Color Image Analysis Research Articles

Detection of damage caused by Nezara viridula on soybean using novel imaging approaches based on computed tomography and image color analysis.

Soybean (Glycine max L.) is an important leguminous plant, in which pests trigger significant damage every year. Important members of this community are insects with piercing-sucking mouthpart, especially the southern green stinkbug, Nezara viridula L.. This insect with its extraoral digestion causes visible alterations (morphological and color changes) in the seeds. We aimed to obtain precise information about the extent and nature of damage in soybeans caused by N. viridula using nondestructive imaging methods. Two infestation conditions were applied: one with controlled numbers of pests (six insects/15 pods) and another with naturally occurring pests (samples collected from the apical part of the plant and samples from whole plants). An intact control group was also included, resulting in four treatment groups. Seed samples were analyzed by computed tomography (CT) and image color analysis under laboratory conditions. According to our CT findings, the damage caused by N. viridula changed the radiodensity, volume, and shape (Solidity) of the soybean seeds during the pod-filling and maturing period. Radiodensity was significantly reduced in all three damaged categories compared to the intact sample; the mean radiodensity reduction range was 49-412 HU. The seed volume also decreased significantly (25%-80% decrease), with a threefold reduction for samples exposed to regulated damage compared to natural ones. The samples exposed to natural damage showed significant but minor reduction in solidity, while samples exposed to regulated damage showed a prominent decrease (~12%). Image color analysis showed that the damaged samples were well distinguishable, and the differences were statistically verifiable. The achieved data derived from our external and internal imaging approaches contribute to a better understanding of the internal chemical processes, and CT analysis helps to understand the alteration trends of the hidden structure of seeds caused by a pest. Our results can contribute to the development of a practically applicable system based on image analysis, which can identify lots damaged by insects.

Improving construction site efficiency through automated progress monitoring of underground pipe installation sites using image color analysis of iPhone LiDAR camera data

This study presents an innovative method utilizing smartphone for automated progress monitoring at underground pipe installation sites. Leveraging the LiDAR iPhone camera, the method captures detailed point cloud data of construction sites. Sophisticated color analysis of images accurately distinguishes between areas with and without pipes within excavations. Key aspects of the proposed workflow include segmentation of the excavation area, differentiation between main and side excavations, and application of an earth color mask in the RGB space to isolate pipes. The research focuses on enhancing measurement precision for excavation width, depth, and pipe burial depth, significantly reducing the manual labor traditionally required at construction sites, thereby offering an efficient and cost-effective solution. We further demonstrated the robustness of the proposed algorithm by applying it to two types of data acquired at actual construction sites. This approach is expected to contribute significantly to the digital transformation in the construction industry.

Digital color analysis and machine learning for ballpoint pen ink clustering and aging investigation

Fraudulent activities often involve document manipulation, which poses a significant challenge to forensic science. To address this issue, a novel method was developed that combines intended artificial UV pre-degradation, digital color analysis (DCA) of stroke images, and various machine learning (ML) models. This method can cluster blue ballpoint pen inks and predict their photodegradation time. The results of the study indicate that the k-shape clustering method is highly effective in differentiating between inks based on their degradation curve patterns and HSV or RBS color features, aligning well with results from chromatography analyses. Furthermore, the random forest regression model demonstrated superior performance in predicting age, exhibiting the highest coefficients of determination. The DCA-ML method is a straightforward, cost-effective, and highly accurate solution for clustering blue pen inks. Using photodegradation curves to predict document age could eliminate the need for conventional physicochemical analysis techniques.

Accurate and fast quaternion fractional-order Franklin moments for color image analysis

Quaternion type moments (QTM) based on hypergeometric functions and harmonic functions have attracted interest in the imaging field. The Franklin system is a complete orthonormal system consisting of piecewise linear continuous functions with Haar wavelet collocation points. However, due to the lack of efficient computational methods and explicit expressions, research on piecewise polynomials with Haar wavelet collocation points remains underexplored. Furthermore, the impact of magnitude and phase of quaternion on the color image features extracted by QTM has not been comprehensively explored. To address these problems, a fast and accurate computation method for Franklin functions is proposed. Quaternion fractional-order Franklin moments (QFFM) are proposed and using the Minkowski distance as a prior parameter for selecting unit pure quaternions. Experiments are conducted to demonstrate the superior performance of QFFM in image reconstruction and color image classification tasks compared with QTM-based and deep learning-based methods. Moreover, with the help of experiments, it is confirmed the effectiveness of using the Minkowski distance to select magnitude and phase of quaternion.

