Texture Image Research Articles

Parkinson's disease detection and stage classification: quantitative gait evaluation through variational mode decomposition and DCNN architecture

Parkinson's disease (PD) is a progressive, debilitating neurological movement disorder that affects the person's muscle control, movement, speech, cognition and dexterity. For diagnosing PD in a clinical setting, in addition to the neurological examinations, clinicians use the unified Parkinson disease rating scale (UPDRS) to assess the motor and non-motor impairments. Such a clinical assessment highly depends on the experience and expertise of the clinicians, and it may result in biased evaluation. Hence, to assist the clinicians, we put forward a gait analysis-based deep convolutional neural network (DCNN) framework which leverages the potentials of variational mode decomposition (VMD) technique with the recurrence plots (RP) to enhance the PD severity classification performance. Specifically, transforming the VMD modes of vertical ground reaction force (VGRF) time series data into two-dimensional texture images to capture the temporal dependency, this work trains the DCNN classifier through recurrence images for its ability to extract the discriminative features among the PD severity levels. For evaluation, this study utilises the VGRF dataset of 93 PD subjects and 73 healthy controls from Physiobank for three different walking tests. Consequently, utilising VMD, RP and DCNN in a unified framework, this investigation shows that the PD severity rating can be significantly enhanced through DCNN model that is trained using RP of dominant intrinsic mode functions (IMFs). The novelty of the proposed framework lies in identifying the prominent gait biomarkers through dominant IMFs from power spectral analysis for reducing the computational burden of DCNN. Moreover, to handle the data over-fitting issue in the classifier, L2 regularisation technique, which penalises the weight parameters of the nodes, is used in combination with the dropout layer. Experimental results underscore that the proposed VMD-RP-DCNN architecture can address the spectral overlapping issue in VGRF decomposition and achieve an average PD severity prediction accuracy of 98.45%.

Cartoon–Texture Image Decomposition Using Least Squares and Low-Rank Regularization

Cartoon–Texture Image Decomposition Using Least Squares and Low-Rank Regularization

The Dutch Apollo 11 Goodwill display contains genuine Moon rocks

In the 1970s, US President Richard Nixon offered moon samples returned by the Apollo 11 and Apollo 17 missions to the leaders of the nations of the World. In this study, we used a combination of advanced X-ray analysis methods, including microtomography, tomosynthesis and hyperspectral chemical mapping to carry out a non-destructive forensic investigation of the Dutch Apollo 11 Goodwill sample, normally on display at the Boerhaave museum in the Netherlands. These powerful methods were uniquely able to non-destructively interrogate the samples encased in plastic without contact, providing 3D images of sample textures and compositional analysis, to assess whether the results agree with archive data on Apollo 11 coarse-grained soil sample number 10085, and to provide new insights on their origins. Our forensic investigation asked the question: were the rocks in the Dutch display actually picked up on the surface of the moon by Neil Armstrong and Buzz Aldrin?

Interactions Determining Stereoselectivity in Two-Dimensional Systems─The Case of 22-Hydroxycholesterol Epimers.

Oxidized derivatives of cholesterol play an important role in the functioning of biomembranes. Unlike other biomolecules, which are physiologically active in only one enantiomeric form, some oxysterols exist endogenously as two stereoisomers that exhibit strictly different biological effects. In this paper, we focused our attention on 22-hydroxycholesterol (22-OH) epimers, 22(R)-OH and 22(S)-OH, and examined their properties in Langmuir monolayers spread at the air/water interface, using classical surface manometry complemented with Brewster angle microscopy (BAM) images of the film texture. The studied epimers showed quite different monolayer characteristics. Namely, 22(S)-OH formed homogeneous, condensed monolayers of high collapse pressure, while 22(R)-OH films were more disordered and expanded with quite low collapse pressure. Interestingly, the latter compound showed in the course of the surface pressure-molecular area isotherm a temperature-dependent plateau transition, characterized by the coexistence of domains of molecules with different inclinations, visible in BAM images as patches of varying brightness. Molecular dynamics (MD) simulations confirmed this and revealed that the greater structural variability of 22(R)-OH is due to the greater hydration of the oxygen atom at C(22) compared to the other epimer. Next, we tested whether 22-OH epimers could differentiate interactions with sphingomyelin (SM). Although the strength of interaction with SM was similar for both epimers, the composition of the films corresponding to this minimum was different. With the aid of MD, it was found that these differences result directly from the interplay between 22-OH molecules and their ability for hydrogen bond formation. Therefore, the stereochemistry of oxysterols seems to play a crucial role in the overall structural organization of the membrane.

