Infrared Image Analysis Research Articles

Computer Vision and Image Analysis of Art 2022 Conference Overview and Papers Program

This conference on computer image analysis in the study of art presents leading research in the application of image analysis, computer vision, and pattern recognition to problems of interest to art historians, curators and conservators. A number of recent questions and controversies have highlighted the value of rigorous image analysis in the service of the analysis of art, particularly painting. Consider these examples: the fractal image analysis for the authentication of drip paintings possibly by Jackson Pollock; sophisticated perspective, shading and form analysis to address claims that early Renaissance masters such as Jan van Eyck or Baroque masters such as Georges de la Tour traced optically projected images; automatic multi-scale analysis of brushstrokes for the attribution of portraits within a painting by Perugino; and multi-spectral, x-ray and infra-red scanning and image analysis of the Mona Lisa to reveal the painting techniques of Leonardo. The value of image analysis to these and other questions strongly suggests that current and future computer methods will play an ever larger role in the scholarship of visual arts.

Evaluation of the Anti-transpirant Activity of ABA Receptor Agonists in Monocot and Eudicot Plants.

ABA receptor agonists capable of improving plant performance under drought conditions have been described during the last years. However, monocot and eudicot plant species respond differently to various agonists. Here, we provide a detailed methodology to evaluate the anti-transpirant activity of ABA receptor agonists in both monocot and eudicot plant species using infrared imaging and image data analysis. Plant growth conditions, chemical application, and infrared image analysis are explained in detail to evaluate the anti-transpirant activity of ABA receptor agonists in the eudicot model Arabidopsis thaliana and in the C4-monocot model Setaria viridis.

Fourier Transform Infrared Imaging Analysis of Interactions Between Polypropylene Grafted with Maleic Anhydride and Silica Spheres Using Two-Trace Two-Dimensional Correlation Mapping.

A technique for analyzing infrared imaging data based on two-trace two-dimensional (2T2D) correlation analysis is presented to extract pertinent information underlying spectroscopic imaging data. In 2T2D correlation mapping, each spectrum in hyperspectral data is individually compared with a reference spectrum to generate 2T2D asynchronous correlation intensity at the x- and y-coordinates on a 2T2D correlation map. Asynchronous correlation intensity develops only when the signal contribution from a certain species becomes even more significant in the sample spectrum compared with the reference spectrum. This feature can be advantageously utilized to examine molecular interaction or an intermediate form of the component present in a system of interest. 2T2D correlation mapping is examined using Fourier transform infrared imaging data of polymer composites based on polypropylene grafted with maleic anhydride melt-mixed with silica spheres. Infrared images derived by using conventional visualization based on a single wavenumber (i.e., 1713 cm-1) are dominated with the overwhelming infrared absorbance induced by the normal maleic anhydride species, making the identification of subtle but pertinent changes in the composite system difficult. A 2T2D correlation map derived from the maleic anhydride/silica spheres composite developed a significant asynchronous correlation intensity between the infrared bands at 1695 and 1713 cm-1 around a specific region on the map where the maleic anhydride and silica spheres coexist. On the other hand, such a correlation pattern becomes less acute when the silica spheres is modified with the octadecyldimethyl group to prevent the hydrogen bonding with the maleic anhydride. It thus revealed that the silanol groups on the surface of the silica spheres substantially interact with the maleic anhydride via the development of the hydrogen bonding.