Image analysis by fractional-order weighted spherical Bessel-Fourier moments

Moment and moment invariants as effective feature descriptors have been widely applied in image analysis, pattern recognition and computer vision applications. Scholars have demonstrated that fractional-order orthogonal moments outperform their corresponding moments in representing the fine details of a given image. To this end, we propose a new fractional-order orthogonal moments based on fractional-order weighted spherical Bessel polynomial in this paper, which is named as Fractional-order weighted Spherical Bessel-Fourier Moments (FrSBFMs). Moreover, to address the issue of color image analysis, FrSBFMs are integrated with quaternion theory to develop Quaternion FrSBFMs (QFrSBFMs). In addition, the rotation invariance and parameter α analysis of FrSBFMs and QFrSBFMs are discussed in detail. The experimental results show the effectiveness of the proposed FrSBFMs and QFrSBFMs in terms of image reconstruction capability, pattern recognition accuracy and zero-watermark verification.

A novel colorimetric sensor through peroxymonosulfate activated by CoFe@CN nanozymes for on-site resorcinol detection

The presence of resorcinol (RS) in water contaminated with pollutants may engender environmental and ecosystem risks. In this study, we developed rapid and efficient colorimetric methods for the on-site quantitative determination of RS. Specifically, a synthetic nanozyme composed of nitrogen-doped carbon-coated cobalt-iron (CoFe@CN) was utilized to initiate the oxidation reaction of 3,3′,5,5′-tetramethylbenzidine (TMB) with the activator of peroxymonosulfate (PMS). Compared to the activator of hydrogen peroxide (H2O2), PMS demonstrated a sixfold enhancement in catalytic efficacy, markedly elevating the detection performance. Density Functional Theory calculations indicate that RS can serve as a reducing agent for oxTMB, confirming the theoretical feasibility of this process. A linear relationship between inhibition rate (IR %) of RS concentration and absorbance was established and the colorimetric analysis revealed a linear relationship between RS concentration and gray scale values over the range of 0–45 μM, with a detection limit of 3.39 μM. Furthermore, smartphone-based analysis of color images allows for on-site quantitative analysis of RS concentration, leveraging portability and real-time detection capabilities. This study provides pioneering analytical support for on-site water quality monitoring within the ecological environment.

Utilizing color analysis and image processing for the assessment of the color of block para rubber

This research explores the use of image processing and color analysis as a reliable approach for evaluating para rubber color quality. An automated color comparator prototype, based on the CIELAB color method and employing various image processing algorithms, is introduced. The research aims to enhance the precision of color analysis for block para rubbers (BPRs), a critical raw material in the rubber industry, impacted by environmental conditions during the sun-drying process. The study follows international standards for BPR specimen preparation and employs an image acquisition system, threshold techniques, and algorithms for color analysis. The proposed method aligns with Rubber Research Institute standards. The outcomes demonstrate the effectiveness of the automated system in accurately assessing the color values of BPRs, providing valuable insights for the rubber industry. This image processing technique offers advantages in natural rubber production for products requiring specific color characteristics.

Dual-lanthanide cyclodextrin-polyoxometalate fluorescent assembly for rapid and sensitive detection of ciprofloxacin