Structural stability and thermodynamics of artistic composition

Inspired by the way that digital artists zoom out of the canvas to assess the visual impact of their works, we introduce a conceptually simple yet effective metric for quantifying the clarity of digital images. This metric contrasts original images with progressively "melted" counterparts, produced by randomly flipping adjacent pixel pairs. It measures the presence of stable structures, assigning the value zero to completely uniform or random images and finite values for those with discernible patterns. This metric respects the color diversity of the original image and withstands image compression and color quantization. Its suitability for diverse image analysis problems is demonstrated through its effective evaluation of textural images, the identification of structural transitions in physical systems like the Potts model, and its consistency with color theory in digital arts. This allows us to demonstrate that color in visual art functions as a state variable, akin to the spin configuration in magnets, driving artistic designs to transition between states with distinct clarity. When combined with the Shannon entropy, which quantifies color diversity, the structural stability metric can serve as a navigation tool for artists to explore pathways on the complex structural information landscape toward the completion of their artwork. As a practical demonstration, we apply our metric to refine and optimize an emote design for a video game. The structural stability metric emerges as a versatile tool for extracting nuanced structural information from digital images, which may enhance decision-making and data analysis across scientific and creative domains.

An intelligent multi-element fault diagnosis method of rolling bearings considering damage degrees and sensor abnormity under small samples

Aiming at the intelligent fault diagnosis problem of rolling bearings, a novel diagnosis method considering damage degrees and sensor abnormity under small samples is proposed. A complex fault mode simulation scheme with a total of 18 states is designed for rolling bearings, including a single element fault, double elements fault, and all elements fault with damage degrees of slight and heavy and the loose threaded connection of the used sensor. The variational mode decomposition (VMD) is used to decompose the original vibration signals and reconstruct the denoised signals, the reconstructed signals are converted into the grayscale images, and then processed by local binary pattern (LBP) to enhance the image texture features. Under small samples, an improved deep convolutional generative adversarial network (DCGAN) through upsampling, activation function optimization, Dropout addition and model architecture adjustment is used to expand the grayscale texture image (GTI) samples. The improved DCGAN converges the fastest in all states, and the final MMD values are all below 0.5. For the different sample expansion ratios, the residual neural network (ResNet) as the fault diagnosis model is used to verify the effectiveness of DCGAN sample expansion method in improving the accuracy of fault diagnosis. The results show when the original number of samples is 100, the optimal expansion ratio is 1:1. And the fault diagnosis accuracy of ResNet with DCGAN sample expansion is increased by 6.81% from 85.97 to 92.78%, which proves that the proposed method can not only effectively distinguish the fault modes from a single element to all elements with different damage degrees of rolling bearings, but also identify the sensor abnormity with a high accuracy. This work provides an effective way for the intelligent diagnosis of complex fault modes of rolling bearings under small samples.

Training ESRGAN with multi-scale attention U-Net discriminator

In this paper, we propose MSA-ESRGAN, a novel super-resolution model designed to enhance the perceptual quality of images. The key innovation of our approach lies in the integration of a multi-scale attention U-Net discriminator, which allows for more accurate differentiation between subject and background areas in images. By leveraging this architecture, MSA-ESRGAN surpasses traditional methods and several state-of-the-art super-resolution models in terms of Natural Image Quality Evaluator (NIQE) scores as well as Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM) across various benchmark datasets, including BSD100, Set5, Set14, Urban100, and OST300. Additionally, subjective evaluations further confirm the enhanced visual quality delivered by MSA-ESRGAN, particularly in terms of preserving texture and overall image realism. To ensure a fair comparison with Real-ESRGAN, we initialized our generator with a pre-trained Real-ESRNET model and followed the same training setup. Our model was trained on the DIV2K dataset using high-resolution image patches and the Adam optimizer, incorporating exponential moving average (EMA) for stability and performance enhancement. Evaluations on multiple benchmark datasets demonstrate that MSA-ESRGAN consistently delivers superior perceptual quality, as evidenced by higher NIQE, PSNR, and SSIM scores compared to other methods. Specifically, our model shows significant improvements in both objective and subjective measures of image quality. Furthermore, an ablation study highlighted the critical role of our multi-scale attention U-Net discriminator in enhancing the model’s performance. The results underscore the effectiveness of MSA-ESRGAN in maintaining image naturalness and perceptual quality, providing a robust benchmark for blind super-resolution tasks.

