Investigation of defects in Fresco substrates by means of the ECoSp imaging system and the principal component image analysis

2004 ◽  
Vol 213 ◽  
pp. 707-711 ◽  
Author(s):  
C. Bonifazzi ◽  
E. Lodi ◽  
G. Maino ◽  
V. Muzzioli ◽  
L. Nanetti ◽  
...  
Keyword(s):  
Image Analysis ◽  
Imaging System ◽  
Principal Component ◽  
Component Image

scholarly journals Application of Neural Network Algorithm Based on Principal Component Image Analysis in Band Expansion of College English Listening

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Cailing Hao
Keyword(s):  
Neural Network ◽  
Principal Component Analysis ◽  
Image Analysis ◽  
Frequency Band ◽  
High Frequency ◽  
Principal Component ◽  
Speech Signals ◽  
College English ◽  
Neural Network Algorithm ◽  
Component Image

With the development of information technology, band expansion technology is gradually applied to college English listening teaching. This technology aims to recover broadband speech signals from narrowband speech signals with a limited frequency band. However, due to the limitations of current voice equipment and channel conditions, the existing voice band expansion technology often ignores the high-frequency and low-frequency correlation of the audio, resulting in excessive smoothing of the recovered high-frequency spectrum, too dull subjective hearing, and insufficient expression ability. In order to solve this problem, a neural network model PCA-NN (principal components analysis-neural network) based on principal component image analysis is proposed. Based on the nonlinear characteristics of the audio image signal, the model reduces the dimension of high-dimensional data and realizes the effective recovery of the high-frequency detailed spectrum of audio signal in phase space. The results show that the PCA-NN, i.e., neural network based on principal component analysis, is superior to other audio expansion algorithms in subjective and objective evaluation; in log spectrum distortion evaluation, PCA-NN algorithm obtains smaller LSD. Compared with EHBE, Le, and La, the average LSD decreased by 2.286 dB, 0.51 dB, and 0.15 dB, respectively. The above results show that in the image frequency band expansion of college English listening, the neural network algorithm based on principal component analysis (PCA-NN) can obtain better high-frequency reconstruction accuracy and effectively improve the audio quality.

Correlation of Pore Structure and Permeability by SEM-Image Analysis

1990 ◽  
Vol 48 (1) ◽  
pp. 428-429
Author(s):  
C. A. Callender ◽  
Wm. C. Dawson ◽  
J. J. Funk
Keyword(s):  
Image Analysis ◽  
Pore Structure ◽  
Imaging System ◽  
Pore Space ◽  
Simulation Models ◽  
Image Analyzer ◽  
Imaging Data ◽  
Sem Images ◽  
Thin Sections ◽  
Rock Types

The geometric structure of pore space in some carbonate rocks can be correlated with petrophysical measurements by quantitatively analyzing binaries generated from SEM images. Reservoirs with similar porosities can have markedly different permeabilities. Image analysis identifies which characteristics of a rock are responsible for the permeability differences. Imaging data can explain unusual fluid flow patterns which, in turn, can improve production simulation models.Analytical SchemeOur sample suite consists of 30 Middle East carbonates having porosities ranging from 21 to 28% and permeabilities from 92 to 2153 md. Engineering tests reveal the lack of a consistent (predictable) relationship between porosity and permeability (Fig. 1). Finely polished thin sections were studied petrographically to determine rock texture. The studied thin sections represent four petrographically distinct carbonate rock types ranging from compacted, poorly-sorted, dolomitized, intraclastic grainstones to well-sorted, foraminiferal,ooid, peloidal grainstones. The samples were analyzed for pore structure by a Tracor Northern 5500 IPP 5B/80 image analyzer and a 80386 microprocessor-based imaging system. Between 30 and 50 SEM-generated backscattered electron images (frames) were collected per thin section. Binaries were created from the gray level that represents the pore space. Calculated values were averaged and the data analyzed to determine which geological pore structure characteristics actually affect permeability.

scholarly journals Optimized Method for Mapping Inorganic Pigments by Means of Multispectral Imaging Combined with Hyperspectral Spectroscopy for the Study of Vincenzo Pasqualoni’s Wall Painting at the Basilica of S. Nicola in Carcere in Rome

Minerals ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 839
Author(s):  
Lucilla Pronti ◽  
Giuseppe Capobianco ◽  
Margherita Vendittelli ◽  
Anna Candida Felici ◽  
Silvia Serranti ◽  
...  
Keyword(s):  
Multispectral Imaging ◽  
Imaging System ◽  
Principal Component ◽  
Reflectance Spectroscopy ◽  
Wall Painting ◽  
High Spectral Resolution ◽  
Spectral Difference ◽  
Color Palette ◽  
A Minor ◽  
Nir Reflectance Spectroscopy

Multispectral imaging is a preliminary screening technique for the study of paintings. Although it permits the identification of several mineral pigments by their spectral behavior, it is considered less performing concerning hyperspectral imaging, since a limited number of wavelengths are selected. In this work, we propose an optimized method to map the distribution of the mineral pigments used by Vincenzo Pasqualoni for his wall painting placed at the Basilica of S. Nicola in Carcere in Rome, combining UV/VIS/NIR reflectance spectroscopy and multispectral imaging. The first method (UV/VIS/NIR reflectance spectroscopy) allowed us to characterize pigment layers with a high spectral resolution; the second method (UV/VIS/NIR multispectral imaging) permitted the evaluation of the pigment distribution by utilizing a restricted number of wavelengths. Combining the results obtained from both devices was possible to obtain a distribution map of a pictorial layer with a high accuracy level of pigment recognition. The method involved the joint use of point-by-point hyperspectral spectroscopy and Principal Component Analysis (PCA) to identify the pigments in the color palette and evaluate the possibility to discriminate all the pigments recognized, using a minor number of wavelengths acquired through the multispectral imaging system. Finally, the distribution and the spectral difference of the different pigments recognized in the multispectral images, (in this case: red ochre, yellow ochre, orpiment, cobalt blue-based pigments, ultramarine and chrome green) were shown through PCA false-color images.

scholarly journals Simultaneous Feulgen densitometry and autoradiographic grain counting with the Quantimet 720D image-analysis system. III. Improvements in Feulgen densitometry.