Synergistic enhancement of thermal conductivity by addition of graphene nanoplatelets to three‐dimensional boron nitride scaffolds for polyamide 6 composites

AbstractThree‐dimensional boron nitride/graphene nanoplatelets (3D‐BN‐GNP) scaffolds were fabricated using an ice‐templating method and polyamide 6 (PA6)‐based composites were prepared by vacuum impregnation of caprolactam monomers into the scaffolds, followed by polymerization. The BN sheets in the PA6/3D‐BN and PA6/3D‐BN‐GNP composites display a predominant parallel alignment along the ice‐crystal formation constructing thermally conductive paths. The addition of few GNPs assists the dispersion of BN sheets in the PA6/3D‐BN‐GNP composites and repair the broken thermal paths caused by local agglomeration of the BN sheets. Consequently, GNPs play a morphology‐promoted synergistic role in the enhancement of the thermal conductivity of the PA6/3D‐BN‐GNP composites. The PA6/3D‐BN‐GNP composite prepared with 23.40 wt% BN sheets and 2.60 wt% GNPs exhibits the highest thermal conductivity of 2.80 W m−1 K−1, which is 833% and 33% higher than the values recorded for the pure PA6 and the PA6/3D‐BN composite at BN loading of 26.18 wt%, respectively. Infrared imaging analysis revealed that the surface of the PA6/3D‐BN‐GNP composite has a fast response to heating and cooling, suggesting the potential of the composites in thermal management applications.

Quantitative infrared image analysis of simultaneous upstream and downstream microgravity flame spread over thermally thin cellulose fuel in low speed forced flow

Quantitative image analysis of infrared (IR) measurements are compared to visible color images for simultaneous upstream and downstream flame spread over thermally thin cellulose samples in low speed forced flow in microgravity. These results are a unique set of data that provide a wealth of information to guide future modeling efforts. Test conditions at ambient pressure were conducted in 21–50% O2 by volume (N2 balance) and forced flow velocities from 2 cm/s to 20 cm/s. The tests conditions encompass strictly upstream flame spread to simultaneous upstream and downstream flame spread. Steady upstream flame spread is observed while the downstream flame only begins to spread at the highest flow velocities due to the oxygen shadow cast by the upstream flame (oxygen depleted region downstream of the flame). Blackbody surface temperature gradients for the upstream flame are an order of magnitude larger than the downstream gradients. Upstream preheat lengths decrease with flow while downstream preheat lengths increase. Downstream pyrolysis lengths reach steady state for most cases and increase with convective heating while upstream pyrolysis lengths increase with time. Surface thermocouple (TC) histories were non-dimensionalized to obtain a characteristic surface temperature profile using new scaling analyses. Applying the ellipticity of the leading edge to the scaling reveals the local flow velocity at the leading edge is on the order of diffusive velocities and the flame thermal expansion acts as a significant barrier to divert the flow. A surface energy balance reveals that the peak heat flux for both upstream and downstream flames increases with oxygen concentration and forced flow velocity. The upstream flame heat flux is equally divided between fuel heat up and surface radiation. The downstream heat flux goes almost exclusively to surface radiation. Comparisons of these results to previous applicable research are provided throughout the text and generally agree well.

Вычисление коэффициента ускорения испытаний на безотказность на основе тепловизионного анализа кристалла сверхбольших интегральных схем

A new approach for evaluating the acceleration factor of forced reliability tests of very large scale integrated circuits (VLSI) is presented. The approach is based on subjecting the VLSI chip to an infrared image analysis. Currently, the VLSI reliability testing acceleration factor is evaluated based on the Arrhenius law, according to which this factor depends on the chip temperature. The chip temperature, in turn, is represented by the sum of the chip package temperature and the product of the maximum dissipated power and the chip-to-package thermal resistance. The drawback of the existing method is that the calculation is carried out for only a single chip temperature value that was obtained analytically. But the VLSI is a complex system, and it is not correct to judge about the testing acceleration factor proceeding from a single chip temperature value. It is proposed to calculate the VLSI reliability testing acceleration factor based on the temperatures at many points on the VLSI chip surface. This will make it possible to take into account the test sequence influence on the temperature distribution over the chip surface, thereby helping select the test sequences so that to obtain the maximal and uniform chip heating. Owing to the proposed method, it becomes possible to evaluate the testing acceleration factor more accurately and also to potentially increase it by choosing the test sequence. A more accurate evaluation of the acceleration factor allows the reliability tests reliability to be improved. The proposed method for evaluating the acceleration factor was validated experimentally. The workplace is described, the calculations of the reliability testing acceleration factors using two approaches are carried out, and their comparison is given.