A Model Development Approach Based on Point Cloud Reconstruction and Mapping Texture Enhancement

To address the challenge of rapid geometric model development in the digital twin industry, this paper presents a comprehensive pipeline for constructing 3D models from images using monocular vision imaging principles. Firstly, a structure-from-motion (SFM) algorithm generates a 3D point cloud from photographs. The feature detection methods scale-invariant feature transform (SIFT), speeded-up robust features (SURF), and KAZE are compared across six datasets, with SIFT proving the most effective (matching rate higher than 0.12). Using K-nearest-neighbor matching and random sample consensus (RANSAC), refined feature point matching and 3D spatial representation are achieved via antipodal geometry. Then, the Poisson surface reconstruction algorithm converts the point cloud into a mesh model. Additionally, texture images are enhanced by leveraging a visual geometry group (VGG) network-based deep learning approach. Content images from a dataset provide geometric contours via higher-level VGG layers, while textures from style images are extracted using the lower-level layers. These are fused to create texture-transferred images, where the image quality assessment (IQA) metrics SSIM and PSNR are used to evaluate texture-enhanced images. Finally, texture mapping integrates the enhanced textures with the mesh model, improving the scene representation with enhanced texture. The method presented in this paper surpassed a LiDAR-based reconstruction approach by 20% in terms of point cloud density and number of model facets, while the hardware cost was only 1% of that associated with LiDAR.

ATGT3D: Animatable Texture Generation and Tracking for 3D Avatars

We propose the ATGT3D an Animatable Texture Generation and Tracking for 3D Avatars, featuring the innovative design of the Eye Diffusion Module (EDM) and Pose Tracking Diffusion Module (PTDM), which are dedicated to high-quality eye texture generation and synchronized tracking of dynamic poses and textures, respectively. Compared to traditional GAN and VAE methods, ATGT3D significantly enhances texture consistency and generation quality in animated scenes using the EDM, which produces high-quality full-body textures with detailed eye information using the HUMBI dataset. Additionally, the Pose Tracking and Diffusion Module (PTDM) monitors human motion parameters utilizing the BEAT2 and AMASS mesh-level animatable human model datasets. The EDM, in conjunction with a basic texture seed featuring eyes and the diffusion model, restores high-quality textures, whereas the PTDM, by integrating MoSh++ and SMPL-X body parameters, models hand and body movements from 2D human images, thus providing superior 3D motion capture datasets. This module maintains the synchronization of textures and movements over time to ensure precise animation texture tracking. During training, the ATGT3D model uses the diffusion model as the generative backbone to produce new samples. The EDM improves the texture generation process by enhancing the precision of eye details in texture images. The PTDM involves joint training for pose generation and animation tracking reconstruction. Textures and body movements are generated individually using encoded prompts derived from masked gestures. Furthermore, ATGT3D adaptively integrates texture and animation features using the diffusion model to enhance both fidelity and diversity. Experimental results show that ATGT3D achieves optimal texture generation performance and can flexibly integrate predefined spatiotemporal animation inputs to create comprehensive human animation models. Our experiments yielded unexpectedly positive outcomes.

An Experimental Investigation into the Enhancement of Surface Quality of Inconel 718 Through Axial Ultrasonic Vibration-Assisted Grinding in Dry and MQL Environments

Ultrasonic vibration-assisted grinding (UVAG) has proven to be beneficial for grinding difficult-to-machine materials. This work attempts to enhance the grinding performance of Inconel 718 through a comprehensive study of UVAG characteristics. Grinding experiments were performed in both dry and Minimum Quantity Lubrication (MQL) environments, and assessment of the grinding forces, specific energy, residual stress, and surface topography was done. A substantial reduction of both surface roughness and grinding force components was observed in UVAG compared to conventional grinding (CG). Utilizing UVAG with MQL at the maximum vibration amplitude led to a 64% reduction in tangential grinding force and a 51% decrease in roughness parameter, Ra, when compared to CG conducted in a dry environment. The high-frequency indentations of the abrasives in UVAG generated compressive residual stresses on the ground surface. Surface parameters pointed to uniform texture and SEM images showed widening of abrasive grain tracks on the workpiece surface during UVAG. The utilization of UVAG under MQL produced a synergistic impact and resulted in the lowest grinding forces, specific energy, and optimal surface quality among all the grinding conditions investigated. Overall analysis of the results indicated that the axial configuration of the vibration set-up is favorable for UVAG, and the high-frequency periodic separation-cutting characteristic of the process improves lubricating efficiency and grinding performance.