1983 ◽  
Vol 31 (10) ◽  
pp. 1224-1232 ◽  
Author(s):  
R J Sklarew
Keyword(s):  
Image Analysis ◽  
Imaging System ◽  
Rat Kidney ◽  
Light Sources ◽  
Kidney Cells ◽  
Image Analysis System ◽  
Replication Error ◽  
Feulgen Densitometry ◽  
Optical Configuration ◽  
Analysis System

A method has been developed for densitometric estimation of the Feulgen-stained DNA content of 3H-labeled nuclei in autoradiographs in conjunction with automated grain counting using a Quantimet Imaging System. Refinements in the methodology are reported which include 1) the incorporation of an Image-Editor Module into the Quantimet module configuration; 2) the optimization of incident illumination based upon evaluation of various light sources; 3) changes in the optical configuration which reduce glare and minimize the level of monitor shading correction; 4) the optimization of scanner sensitivity; and 5) the evaluation of cell-flattening and staining with respect to densitometry resolution and sensitivity. These refinements resulted in a CV of less than 6.4% in the G-1 and G-2 DNA peaks of rat kidney cells in autoradiographs compared to the previous CV of 10.5%, and a G-2 to G-1 ratio of 2.025. For a fixed field position the CV was 5.1% and the replication error less than 1.0%.

Pork Quality Classification Using a Hyperspectral Imaging System and Neural Network

2007 ◽  
Vol 3 (1) ◽  
Author(s):  
Qiao Jun ◽  
Michael Ngadi ◽  
Ning Wang ◽  
Aynur Gunenc ◽  
Mariana Monroy ◽  
...  
Keyword(s):  
Neural Network ◽  
Hyperspectral Imaging ◽  
Imaging System ◽  
Objective Assessment ◽  
Principal Component ◽  
Pork Quality ◽  
Pca Analysis ◽  
Error Frequency ◽  
Quality Classification ◽  
Average Reflectance

Pork quality is usually determined subjectively as PSE, PFN, RFN, RSE and DFD based on color, texture and exudation of the meat. In this study, a hyperspectral-imaging-based technique was developed to achieve rapid, accurate and objective assessment of pork quality. The principal component analysis (PCA) and stepwise operation methods were used to select feature waveband from the entire spectral wavelengths (430 to 980 nm). Then the feature waveband images were extracted at the selected feature wavebands from raw hyperspectral images, and the average reflectance (R) was calculated within the whole loin-eye area. Artificial neural network was used to classify these groups. Results showed that PCA analysis had a better performance than that of stepwise operation for feature waveband images selection. The 1st derivative data gave a better result than that of mean reflectance spectra data. The best classified result was 87.5% correction. The error frequency showed that RSE samples were easier to classify. The PFN and PSE samples were difficult to separate from each other.

scholarly journals Detection and Microscopy of Alnus glutinosa Pollen Fluorescence Peculiarities

Forests ◽  
2019 ◽  
Vol 10 (11) ◽  
pp. 959
Author(s):  
Šaulienė ◽  
Šukienė ◽  
Daunys ◽  
Valiulis ◽  
Lankauskas ◽  
...  
Keyword(s):  
Image Analysis ◽  
Principal Component ◽  
Fluorescence Analysis ◽  
Alnus Glutinosa ◽  
Fluorescence Properties ◽  
Light Spectrum ◽  
Time Data ◽  
Process Data ◽  
Pollen Storage ◽  
Black Alder

Alnus glutinosa is an important woody plant in Lithuanian forest ecosystems. Knowledge of fluorescence properties of black alder pollen is necessary for scientific and practical purposes. By the results of the study, we aimed to evaluate possibilities of identifying Alnus glutinosa pollen fluorescence properties by modeling ozone effect and applying two different fluorescence-based devices. To implement the experiments, black alder pollen was collected in a typical habitat during the annual flowering period in 2018–2019. There were three groups of experimental variants, which differed in the duration of exposure to ozone, conditions of pollen storage before the start of the experiment, and the exposure time. Data for pollen fluorescence analysis were collected using two methods. The microscopy method was used in order to evaluate the possibility of employing image analysis systems for investigation of pollen fluorescence. The second data collection method is related to an automatic device identifying pollen in real time, which uses the fluorescence method in the pollen recognition process. Data were assessed employing image analysis and principal component analysis (PCA) methods. Digital images of ozone-exposed pollen observed under the fluorescence microscope showed the change of the dominant green colour toward the blue spectrum. Meanwhile, the automatic detector detects more pollen whose fluorescence is at the blue light spectrum. It must be noted that assessing pollen fluorescence several months after exposure to ozone, no effect of ozone on fluorescence remains.

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