The optimized color space is searched by using the wavelet scattering network in the KTH_TIPS_COL color image database for image texture classification. The effect of choosing the color space on the classification acc...The optimized color space is searched by using the wavelet scattering network in the KTH_TIPS_COL color image database for image texture classification. The effect of choosing the color space on the classification accuracy is investigated by converting red green blue （RGB） color space to various other color spaces. The results show that the classification performance generally changes to a large degree when performing color texture classification in various color spaces, and the opponent RGB-based wavelet scattering network outperforms other color spaces-based wavelet scattering networks. Considering that color spaces can be changed into each other, therefore, when dealing with the problem of color texture classification, converting other color spaces to the opponent RGB color space is recommended before performing the wavelet scattering network.展开更多
In this paper an evaluation of the influence of luminance L* at the L*a*b* color space during color segmentation is presented. A comparative study is made between the behavior of segmentation in color images using onl...In this paper an evaluation of the influence of luminance L* at the L*a*b* color space during color segmentation is presented. A comparative study is made between the behavior of segmentation in color images using only the Euclidean metric of a* and b* and an adaptive color similarity function defined as a product of Gaussian functions in a modified HSI color space. For the evaluation synthetic images were particularly designed to accurately assess the performance of the color segmentation. The testing system can be used either to explore the behavior of a similarity function (or metric) in different color spaces or to explore different metrics (or similarity functions) in the same color space. From the results is obtained that the color parameters a* and b* are not independent of the luminance parameter L* as one might initially assume.展开更多
In large-scale deer farming image analysis,K-means or maximum between-class variance(Otsu)algorithms can be used to distinguish the deer from the background.However,in an actual breeding environment,the barbed wire or...In large-scale deer farming image analysis,K-means or maximum between-class variance(Otsu)algorithms can be used to distinguish the deer from the background.However,in an actual breeding environment,the barbed wire or chain-link fencing has a certain isolating effect on the deer which greatly interferes with the identification of the individual deer.Also,when the target and background grey values are similar,the multiple background targets cannot be completely separated.To better identify the posture and behaviour of deer in a deer shed,we used digital image processing to separate the deer from the background.To address the problems mentioned above,this paper proposes an adaptive threshold segmentation algorithm based on color space.First,the original image is pre-processed and optimized.On this basis,the data are enhanced and contrasted.Next,color space is used to extract the several backgrounds through various color channels,then the adaptive space segmentation of the extracted part of the color space is performed.Based on the segmentation effect of the traditional Otsu algorithm,we designed a comparative experiment that divided the four postures of turning,getting up,lying,and standing,and successfully separated multiple target deer from the background.Experimental results show that compared with K-means,Otsu and hue saturation value(HSV)+K-means,this method is better in performance and accuracy for adaptive segmentation of deer in artificial breeding scenes and can be used to separate artificially cultivated deer from their backgrounds.Both the subjective and objective aspects achieved good segmentation results.This article lays a foundation for the effective identification of abnormal behaviour in sika deer.展开更多
A support vector regression(SVR) based color image restoration algorithm is proposed.The test color images are firstly mapped into the YUV color space,and then SVR is applied to build up a theoretical model between th...A support vector regression(SVR) based color image restoration algorithm is proposed.The test color images are firstly mapped into the YUV color space,and then SVR is applied to build up a theoretical model between the degraded images and the original one.Performance comparisons of the proposed algorithm versus traditional filtering algorithms are given.Experimental results show that the proposed algorithm has better performance than traditional filtering algorithms and has less computation time than iterative blind deconvolution algorithm.展开更多