Hyperspectral Shortwave Infrared Image Analysis for Detection of Adulterants in Almond Powder with One-Class Classification Method.

The widely used techniques for analyzing the quality of powdered food products focus on targeted detection with a low-throughput screening of samples. Owing to potentially significant health threats and large-scale adulterations, food regulatory agencies and industries require rapid and non-destructive analytical techniques for the detection of unexpected compounds present in products. Accordingly, shortwave-infrared hyperspectral imaging (SWIR-HSI) for high throughput authenticity analysis of almond powder was investigated in this study. Two different varieties of almond powder, adulterated with apricot and peanut powder at different concentrations, were imaged using the SWIR-HSI system. A one-class classifier technique, known as data-driven soft independent modeling of class analogy (DD-SIMCA), was used on collected data sets of pure and adulterated samples. A partial least square regression (PLSR) model was further developed to predict adulterant concentrations in almond powder. Classification results from DD-SIMCA yielded 100% sensitivity and 89–100% specificity for different validation sets of adulterated samples. The results obtained from the PLSR analysis yielded a high determination coefficient (R2) and low error values (<1%) for each variety of almond powder adulterated with apricot; however, a relatively higher error rates of 2.5% and 4.4% for the two varieties of almond powder adulterated with peanut powder, which indicates the performance of quantitative analysis model could vary with sample condition, such as variety, originality, etc. PLSR-based concentration mapped images visually characterized the adulterant (apricot) concentration in the almond powder. These results demonstrate that the SWIR-HSI technique combined with the one-class classifier DD-SIMCA can be used effectively for a high-throughput quality screening of almond powder regarding potential adulteration.

Substation Equipment Fault Identification Based on Infrared Image Analysis

As a key part of the power system, the substations undertake important work. With the development and construction of smart grids, the status data, image monitoring data, and environmental meteorological data of power systems are gradually being integrated and shared on a unified platform. Traditional models based on theoretical analysis are difficult to deal with multi-dimensional, massive data set information. Under this background, starting from the inherent law of the data itself, the use of machine learning methods combined with the infrared image data of the equipment can achieve intelligent identification and early warning of substation equipment failures. This paper first uses the convolutional neural network to identify the substation equipment in the picture, and then combines the infrared image to perform image registration. Finally, the deep belief network is used to determine whether the device is in wrong condition. The overall substation equipment fault identification is tested on real data, and the experimental results show that the proposed method has high accuracy.

2D fourier transform for global analysis and classification of meibomian gland images

In recent years, significant progress has been made in the Meibography technique resulting from the use of advanced image analysis methods allowing a quantitative description of the Meibomian gland structures. Many objective measures of gland distortion were previously proposed allowing for user-independent classification of acquired gland images. However, due to the complicated nature of gland deformation, none of the single-valued parameters can fully describe the analyzed gland images. There is a need to increase the number of descriptive factors, selectively sensitive to different gland features. Here we show that global 2D Fourier transform analysis of infra-red gland images provides values of two new such parameters: mean gland frequency and anisotropy in gland periodicity. We show that their values correlate with gland dysfunction and can be used to automatically categorize the images into the three subjective classes (healthy, intermediate and unhealthy). We also demonstrated that classification performance can be improved by dimensionality reduction approach using principal component analysis.

