Review of Computer Vision Applications in Fabric Recognition and Color Analysis

Detecting various parameters of woven fabrics is one of the important methods to evaluate the quality of fabrics.In the early stage of industrial development,fabrics were mainly relied on manual to determine the quality,which was inefficient and unstable,so intelligent inspection is a popular development trend today.In recent years,computer vision technology has been widely used in the fields of fabric density measurement,color analysis,and weave pattern recognition.Based on the above three aspects,the advanced research progress of global researchers is reviewed in this paper and the shortcomings of current research and possible research directions in the future are analyzed.Computer vision technology is not only objective evaluation,but also has the advantages of accuracy and efficiency,and has a good development prospect in the field of textiles.

Real-time ore sorting using color and texture analysis

Sensor-based ore sorting is a technology used to classify high-grade mineralized rocks from low-grade waste rocks to reduce operation costs.Many ore-sorting algorithms using color images have been proposed in the past,but only some validate their results using mineral grades or optimize the algorithms to classify rocks in real-time.This paper presents an ore-sorting algorithm based on image processing and machine learning that is able to classify rocks from a gold and silver mine based on their grade.The algorithm is composed of four main stages:(1)image segmentation and partition,(2)color and texture feature extraction,(3)sub-image classification using neural networks,and(4)a voting system to determine the overall class of the rock.The algorithm was trained using images of rocks that a geologist manually classified according to their mineral content and then was validated using a different set of rocks analyzed in a laboratory to determine their gold and silver grades.The proposed method achieved a Matthews correlation coefficient of 0.961 points,higher than other classification algorithms based on support vector machines and convolutional neural networks,and a processing time under 44 ms,promising for real-time ore sorting applications.

Political Optimizer with Deep Learning-Enabled Tongue Color Image Analysis Model

Biomedical image processing is widely utilized for disease detection and classification of biomedical images.Tongue color image analysis is an effective and non-invasive tool for carrying out secondary detection at anytime and anywhere.For removing the qualitative aspect,tongue images are quantitatively inspected,proposing a novel disease classification model in an automated way is preferable.This article introduces a novel political optimizer with deep learning enabled tongue color image analysis(PODL-TCIA)technique.The presented PODL-TCIA model purposes to detect the occurrence of the disease by examining the color of the tongue.To attain this,the PODL-TCIA model initially performs image pre-processing to enhance medical image quality.Followed by,Inception with ResNet-v2 model is employed for feature extraction.Besides,political optimizer(PO)with twin support vector machine(TSVM)model is exploited for image classification process,shows the novelty of the work.The design of PO algorithm assists in the optimal parameter selection of the TSVM model.For ensuring the enhanced outcomes of the PODL-TCIA model,a wide-ranging experimental analysis was applied and the outcomes reported the betterment of the PODL-TCIA model over the recent approaches.

Quality assessment of crude and processed Leonuri Fructus by chemical and color analysis combined with chemometric method

Objective： Crude Leonuri Fructus（CLF）, the fruits of the Leonurus japonicus Houtt, and processed Leonuri Fructus（PLF） by stir-baking as the important Chinese herbal medicines, have been used in China and other Asian countries for thousands of years. The objective of this research is to reveal the difference between CLF and PLF.Methods： The sensory technologies of the colorimetry, sensitive and validated HPLC-ELSD and GC combined with flame ionization detector（GC-FID） were employed to discriminate CLF and its processed product PLF. The color parameters of the samples were determined by colorimetric instrument CR-410. Moreover, the content of stachydrine and six fatty acids were determined by HPLC and GC. Subsequently,analysis of variance（ANOVA）, principal components analysis（PCA）, hierarchical cluster analysis（HCA）,and Kendall＇s correlation test were performed for data analysis.Results： The CLF and PLF were divided into two categories by PCA and HCA in terms of their component content and color. The results distinctly demonstrated significant changes in color and the content of indicative components between CLF and PLF.Conclusion： The study revealed that HPLC, GC, and colorimetric method in combination with chemometric method could be used as comprehensive quality evaluation for CLF and PLF.

Short-duration transient visual evoked potentials and color reflectivity discretization analysis in glaucoma patients and suspects

AIM： To evaluate the use of short-duration transient visual evoked potentials（VEP） and color reflectivity discretization analysis（CORDA） in glaucomatous eyes,eyes suspected of having glaucoma,and healthy eyes.METHODS： The study included 136 eyes from 136 subjects： 49 eyes with glaucoma,45 glaucoma suspect eyes,and 42 healthy eyes.Subjects underwent Humphrey visual field（VF） testing,VEP testing,as well as peripapillary retinal nerve fiber layer optical coherence tomography imaging studies with post-acquisition CORDA applied.Statistical analysis was performed using means and ranges,ANOVA,post-hoc comparisons using Turkey＇s adjustment,Fisher＇s Exact test,area under the curve,and Spearman correlation coefficients.RESULTS： Parameters from VEP and CORDA correlated significantly with VF mean deviation（MD）（P〈0.05）.In distinguishing glaucomatous eyes from controls,VEP demonstrated area under the curve（AUC） values of 0.64-0.75 for amplitude and 0.67-0.81 for latency.The CORDA HR1 parameter was highly discriminative for glaucomatous eyes vs controls（AUC=0.94）.CONCLUSION： Significant correlations are found between MD and parameters of short-duration transient VEP and CORDA,diagnostic modalities which warrant further consideration in identifying glaucoma characteristics.