Combined query embroidery image retrieval based on enhanced CNN and blend transformer

Embroidery images carry rich historical information and are an important form of embroidery art. In the field of combination query image retrieval, how to efficiently retrieve the embroidery image information required by users has become a current research challenge. In recent years, convolutional neural networks (CNNs) have achieved significant success in image feature extraction, but they tend to focus on local information, making it easy to ignore global context information when processing such textured embroidery images. Therefore, we propose a combination query retrieval method for embroidery images. First, we propose Blend-Transformer, which introduces Group External Attention (GEA). GEA can integrate feature information from three different dimensions, effectively capturing the local and global context information of embroidery images. Second, we propose Enhanced CNN, which introduces Shuffle Attention (SA), regrouping the reference image features extracted by CNN and reaggregating them by channel to enhance the richness of embroidery image feature information. Through experiments on the TCE-S and ICR2020 standard datasets, we verify the excellent performance of the proposed algorithm in embroidery image retrieval. Our method fills the gap in embroidery image retrieval research and provides a new perspective for the protection of embroidery art.

Distinguishing Structures from Textures by Patch‐based Contrasts around Pixels for High‐quality and Efficient Texture filtering

AbstractIt is still challenging with existing methods to distinguish structures from texture details, and so preventing texture filtering. Considering that the textures on both sides of a structural edge always differ much from each other in appearances, we determine whether a pixel is on a structure edge by exploiting the appearance contrast between patches around the pixel, and further propose an efficient implementation method. We demonstrate that our proposed method is more effective than existing methods to distinguish structures from texture details, and our required patches for texture measurement can be smaller than the used patches in existing methods by at least half. Thus, we can improve texture filtering on both quality and efficiency, as shown by the experimental results, e.g., we can handle the textured images with a resolution of 800 × 600 pixels in real‐time. (The code is available at https://github.com/hefengxiyulu/MLPC)

Lossless Recompression of Vector Quantization Index Table for Texture Images Based on Adaptive Huffman Coding Through Multi-Type Processing

With the development of the information age, all walks of life are inseparable from the internet. Every day, huge amounts of data are transmitted and stored on the internet. Therefore, to improve transmission efficiency and reduce storage occupancy, compression technology is becoming increasingly important. Based on different application scenarios, it is divided into lossless data compression and lossy data compression, which allows a certain degree of compression. Vector quantization (VQ) is a widely used lossy compression technology. Building upon VQ compression technology, we propose a lossless compression scheme for the VQ index table. In other words, our work aims to recompress VQ compression technology and restore it to the VQ compression carrier without loss. It is worth noting that our method specifically targets texture images. By leveraging the spatial symmetry inherent in these images, our approach generates high-frequency symbols through difference calculations, which facilitates the use of adaptive Huffman coding for efficient compression. Experimental results show that our scheme has better compression performance than other schemes.

PBNet: Combining Transformer and CNN in Passport Background Texture Printing Image Classification

Passport background texture classification has always been an important task in border checks. Current manual methods struggle to achieve satisfactory results in terms of consistency and stability for weakly textured background images. For this reason, this study designs and develops a CNN and Transformer complementary network (PBNet) for passport background texture image classification. We first design two encoders by Transformer and CNN to produce complementary features in the Transformer and CNN domains, respectively. Then, we cross-wisely concatenate these complementary features to propose a feature enhancement module (FEM) for effectively blending them. In addition, we introduce focal loss to relieve the overfitting problem caused by data imbalance. Experimental results show that our PBNet significantly surpasses the state-of-the-art image segmentation models based on CNNs, Transformers, and even Transformer and CNN combined models designed for passport background texture image classification.