Distribution of root system of hop plants in hop gardens with regular rows cultivation

Spatial distribution of the root system of hop (Humulus lupulus L.) in the soil profile is a less explored issue. However, it is known that it can play an important role in the development of new tillage technologies, fertilisation and irrigation, including the use of precision farming principles. In the period from 2015 to 2018, the distribution of the hop root system was evaluated on twelve hop plants of five Czech hop cultivars. The age of the plants ranged from 3 to 15 years. The evaluation took place in the Saaz region at regularly cultivated hop gardens. As part of the evaluation, the root systems of hop plants were removed from the soil profile and subsequently spatially reconstructed. With the help of infrared image analysis, the root intensity in the soil profile and the morphology of the root systems were determined. The root depth of the plants ranged from 1 m to 2.25 m. The lateral width of the hop root system ranged from 0.6 m to 1.5 m. As a result of the rows cultivation, the lateral development of the roots in the upper soil layers was limited. The results were confirmed using the infrared image analysis method to specify the distribution of the root system and the root density of hop plants in the soil profile.

R22 ve alternatifleri R438A ile R417A soğutucu akışkanları için kızılötesi görüntü işleme teknikleri kullanarak, soğutma sistem performansının incelenmesi

Today, it has been observed that with various protocols, halogenic chlorofluorocarbon (CFC) and hydrochlorofluorocarbon (HCFC) refrigerants will not be used in the future and various alternatives must be created. In the conventional cooling efficiency coefficient (COP) calculation method, thermodynamic property tables and pressure-enthalpy diagrams are used. Thanks to the proposed method, it will not be necessary to use these tables and diagrams when calculating the COP. It will be sufficient to take images from the regions indicated by the infrared imaging system. In this study, performance analysis of commercial refrigeration systems of R417A and R438A fluids that are alternative to R22 fluid, which are friendly to the ozone layer, has been performed. With the prohibition of the consumption of CFC gases, the use of HCFC gases, another gas group that damages the ozone layer, has been restricted and then banned. The most used of these gases is R22 gas. In order to perform the performances of reference R22 gas and R438A and R417A gases with infrared image analysis, two methods are proposed: COP with regional feature data acquisition and COP. In addition, on the two interfaces realized, the sizes obtained from infrared images are compared graphically and numerically. According to the obtained numerical and visual application results, R438A showed the closest performance to R22 gas. Compared to traditional computational performance tests, the ease of use of the system has shown that it is more advantageous in terms of remote measurement and simultaneous recording.

Physical and Thermal Properties of High-Density Polyethylene Film Modified with Polypropylene and Linear Low-Density Polyethylene

In view of the toughness and processing difficulty of high-density polyethylene (HDPE) film, the HDPE was modified by polypropylene (PP) and linear low density polyethylene (LLDPE), and the melt index, haze, dart impact strength, elongation at break were characterized. In addition the infrared spectra (IR), scanning electron microscopy (SEM), infrared image analysis, and differential scanning calorimetry (DSC) data were obtained. The results showed that the toughening effect of the 10%PP/30%LLDPE/60%HDPE composition was the best; the haze was reduced 6% and its dart impact strength and elongation at break were increased by 27.3% and 47%, respectively, relative to the pure HDPE. The blend of 10%PP/30%LLDPE/60%HDPE had compatibility. The melting point of the 10%PP/30%LLDPE/60%HDPE blend film increased by 5 °C compared with the pure HDPE film, with the results indicating the application fields of HDPE film could be widened.

Intrarater and Interrater Reliability of Infrared Image Analysis of Forearm Acupoints before and after Moxibustion.

The objective of this study was to determine the intrarater and interrater reliabilities of infrared image analysis of forearm acupoints before and after moxibustion. In this work, infrared images of acupoints in the forearm of 20 volunteers (M/F, 10/10) were collected prior to and after moxibustion by infrared thermography (IRT). Two trained raters performed the analysis of infrared images in two different periods at a one-week interval. The intraclass correlation coefficient (ICC) was calculated to determine the intrarater and interrater reliabilities. With regard to the intrarater reliability, ICC values were between 0.758 and 0.994 (substantial to excellent). For the interrater reliability, ICC values ranged from 0.707 to 0.964 (moderate to excellent). Given that the intrarater and interrater reliability levels show excellent concordance, IRT could be a reliable tool to monitor the temperature change of forearm acupoints induced by moxibustion.