Quick screening and in-situ detection of trace antibiotics in environmental samples is needed for better control and monitoring of antibiotics pollution. Herein, a novel dual-lanthanide (Tb3+ and Eu3+) cyclodextrin-polyoxometalate fluorescent assembly (CD-LnPOM) has been developed for ciprofloxacin (CIP) detection. The "ratiometric" fluorescence response of CD-LnPOM enables specific detection of CIP within 5 s over a wide concentration range (0–100 μM) with a detection limit of 0.266 nM. When CIP presents, cyclodextrin acts as a host and encapsulates CIP in the cavity through host-guest interaction, which enhances the antenna sensitization between Tb3+ and CIP. Meanwhile, due to the more robust coordination between CIP and Tb3+, CIP blocks the energy transfer from Tb3+ to Eu3+ and recovers the sensitization of Tb3+ by POM. Based on the developed CD-LnPOM probe, a portable sensing device based on digital image color analysis is successfully demonstrated for real river water and antibiotic sludge samples. The developed fluorescent probe enables rapid and in-situ detection of trace antibiotics, which offers a new method for antibiotics screening and monitoring.

Improvement of color image analysis using a hybrid artificial intelligence algorithm

Improvement of color image analysis using a hybrid artificial intelligence algorithm

RGB Imaging as a Tool for Remote Sensing of Characteristics of Terrestrial Plants: A Review.

Approaches for remote sensing can be used to estimate the influence of changes in environmental conditions on terrestrial plants, providing timely protection of their growth, development, and productivity. Different optical methods, including the informative multispectral and hyperspectral imaging of reflected light, can be used for plant remote sensing; however, multispectral and hyperspectral cameras are technically complex and have a high cost. RGB imaging based on the analysis of color images of plants is definitely simpler and more accessible, but using this tool for remote sensing plant characteristics under changeable environmental conditions requires the development of methods to increase its informativity. Our review focused on using RGB imaging for remote sensing the characteristics of terrestrial plants. In this review, we considered different color models, methods of exclusion of background in color images of plant canopies, and various color indices and their relations to characteristics of plants, using regression models, texture analysis, and machine learning for the estimation of these characteristics based on color images, and some approaches to provide transformation of simple color images to hyperspectral and multispectral images. As a whole, our review shows that RGB imaging can be an effective tool for estimating plant characteristics; however, further development of methods to analyze color images of plants is necessary.

Quaternion fast and accurate polar harmonic Fourier moments for color image analysis and object recognition

Image moments, as a kind of global feature descriptor of images, have become a valuable tool for pattern recognition and image analysis. However, traditional methods are mainly used to deal with grayscale images. In this paper, we apply quaternions to fast and accurate polar harmonic Fourier moments, proposing a kind of quaternion fast and accurate polar harmonic Fourier moment (QFAPHFM) capable of handling color images. Furthermore, this paper provides a detailed analysis of the invariance of QFAPHFMs under rotation, scaling, and translation transformations. The experimental results show that QFAPHFMs exhibit excellent performance in both image reconstruction and object recognition tasks. QFAPHFMs achieve accurate image reconstruction under noiseless and noisy conditions, and demonstrate excellent recognition performance in the color-based object recognition tasks.

Intelligent color image analysis of sintered ores for simple and rapid determination of Fe3O4 concentration

A simple determination of the Fe3O4 concentrations of sintered ores using color images of the samples has been explored. Sintered ore is mainly composed of Fe2O3 (red), Fe3O4 (black), and other white inorganic oxides, so the color of sintered ore could be representative of the relative abundance of the constituents. Two important challenges were addressed to achieve reliable quantitative color image analysis. First, minute dents and bumps (embosses) exist on the sample surface due to inconsistent particle sizes and particle agglomeration, thereby generating dark shadows. Second, small white spots corresponding to inorganic oxide particles were spread throughout acquired images. The white spots yield very high RGB values, which would hamper the translation of the real sample color originating from the iron oxides. Therefore, the segmentations of particle agglomeration-induced shadows and white spots in the sample images were separately executed using Otsu's method and modified fuzzy C-means (MFCM), respectively. Then, color moments and derived variables from the segmented images were employed to determine Fe3O4 concentrations (6.5–10.5 wt%) using extreme gradient boosting (XGBoost). The predicted concentrations from the color analysis correlated well with reference concentrations determined using conventional titration, with a root mean square error of prediction (RMSEP) of 0.39 wt%.