Machine vision has been recently utilized for quality control of food and agricultural products, which was traditionally done by manual inspection. The present study was an attempt for automatic defect detection and s...Machine vision has been recently utilized for quality control of food and agricultural products, which was traditionally done by manual inspection. The present study was an attempt for automatic defect detection and sorting of some single-color fruits such as banana and plum. Fruit images were captured using a color digital camera with capturing direction of zero degree and under illuminant D65. It was observed that growing decay and time-aging made surface color changes in bruised parts of the object. 3D RGB and HSV color vectors as well as a single channel like H （hue）, S （saturation）, V （value） and grey scale images were applied for color quantization of the object. Results showed that there was a distinct threshold in the histogram of the S channel of images which can be applied to separate the object from its background. Moreover, the color change via the defect and time-aging is correctly distinguishable in the hue channel image. The effect of illumination, gloss and shadow of 3D image processing is less noticeable for hue data in comparison to saturation and value. The value of H channel was quantized to five groups based on the difference between each pixel value and the H value of a healthy object. The percentage of different degree of defects can be computed and used for grading the fruits.展开更多
A method based on HSI color space is presented to solve the problem of circle detection from color images. In terms of the evaluation to the edge detection method based on intensity, the edge detection based on hue is...A method based on HSI color space is presented to solve the problem of circle detection from color images. In terms of the evaluation to the edge detection method based on intensity, the edge detection based on hue is chosen to process the color image, and the simplified calculation of hue transform is discussed. Then the algorithm of circle detection based on Canny edge detection is proposed. Due to the dispersive distribution of the detected result, Hough transformation and template smooth are used in circle detection, and the proposed method gives a quite good result.展开更多
Colors of textile materials are the first parameter of quality evaluated by consumers and a key component considered in selecting printed fabric. In the textiles industry, digital printed fabric analysis is one of the...Colors of textile materials are the first parameter of quality evaluated by consumers and a key component considered in selecting printed fabric. In the textiles industry, digital printed fabric analysis is one of the basic elements in successfully utilizing a color mechanism scheme and objectively evaluating fabric color alterations. Precise color measurement, however, is mostly used in sample analysis and quality inspection which help to produce reproducible or similar product. It is important that for quality inspection, the color of the product should be measured as a necessary requirement of quality control whether the product is to be accepted or not. Presented in this study is an unsupervised segmentation of printed fabrics patterns using mean shift algorithm and color measurements over the segmented regions of printed fabric patterns. The results established a consistent and reliable color measurement of multiple color patterns and appearance with the established range without any interactions.展开更多
Quantitative imaging of luminescent signals,ranging from electrochemiluminescence(ECL)and chemiluminescence to colorimetric assays,is increasingly performed using consumer-grade digital cameras and smartphones.However...Quantitative imaging of luminescent signals,ranging from electrochemiluminescence(ECL)and chemiluminescence to colorimetric assays,is increasingly performed using consumer-grade digital cameras and smartphones.However,device-dependent variability,nonlinear signal encoding,and the absence of standardized workflows hinder reproducibility and quantification accuracy.This work presents a generalized methodology for robust signal quantification in luminescent systems using digital imaging,with ECL as a model case.By combining synchronized electrochemical control,manual optimization of imaging parameters,gamma correction,and color space transformations,accurate device-independent analysis is enabled.Using Ru(bpy)_(3)^(2+)/TPrA as a test system,we evaluate RGB,CIEXYZ,and CIELAB color spaces,identifying optimal channels for sensitivity and dynamic range.Our performance assessment underscores the importance of transfer function selection and supports both linear and nonlinear quantification models.Results show that linearized r and X color channels offer broad dynamic ranges with moderate sensitivity,while encoded R and a*channels provide higher sensitivity at low concentrations,requiring nonlinear modeling to extend their quantification range.This scalable approach enables standardized,high-throughput optical analysis using low-cost camera platforms,with broad applications in diagnostics,biosensing,and analytical chemistry.展开更多