Intra- and Interrater Reliability of Infrared Image Analysis of Facial Acupoints in Individuals with Facial Paralysis.

Infrared thermography (IRT), as a noncontact tool for temperature measurement, is widely applied in the study of acupuncture modernization. The aim of this study was to assess the intra- and interrater reliability of infrared image analysis of facial acupoints of subjects with facial paralysis and determine the factors influencing the variability of the measured values. A total of 26 patients with facial paralysis on one side, aged 26 to 53 years, participated voluntarily in the study. Facial infrared thermal images of all participants were analyzed by two trained raters at two different time points at a one-week interval. The intraclass correlation coefficient (ICC) was used to determine the intra- and interrater reliability of IRT measurements. The ICC values varied depending on the analyzed acupoints. The reliability of temperature measurement ranged from moderate to excellent (intrarater, ICC ranged from 0.669 to 0.990; interrater, ICC ranged from 0.661 to 0.987). The reliability of temperature difference measurement ranged from low to excellent (intrarater, ICC ranged from 0.412 to 0.882; interrater, ICC ranged from 0.334 to 0.828). The main influencing factor of reliability is the incomplete consistency in selecting acupoint positions when repeatedly positioning the same acupoint manually. Despite low reliability of temperature difference measurement at some acupoints, some auxiliary measures can be used to reduce the error of manual positioning. Thus, infrared thermal imaging still has the potential to assist in objective and quantitative research on acupuncture.

Estimated leakage current based on the thermal image of the polymer insulator using the color detection method

This paper reports estimates of leakage currents based on thermal images (infrared) of polymer isolators using color detection methods. In this study, laboratory pollution performance tests using sodium chloride and kaolin were carried out in accordance with the IEC 60507 standard with AC high voltage at 18kV. The severity of pollution is controlled and indicated by ESDD values of heavy pollutants, conditions of relative humidity controlled at 70RH and temperature of 260C. The color detection method is used to obtain the color percentage of thermal images. The ANFIS method predicts leakage currents from contaminated polymer insulators with input from the percentage of thermal image color. The results show that the leakage current can be estimated based on infrared image analysis with a color detection method, where each current leakage level correlates with red, yellow and blue.

Image stitching and partitioning algorithms for infrared thermal human-body images

The thermal infrared image of the human body directly reflects the temperature distribution of the human body surface. Based on in-depth analysis, the infrared image can provide intelligent diagnosis assistance for human diseases. This paper proposed two preprocessing algorithms, i.e., upper-lower body image-stitching and body image partitioning, for medical infrared image analysis. In the image stitching stage, the human body is first extracted from the background by local thresholding based on the characteristics of the actual imaging environment. Then the upper and lower body images are aligned and fused using binary and grayscale template matching. In the image partitioning stage, the key points of the part area are determined by the extremum-point analysis of the human contour. The human body is then partitioned into regions including head, trunk, limbs, etc. Experiments show that the proposed preprocessing algorithms produce satisfactory results in image-stitching and portioning, and can effectively support the quantitative and qualitative analysis of human body temperature distribution.

Infrared image enhancement model based on gravitational force and lateral inhibition networks

Problems such as low contrast, noise, and edge blur are often encountered in color infrared images produced by infrared cameras. To solve these problems, we propose a new image enhancement algorithm based on the gravitational force and lateral inhibition network. First, information on total gravitational force for each dimension of the color infrared image was obtained. These two-dimensional three gray level images obtained using three-dimensional color properties help to define noise, edge and region within each dimension. Secondly, these three gray level images were subjected to a dual threshold value. A mean filter was used to reduce noise, while the lateral inhibition network was used for resolution and edge detection, and the regional gravity factor was used for contrast control. Finally, each dimension was combined again and a color enhanced image was produced. This study sets out to develop a method of enhancement images for infrared image analysis in cooling systems. The images used in the study are made up of a compressor, a condenser, and an evaporator belonging to the cooling system. The implementation of our method is simple and easy to understand and yields more accurate results. The experimental results show that the proposed method can eliminate noise, blur, and low contrast, and can also improve the details of infrared images better than other methods.

