological identification of exosomes by scanning electron microscopy. Both approaches suggested that the spectra were collected from single exosomes. Finally, we demonstrate for the first time that our platform can distinguish among exosomes from different biological sources based on their Raman signature, a promising approach for developing exosome-based fingerprinting. Our study serves as a solid technological foundation for future exploration of the roles of exosomes in various biological processes and their use as biomarkers for disease diagnosis and treatment monitoring.
关键词： surface-enhanced Raman spectroscopy,graphene,biomarker,exosome,principal component analysis
更新于2025-09-23 15:22:29
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Hyperspectral signature analysis using neural network for grade estimation of copper ore
摘要： The ever-increasing demand for the different metal and mineral resources from the earth’s subsurface has brought tremendous pressure on the geochemical laboratory for the growing countries. The success of any mining industry relies on the estimated values of ore grade in the mineral deposit. Hence, rapid assessment of ore grade is critical in daily schedule in mines operations. Commonly the assay value is determined by chemical analysis or X-Ray Fluorescence (XRF), which is one of the constrained by real-time grade estimation, duration of sample preparation and processing. Several researches carried out in exploration and revealed that hyperspectral technique is a promising tool for mineral identification and mapping. The goal of the present study is to determine the effectiveness of narrow band spectroscopy in Cu grade estimation. To achieve this, a multilayer feed-forward neural network model has been developed to establish a functional link between hyperspectral signature derived features with the copper grade. Altogether eight different types of features including absorption depth, band depth center, the area under the absorption curve, full width at half maxima were extracted from continuum removed spectra along with derivative reflectance features, e.g. band depth ratio, 1st and 2nd slopes from the hyperspectral profile. The dimensionality was reduced by applying Principal Component Analysis onto the extracted features. The first seven PCAs are then used as input vector of the ANN model. A five-fold cross-validation exercise is carried out for model performance. The high degree of correlation reveals that the PCA generated feature from hyperspectral data coupled with ANN model could be an alternative approach to predict the copper grade for the copper mine.
关键词： copper grade,ore grade estimation,spectral feature,K-Fold cross validation,principal component analysis,artificial neural network
更新于2025-09-23 15:22:29