Fuzzy Inference Systems to Fine-Tune a Local Eigenvector Image Smoothing Method

Image denoising is a fundamental research topic in colour image processing, analysis, and transmission. Noise is an inevitable byproduct of image acquisition and transmission, and its nature is intimately linked to the underlying processes that produce it. Gaussian noise is a particularly prevalent type of noise that necessitates effective removal while ensuring the preservation of the original image’s quality. This paper presents a colour image denoising framework that integrates fuzzy inference systems (FISs) with eigenvector analysis. This framework employs eigenvector analysis to extract relevant information from local image neighbourhoods. This information is subsequently fed into the FIS system which dynamically adjusts the intensity of the denoising process based on local characteristics. This approach recognizes that homogeneous areas may require less aggressive smoothing than detailed image regions. Images are converted from the RGB domain to an eigenvector-based space for smoothing and then converted back to the RGB domain. The effectiveness of the proposed methods is established through the application of various image quality metrics and visual comparisons against established state-of-the-art techniques.

A Multicenter Pilot Study of Biliary Atresia Screening Using Digital Stool Color Imaging.

The presence of alcoholic stool in biliary atresia (BA) patients is the basis of a stool color card (SCC), a screening tool that has led to more patients receiving Kasai portoenterostomy earlier. This study aimed to evaluate the color image processing of stool images captured using smartphones. We propose that measuring digital color parameters is a more objective method for identifying BA stools and may improve the sensitivity of BA screening. A prospective study was conducted in five hospitals in Thailand between October 1, 2020, and December 31, 2021. Stools from infants presenting with jaundice, acholic stool, or dark-colored urine were photographed. Digital image color analysis was performed, and software was developed based on the color on the original SCC. Sensitivity and specificity for predicting BA stools were compared between the SCC and the software. Of 33 infants eligible for data collection, 19 were diagnosed with BA. Saturation and blue were two potential digital color parameters used to differentiate BA stools. The receiver operating characteristic curve was used to determine the optimum cutoff point of both values, and when saturation ≤56 or blue ≥61 was set as a threshold for detecting BA stool, high accuracy was achieved at 81.8% and 78.8%, respectively. Digital image processing is a promising technology. With appropriate cutoff values of saturation in hue, saturation, value and blue in red, green, blue color models, BA stools can be identified, and equivocal-colored stools of non-BA patients can be differentiated with acceptable accuracy in infants presenting with jaundice.

Chitosan Decelerates Melanosis in Shrimp: A Novel Technique for Visual Quality Assessment Using Digital Image Analysis

The study aimed to determine the effects of chitosan, sodium metabisulfite, and citric acid against melanosis, which causes significant economic losses during the storage of shrimps. For this purpose, two different types of chitosan were extracted from tiger prawn shell wastes. The extracted chitosan was characterized by viscosity, FT-IR, XRD, SEM imaging, and color analyses. Prevention of melanosis formation was investigated by testing chitosan, sodium metabisulfite, and citric acid. To achieve this, fresh deep-water pink shrimp samples immersed in different solutions were stored for 12 days at 4℃. Melanosis formation in the samples was observed via color measurement, sensory evaluation, and image analysis. According to the results of the research, the deacetylation degrees of chitosans were found to be 99.50 and 89.51% and 76.31 and 78.24% by two different methods, respectively. Viscosities were measured as 0.34 and 4.17 cSt, respectively. Regarding the color parameters, the L* values were 83.68 and 78.94, a* values were 0.55 and 2.71, and b* values were 10.33 and 13.85, respectively. According to the sensory evaluation conducted throughout the study, melanosis formation was observed in all groups starting from the 6th day. On the 8th day, the control groups exceeded the acceptability threshold, and on the 12th day, melanosis formation spread throughout the body in all groups. The sensory evaluation results were supported by the color measurement and image analysis implemented for the first time in this study. The present study’s results showed that chitosan obtained from shellfish wastes decelerates melanosis formation as an alternative to commercially used chemicals.