Object Contour Tracking Algorithm of Infrared Image Under Complex Background

Traditional image contour tracking algorithm has low tracking accuracy. To solve this problem, an object contour tracking algorithm based on local significant edge features in complex background is proposed. In the algorithm, the projective invariant is firstly introduced, to construct the geometric information descriptor between the edge positions of the infrared image, and set up the histogram of the feature number of each target contour. The geometric similarity between the features is measured by the pasteurized coefficient, the edge features of the neighbourhood around the object contour are established, and the object contour with significant features in the edge of the image is searched. Combining Shape-context operator with edge feature, the feature description vector can be formed, and Euclidean distance is defined to track measurement function. Using this function, the selected object contour is tracked preliminarily. The random consistency checking algorithm is used to eliminate the false tracking feature points and obtain the best tracking value of the object contour, thus the infrared image’s object contour tracking is carried out in the complex background. Experimental simulation shows that the proposed algorithm has high tracking accuracy and effectively improves the quality of infrared image analysis.

Integrated recognition and quantitative detection of starch in surimi by infrared spectroscopy and spectroscopic imaging.

Surimi products have become increasingly-consumed food with prominent characteristics of high nutrition and convenience and its supply falls short of demand. However, due to exhausted fishery resource in recent years, surimi adulteration, such as addition of plant proteins, starch and other animal origin meat, is becoming serious, so recognition of these exogenous substances has become an urgent issue. In this study, Fourier transform infrared spectroscopy (FT-IR) combined with infrared spectroscopic imaging could distinguish heterogeneity in surimi qualitatively and quantitatively and obtain integral chemical images so that spatial distribution of each component in surimi could be visually displayed, thus a rapid recognition method and a prediction model were developed. The different starch contents in surimi had been primarily identified through intensity change of infrared absorption peaks at 1045cm-1 and 988cm-1, specifically with peak shifts to 1041cm-1 and to 992cm-1, respectively. In infrared imaging analysis, principal components (PCs) were separated and one key PC was confirmed as starch by characteristic peaks comparison at 1147cm-1, 1075cm-1, 997cm-1 and 930cm-1. Meanwhile, an established statistic model could predict starch content in surimi correctly with a reliable correlation coefficient (R=0.9856) and root mean square error of prediction (RMSEP=5.64). Therefore, FT-IR combined with infrared spectroscopic imaging could be applicable to integrally recognize and quantitatively detect starch in surimi.

Nanoscale infrared imaging analysis of carbonaceous chondrites to understand organic-mineral interactions during aqueous alteration

Organic matter in carbonaceous chondrites is distributed in fine-grained matrix. To understand pre- and postaccretion history of organic matter and its association with surrounding minerals, microscopic techniques are mandatory. Infrared (IR) spectroscopy is a useful technique, but the spatial resolution of IR is limited to a few micrometers, due to the diffraction limit. In this study, we applied the high spatial resolution IR imaging method to CM2 carbonaceous chondrites Murchison and Bells, which is based on an atomic force microscopy (AFM) with its tip detecting thermal expansion of a sample resulting from absorption of infrared radiation. We confirmed that this technique permits ∼30 nm spatial resolution organic analysis for the meteorite samples. The IR imaging results are consistent with the previously reported association of organic matter and phyllosilicates, but our results are at much higher spatial resolution. This observation of heterogeneous distributions of the functional groups of organic matter revealed its association with minerals at ∼30 nm spatial resolution in meteorite samples by IR spectroscopy.