To quickly identify the mineral pigments in the Dunhuang murals,a spectral matching algorithm(SMA)based on four methods was combined with laser-induced breakdown spectroscopy(LIBS)for the first *** optimal range of LI...
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To quickly identify the mineral pigments in the Dunhuang murals,a spectral matching algorithm(SMA)based on four methods was combined with laser-induced breakdown spectroscopy(LIBS)for the first *** optimal range of LIBS spectrum for mineral pigments was determined using the similarity value between two different types of samples of the same pigment.A mineral pigment LIBS database was established by comparing the spectral similarities of tablets and simulated samples,and this database was successfully used to identify unknown pigments on tablet,simulated,and real mural debris *** results show that the SMA method coupled with the LIBS technique has great potential for identifying mineral pigments.
Mass spectrometry-based metabolomics is getting mature and playing an ever important role in the systematic understanding of biological process in conjunction with other members of "-omics" family. However, ...
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ISBN:
(纸本)9781424441242
Mass spectrometry-based metabolomics is getting mature and playing an ever important role in the systematic understanding of biological process in conjunction with other members of "-omics" family. However, the identification of metabolites in untargeted metabolomics profiling remains a challenge. In this paper, we propose a support vector machine (SVM)-based spectral matching algorithm to combine multiple similarity measures for accurate identification of metabolites. We compared the performance of this approach with several existing spectral matching algorithms on a spectral library we constructed. The results demonstrate that our proposed method is very promising in identifying metabolites in the face of data heterogeneity caused by different experimental parameters and platforms.
A near infrared spectral database system of apples was studied. A new spectral matching algorithm based on Jaccard similarity coefficient (SMA-JSC) is proposed for higher spectralmatching accuracy. A total of 840 app...
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A near infrared spectral database system of apples was studied. A new spectral matching algorithm based on Jaccard similarity coefficient (SMA-JSC) is proposed for higher spectralmatching accuracy. A total of 840 apples of seven different varieties were used to evaluate the performance of this method. At the same time, a comparison was done among the existing spectral matching algorithms with spectral peak information (SMA-P) and the existing spectral matching algorithms with full spectra (SMA-FS). The highest accuracies of these existing SMA-P and SMA-FS were 48.9% and 72.57%, both of which were quite low mainly because of noise. For SMA-JSC, the first-order derivative was calculated and transformed into binary values (with only 0 or 1) to eliminate the influence of noise. The accuracy of our proposed algorithm was 94.1% for the calibration samples and 94.3% for the validation samples. Also, using linear discriminate analysis (LDA), we compared the existing SMA-P, the existing SMA-FS and our new proposed algorithm. The best result obtained using LDA was 88.0% (with the raw spectra) which was larger than the accuracies of the existing SMA-P and SMA-FS but less than the accuracy of our proposed SMA-JSC. In addition, several important spectral data parameters were optimised for automatic spectral peak detection without manually setting parameters (optimised parameters: spectral resolution was 32 cm(-1), threshold of peak width was 29 spectral data points and threshold of peak shape index was 0.005). A selective spectral smoothing algorithm is also proposed to protect peak bands. With these methods, spectral peaks could be detected 100% correctly without manually setting parameters.
A spectral matching algorithm (SMA) that allows atmospheric correction in the presence of dust aerosols is applied to SeaWiFS imagery in the northwest Mediterranean Sea. The goal is to find criteria that could be used...
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A spectral matching algorithm (SMA) that allows atmospheric correction in the presence of dust aerosols is applied to SeaWiFS imagery in the northwest Mediterranean Sea. The goal is to find criteria that could be used to identify SMA target pixels and to gain insights into the method's accuracy relative to the standard SeaWiFS processing scheme (STD). This work also represents the first validation of SMA using in situ data. The validation dataset includes water-leaving radiances collected from both a fixed buoy site and from a ship during the Advanced Optical Properties Experiment (AOPEX) cruise in August 2004. Supplementary information was provided by the ship LIDAR and coastal AERONET stations in Villefranche (France) and Blida (Algeria) that recorded aerosol conditions near the buoy and proximal to the dust sources. respectively. Backward aerosol transport trajectories were also available for the AERONET sites. allowing identification of potential dust sources, especially for aerosol layers observed by the LIDAR. Over the study period, four aerosol events affected the buoy vicinity, but SMA retrievals proved superior to standard processing results only when dust was dominant, rather than when dust was simply present. The conditions appropriate for an SMA application could be defined using AERONET parameters. They are a combination of high aerosol optical depth tau(a) and low Angstrom exponent alpha (or tau(a)/alpha>0.2). Similar results are obtained using the equivalent SeaWiFS parameters produced by the M method although the threshold value is different Since it is preferable to apply the criterion on a per-pixel basis prior to atmospheric correction to select SMA or STD processing, an analogous test using aerosol model-independent quantities derived from SeaWiFS data is proposed. Thus, SMA and STD processing can be applied to a single image, where appropriate. (C) 2009 Elsevier Inc. All rights reserved.
Target detection of hyperspectral image (HSI) is a research hotspot in the field of remote sensing. It is of particular importance in many domains, especially in military application. Unsupervised target detection is ...
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Target detection of hyperspectral image (HSI) is a research hotspot in the field of remote sensing. It is of particular importance in many domains, especially in military application. Unsupervised target detection is usually more difficult because there is no prior information about target. Traditional algorithms exploit spectral information, only. This study introduces the idea of saliency detection from the visual technique into HSI processing domain and proposes a novel approach named spectral saliency target detection (SSD). It establishes a novel salient model, which utilises both spatial saliency and spectral saliency. In the framework of SSD, it combines the model with spectral matching algorithm to make it perform well even in situations where the target is concealed and small. A HSI set comprised of eight different scenes with complex background is setup to evaluate the performance of the proposed algorithm. The final visible detection results demonstrate that the SSD algorithm outperforms the others. The receiver operation characteristic (ROC) curve and area under the ROC curve are applied to evaluate the results. The proposed algorithm shows superior and stable performance.
This paper presents a new texture-less object recognition algorithm using contour fragment-based features with bisected local regions. We propose a new feature descriptor which is computed on a bisected local region a...
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ISBN:
(纸本)9781479912919
This paper presents a new texture-less object recognition algorithm using contour fragment-based features with bisected local regions. We propose a new feature descriptor which is computed on a bisected local region around each feature point. For each feature point, the local region is divided into two bisected regions according to the tangent line of the contour fragment, and then the proposed feature descriptor is computed on each of the bisected local regions. In our feature matching, we remove spurious potential matches by selecting top-k minimum cost image features for each model node, and the survived potential matches are refined by spectral matching algorithm using pairwise geometric interaction between features.
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