Evaluating Variability in Reflective Photoelasticity: Focus on Adhesives, Light Sources, and Camera Setup

This study investigates the impact of various experimental parameters on the reflective photoelastic coating technique used to measure the stress and strain in model soil particles. The focus is directed towards three pivotal parameters: the choice of adhesive for the photoelastic coating, the inherent properties of the light source, and the configuration of the camera for color image analysis. Through a series of uniaxial compression tests on consistently crafted model soil particles, a notable relationship emerges between the adhesive’s shear strength, its viscosity, and the uniformity of the photoelastic response. While the light source’s characteristics and camera adjustments hold significance, their influence on the consistency of the response is comparatively subtle. Consequently, the adhesive’s role is underscored as instrumental in influencing the photoelastic response, offering valuable insights for subsequent research endeavors utilizing the reflective photoelastic technique.

The role of operations over intuitionistic fuzzy index matrices in color image analysis

In this paper, an attempt has been made to define contrast intensification operator on intuitionistic fuzzy index matrices, which is useful for enhancing color images. Further, an algorithm is designed and developed for image preprocessing. The validity is verified by using a few real RGB images.

Speed Bump and Pothole Detection Using Deep Neural Network with Images Captured through ZED Camera

The condition of the roads where cars circulate is of the utmost importance to ensure that each autonomous or manual car can complete its journey satisfactorily. The existence of potholes, speed bumps, and other irregularities in the pavement can cause car wear and fatal traffic accidents. Therefore, detecting and characterizing these anomalies helps reduce the risk of accidents and damage to the vehicle. However, street images are naturally multivariate, with redundant and substantial information, as well as significantly contaminated measurement noise, making the detection of street anomalies more challenging. In this work, an automatic color image analysis using a deep neural network for the detection of potholes on the road using images taken by a ZED camera is proposed. A lightweight architecture was designed to speed up training and usage. This consists of seven properly connected and synchronized layers. All the pixels of the original image are used without resizing. The classic stride and pooling operations were used to obtain as much information as possible. A database was built using a ZED camera seated on the front of a car. The routes where the photographs were taken are located in the city of Celaya in Guanajuato, Mexico. Seven hundred and fourteen images were manually tagged, several of which contain bumps and potholes. The system was trained with 70% of the database and validated with the remaining 30%. In addition, we propose a database that discriminates between potholes and speed bumps. A precision of 98.13% using 37 convolution filters in a 3 × 3 window was obtained, which improves upon recent state-of-the-art work.

Physical and mechanical properties of starch films: the role of the cross-linking mechanism through iodine binding capacity

In this study, a better knowledge of the influence of cross-linking mechanism on the mechanical properties of starch films is presented. Thus, waxy starch and cassava starch films, cross-linked with trisodium trimetaphosphate (STMP), were produced and characterized concerning their morphology, transport, and mechanical properties. Starch cross-linking was verified by RAMAN spectroscopy and by iodine binding capacity (IBC) values, which were determined by color analysis of digital images. Although cross-linking affects the morphology and crystallinity of the films, it was not observed a relationship between the mechanism of the cross-linking reaction of the starch chain (amylose-amylopectin and amylopectin-amylopectin) and the transport properties. The lower Young Modulus and IBC value and the higher elongation at break observed for cross-linking cassava starch films relative to control and waxy films indicate that cross-linking mechanism influences the mechanical properties of starch films and should be considered to tailor the final properties of packaging and biobased products.

Network adaptation for color image semantic segmentation

AbstractImage analysis using deep learning has made significant progress in the last few decades, and the importance of pre‐processing input images has become evident. However, adapting a network structure suitable for input images has not been considered. In this study, a simple network adaptation method for color image analysis is described. The method is illustrated using semantic segmentation, which mainly takes a color image as input. The method is inspired by chrominance subsampling, which is a practical method for image and video analysis. The human visual system is less sensitive to color differences than it is to brightness, and based on this phenomenon, it is possible to improve existing networks by providing less resolution to chroma information than luminance information in the network encoder design by applying the group convolution concept. The proposed method helps to achieve improved results without changing the complexity of the baseline network model, and is especially helpful in applications with limited resources, such as autonomous driving, augmented reality. Experiments were performed on a combination of datasets (i.e. CamVid, Cityscapes and KITTI‐360) and networks (i.e. ENet, ERFNet, Deeplabv3plus with mobilenetv2). The results show that the method improves the performance of existing network structures without increasing the number of parameters.