Advancements in smart agriculture through innovative weed management using wavelet-based convolution neural network

Smart agriculture has shifted the paradigm by integrating advanced technologies, particularly weed management. This paper introduces an innovative approach to weed control by applying a Wavelet-based Convolution Neural Network (WCNN). In the era of precision agriculture, our study explores the integration of WCNN into real-world scenarios, emphasizing its adaptability to diverse environmental conditions. Utilizing the spatial-frequency analysis features of wavelets and convolutional neural networks, the WCNN model is the most effective at finding weeds, classifying them, and managing them specifically in agricultural fields in real-time. This research contributes to the scientific discourse on smart agriculture and addresses the challenges of invasive weeds, presenting a sustainable solution for optimizing resource utilization. Our investigation includes a detailed exploration of WCNN’s adaptive learning mechanisms and dynamic adjustment to changing agricultural landscapes. The model seamlessly integrates with existing smart farming infrastructure, showcasing a substantial reduction in manual intervention and a simultaneous increase in agricultural productivity. We incorporate fog computing and resource optimization into our framework, enhancing the efficiency of onboard data processing. To evaluate the real-world efficacy of WCNN, we conducted comprehensive experiments in texture classification and image labelling using two distinct datasets: the plant seedling and soybean weed datasets. Results demonstrate the superior performance of WCNN, achieving higher accuracy in training and test scenarios with significantly fewer parameters than traditional CNNs. For the soybean weed dataset, WCNN achieved remarkable accuracy in the training (0.9970) and testing (0.9987) phases, with correspondingly low losses of 0.0109 and 0.0048. The WCNN model demonstrated high accuracy during training (0.9739) and testing (0.9902), with minimal losses of 0.0898 and 0.0239 in the plant seedling dataset.

Single-use commercial bio-based plastics under environmental degradation conditions: Is their biodegradability and compostability a fact?

Evaluating compostability is increasingly essential for proving commercial bio-based cutlery or packaging since these materials must biodegrade under controlled conditions quickly. Utensils for eating represent Mexico's most popular consumer single-use materials, and Mexican regulations based on biodegradation or compostability are still vague and lack scientific evaluations. This study analyzed three bio-based polymeric materials (bags, dishes, and forks) from commercial brands following Mexican regulations and using various analytical techniques to verify their biodegradability and compostability. First, weight loss measurements, stress-strain tests, and topographic imaging were applied for preliminary observations at the macro scale up to 90 days of compostability. Besides, spectroscopy, microscopy, and thermal techniques indicate changes and behavior of the bio-based materials depending on the composition. The results suggest that bags exhibited the highest decomposition rate (80 %) compared to dishes and forks. Similarly, mechanical resistance indicates a reduction of 62 % for bags, 30 % for dishes, and almost none for forks. Texture image analysis revealed that the complexity and roughness of the materials increased over time, correlating with the physical changes observed. These results indicate minimal surface topography changes and higher stiffness for dishes and forks, indicating low biodegradability. SEM images supported these findings, showing surface degradation in bags and dishes but not in forks. FTIR and XRD analyses confirmed the presence of polyamide (bags) and polypropylene (dishes and forks). These results reduce biodegradation and differ from the claims made by manufacturers. The thermal analysis found similar results, indicating that the materials' thermal stability decreased after degradation, which is related to lower biodegradability and compostability. Overall, the study concluded only bags meet the criteria for compostability in national regulations. However, dishes and forks made of petroleum-derived polymers have higher resistance to natural and microbial degradation.

Classification of Kudoa thyrsites infected and uninfected fish using a handheld near-infrared spectrophotometer, SIMCA and PLS-DA.

Kudoa thyrsites infection of marine fish typically results in myoliquefaction, which is only apparent 24 to 56 h post-mortem. The traditional methods for the detection of K. thyrsites infected fish are time-consuming and destructive, reducing its marketability. This poses a challenge for the fish industry to remove infected fish before it reaches the market or further processing activities. This study investigated the use of near-infrared (NIR) spectroscopy, in combination with soft independent modelling of class analogy (SIMCA) and partial least square discriminant analysis (PLS-DA), for discriminating K. thyrsites infected fish from uninfected fish. Performance of the classification models was evaluated by calculating the sensitivity, specificity and precision. A total of 334 fish samples (200 sardine, 64 hake and 70 kingklip) were used for this study. Infection of K. thyrsites was determined with the use of qPCR assays. Ninety per cent (90%) of the sardine samples, 78% of the hake samples and 37% of the kingklip samples were infected. Class groups of infected and uninfected fish samples were created for the purpose of generating SIMCA and PLS-DA classification models for each species of fish, as well as for a species independent data set. Principal component analysis (PCA) of NIR spectra did not show any clustering for infected and uninfected samples. Calibration and test sample sets were generated for the purpose of building and testing the SIMCA and PLD-DA classification models. SIMCA and PLS-DA were unable to classify test samples correctly into the two classes. The number of misclassifications (NMC) was higher for the SIMCA models than for the PLS-DA models, with more than 60% incorrectly classified. SIMCA classified most of the test samples into both classes. The precision for PLS-DA were 89% for sardine, 81% for hake, 0% for kingklip and 87% for species independent models, however, most samples were classified at infected. The use of NIR spectroscopy and classification models such as SIMCA and PLS-DA showed limited use as a method to distinguish between K. thyrsites infected and uninfected fish samples. Textural and chemical changes during extended frozen storage of the fish samples may have masked the effects associated with K. thyrsites infection. Further studies are suggested where NIR spectroscopy is used in combination with texture analysis and image spectroscopy.