Analysis of variation in temperature-responsive leaf color mutant lines induced from Gamma irradiation in rice(Oryza sativa L.)

Eight lines of temperature-responsive leaf colormutants induced by applying 300 Gy Gamma-ray irradiation to Thermo-sensitive genic malesterile line 2177s,were obtained through con-tinuous selection in seven generations..Theleaves of these lines started to become greenafter the fourth leaf extension,and except

The Implementation Methods for Computer Aided Design and Analysis on Color-Woven Fabrics

Methods for computer aided design and analysis on color-woven fabrics are presented. The relation of color pattern, colored warp, colored weft and the model for regular weave texture are given based on their formative principle. Some efficient algorithms are developed to determine the color order of warp and weft threads which yields a given color pattern and regular weave texture. Finally, the design system structure and data process flowing are introduced.

Estimation of Low Nutrients in Tomato Crops Through the Analysis of Leaf Images Using Machine Learning

Tomato crops are considered the most important agricultural products worldwide.However,the quality of tomatoes depends mainly on the nutrient levels.Visual inspection is made by farmers to anticipate the nutrient deficiency of the plants.Recently,precision agriculture has explored opportunities to automate nutrient level monitoring.Previous work has demonstrated that a convolutional neural network is able to estimate low nutrients in tomato plants using images of their leaves.However,the performance of the convolutional neural network was not adequate.Thus,this work proposes a novel convolutional neural network-based classifier,namely,CNN+AHN,for estimating low nutrients in tomato crops using an image of the tomato leaves.The CNN+AHN incorporates a set of convolutional layers as the feature extraction part,and a supervised learning method called artificial hydrocarbon network as the dense layer.Different combinations of the architecture of CNN+AHN were examined.Experimental results showed that our best CNN+AHN classifier is able to estimate low nutrients in tomato plants with an accuracy of 95.57%and F1-score of 95.75%,outperforming the literature.

A united optimum images fusion based on analysis of color distortion

In remote sensing community, IHS (intensity, hue, and saturation) transform is one of the most commonly used fusion algorithm. A study on IHS fusion indicates that the color distortion cannot be avoided. Meanwhile, wavelet decomposition has a property of frequency division in transform domain. And the statistical property of wavelet coefficient reflects those significant features. So, a united optimal fusion method, which using the statistical property of wavelet decomposition and IHS transform on pixel and

A New Multistep Approach to Identify Leaf-Off Poplar Plantations Using Airborne Imagery

Identification of type of leafless trees using both fall imagery and field-based surveys is a global concern in the forest science community. Few studies were devoted to separate leafless trees from others in the growth season using remote sensing imagery. But this study was the first attempt to identify the type of leafless tree in the fall imagery. We investigated the potential of the Simple Linear Iterative Clustering (SLIC) and k-mean segmentation techniques, and texture and color image analyses to identify leafless poplar trees using imagery collected in a leaf-off season. For the first time in this study, the star shaped feature identifier was found through a binary image that was successful in identifying leaf-off poplar plantations. Optimal threshold values of Normalized Difference Vegetation Index (NDVI) and Normalized Green Index (NGI) indices were able to differentiate highly vegetated land, green farms, and gardens from the grasses that sometimes grow between poplar plantation lines. A Coefficient of Variation (CV) of red color intensity and histogram of value were also successful in separating bare soil and other land cover types. Imagery was processed and analyzed in a Matlab software. In this study, leafless poplar plantation was identified with a user accuracy of 84% and the overall accuracy was obtained 81.3%. This method provides a framework for identification of leafless poplar trees that may be beneficial for distinguishing other types of leafless trees.

An iterative Wiener filtering method based on the gravity gradient invariants

How to deal with colored noises of GOCE （Gravity field and steady - state Ocean Circulation Explorer） satellite has been the key to data processing. This paper focused on colored noises of GOCE gradient data and the frequency spectrum analysis. According to the analysis results, gravity field model of the optima] degrees 90-240 is given, which is recovered by COCE gradient data. This paper presents an iterative Wiener filtering method based on the gravity gradient invariants. By this method a degree-220 model was calculated from GOCE SGG （Satellite Gravity Gradient） data. The degrees above 90 of ITG2010 were taken as the prior gravity field model, replacing the low degree gravity field model calculated by GOCE orbit data. GOCE gradient colored noises was processed by Wiener filtering. Finally by Wiener filtering iterative calculation, the gravity field model was restored by space-wise harmonic analysis method. The results show that the model＇s accuracy matched well with the ESA＇s （European Space Agency） results by using the same data,

Preprocessing method of night vision image application in apple harvesting robot

Due to the low working efficiency of apple harvesting robots,there is still a long way to go for commercialization.The machine performance and extended operating time are the two research aspects for improving efficiencies of harvesting robots,this study focused on the extended operating time and proposed a round-the-clock operation mode.Due to the influences of light,temperature,humidity,etc.,the working environment at night is relatively complex,and thus restricts the operating efficiency of the apple harvesting robot.Three different artificial light sources(incandescent lamp,fluorescent lamp,and LED lights)were selected for auxiliary light according to certain rules so that the apple night vision images could be captured.In addition,by color analysis,night and natural light images were compared to find out the color characteristics of the night vision images,and intuitive visual and difference image methods were used to analyze the noise characteristics.The results showed that the incandescent lamp is the best artificial auxiliary light for apple harvesting robots working at night,and the type of noise contained in apple night vision images is Gaussian noise mixed with some salt and pepper noise.The preprocessing method can provide a theoretical and technical reference for subsequent image processing.