Learning graph-Fourier spectra of textured surface images for defect localization

In the realm of industrial manufacturing, product inspection remains a significant bottleneck, with only a small fraction of manufactured items undergoing inspection for surface defects. Advances in imaging systems and AI can allow automated full inspection of manufactured surfaces. However, even the most contemporary imaging and machine learning methods perform poorly for detecting defects in images with highly textured backgrounds, that stem from diverse manufacturing processes. This paper introduces an approach based on graph Fourier analysis to automatically identify defective images, as well as crucial graph Fourier coefficients that inform the defects in the images. The approach thereby facilitates precise localization and characterization of defects, amidst highly textured backgrounds. A convolutional neural network model (1D-CNN) was trained with the coefficients of the graph Fourier transform of the images as the input to identify, with classification accuracy of 99.4%, if the image contains a defect. An explainable AI method using SHAP (SHapley Additive exPlanations) was used to further analyze the trained 1D-CNN model to discern important spectral coefficients for each image. This approach sheds light on the crucial contribution of low-frequency graph eigen waveforms to precisely localize surface defects in images, thereby advancing the realization of zero-defect manufacturing.

Multimodal medical image fusion based on interval gradients and convolutional neural networks

Many image fusion methods have been proposed to leverage the advantages of functional and anatomical images while compensating for their shortcomings. These methods integrate functional and anatomical images while presenting physiological and metabolic organ information, making their diagnostic efficiency far greater than that of single-modal images. Currently, most existing multimodal medical imaging fusion methods are based on multiscale transformation, which involves obtaining pyramid features through multiscale transformation. Low-resolution images are used to analyse approximate image features, and high-resolution images are used to analyse detailed image features. Different fusion rules are applied to achieve feature fusion at different scales. Although these fusion methods based on multiscale transformation can effectively achieve multimodal medical image fusion, much detailed information is lost during multiscale and inverse transformation, resulting in blurred edges and a loss of detail in the fusion images. A multimodal medical image fusion method based on interval gradients and convolutional neural networks is proposed to overcome this problem. First, this method uses interval gradients for image decomposition to obtain structure and texture images. Second, deep neural networks are used to extract perception images. Three methods are used to fuse structure, texture, and perception images. Last, the images are combined to obtain the final fusion image after colour transformation. Compared with the reference algorithms, the proposed method performs better in multiple objective indicators of QEN\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$Q_{EN}$$\\end{document}, QNIQE\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$Q_{NIQE}$$\\end{document}, QSD\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$Q_{SD}$$\\end{document}, QSSEQ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$Q_{SSEQ}$$\\end{document} and QTMQI\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$Q_{TMQI}$$\\end{document}.

Computation Legendre moments using image analysis technique

Abstract In this paper, phase transition temperatures of nematic liquid crystals: 4-n alkoxy benzoic acid (nOBA, where n = 4 and 6) are determined from the Legendre moments of liquid crystal textures. Here, the image analysis technique in conjunction with polarizing optical microscope is used to compute the Legendre moments from the textures of liquid crystals. As a function of temperature, Liquid crystal textures are recorded from the arthroscopic mode of optical microscope. Changes in textural features of the sample with respect to temperature bring the variations in intensity values of liquid crystal textures. The brightness image that is generated from the liquid crystal textural patterns separates the information about fluctuations in intensity. In this approach, Legendre moments are computed from the brightness images of the liquid crystal textures and are plotted as a function of temperature. Significant variations in the brightness Legendre moment curve provide insight into the liquid crystal transition temperatures. Results obtained from the present methodology are compared with gray color textures data and the experimental technique of differential scanning calorimetry and are in good agreement with each other. This method is an objective and quantitative technique whereas POM method is qualitative and subjective.