Although Convolutional Neural Networks(CNNs)have achieved remarkable success in image classification,most CNNs use image datasets in the Red-Green-Blue(RGB)color space(one of the most commonly used color spaces).The e...Although Convolutional Neural Networks(CNNs)have achieved remarkable success in image classification,most CNNs use image datasets in the Red-Green-Blue(RGB)color space(one of the most commonly used color spaces).The existing literature regarding the influence of color space use on the performance of CNNs is limited.This paper explores the impact of different color spaces on image classification using CNNs.We compare the performance of five CNN models with different convolution operations and numbers of layers on four image datasets,each converted to nine color spaces.We find that color space selection can significantly affect classification accuracy,and that some classes are more sensitive to color space changes than others.Different color spaces may have different expression abilities for different image features,such as brightness,saturation,hue,etc.To leverage the complementary information from different color spaces,we propose a pseudo-Siamese network that fuses two color spaces without modifying the network architecture.Our experiments show that our proposed model can outperform the single-color-space models on most datasets.We also find that our method is simple,flexible,and compatible with any CNN and image dataset.展开更多
Recognition and counting of greenhouse pests are important for monitoring and forecasting pest population dynamics.This study used image processing techniques to recognize and count whiteflies and thrips on a sticky t...Recognition and counting of greenhouse pests are important for monitoring and forecasting pest population dynamics.This study used image processing techniques to recognize and count whiteflies and thrips on a sticky trap located in a greenhouse environment.The digital images of sticky traps were collected using an image-acquisition system under different greenhouse conditions.If a single color space is used,it is difficult to segment the small pests correctly because of the detrimental effects of non-uniform illumination in complex scenarios.Therefore,a method that first segments object pests in two color spaces using the Prewitt operator in I component of the hue-saturation-intensity(HSI)color space and the Canny operator in the B component of the Lab color space was proposed.Then,the segmented results for the two-color spaces were summed and achieved 91.57%segmentation accuracy.Next,because different features of pests contribute differently to the classification of pest species,the study extracted multiple features(e.g.,color and shape features)in different color spaces for each segmented pest region to improve the recognition performance.Twenty decision trees were used to form a strong ensemble learning classifier that used a majority voting mechanism and obtains 95.73%recognition accuracy.The proposed method is a feasible and effective way to process greenhouse pest images.The system accurately recognized and counted pests in sticky trap images captured under real greenhouse conditions.展开更多
Objective To analyze the characteristics of tongue imaging color parameters in patients treated with percutaneous coronary intervention(PCI)and non-PCI for coronary atherosclerotic heart disease(CHD),and to observethe...Objective To analyze the characteristics of tongue imaging color parameters in patients treated with percutaneous coronary intervention(PCI)and non-PCI for coronary atherosclerotic heart disease(CHD),and to observethe effects of PCI on the tongue images of patients as a basis for the clinical diagnosis and treatment of patientswith CHD.Methods This study used a retrospective cross-sectional survey to analyze tongue photographs and medicalhistory information from 204 patients with CHD between November 2018 and July 2020.Tongue images ofeach subject were obtained using the Z-BOX Series traditional Chinese medicine(TCM)intelligent diagnosisinstruments,the SMX System 2.0 was used to transform the image data into parameters in the HSV color space,and finally the parameters of the tongue image between patients in the PCI-treated and non-PCI-treated groupsfor CHD were analyzed.Results Among the 204 patients,112 were in the non-PCI treatment group(38 men and 74 women;average age of(68.76±9.49)years),92 were in the PCI treatment group(66 men and 26 women;average age of(66.02±10.22)years).In the PCI treatment group,the H values of the middle and tip of the tongue and the overall coating of thetongue were lower(P<0.05),while the V values of the middle,tip,both sides of the tongue,the whole tongueand the overall coating of the tongue were higher(P<0.05).Conclusion The color parameters of the tongue image could reflect the physical state of patients treated withPCI,which may provide a basis for the clinical diagnosis and treatment of patients with CHD.展开更多
Image segmentation, as a basic building block for many high-level image analysis problems, has attracted many research attentions over years. Existing approaches, however, are mainly focusing on the clustering analysi...Image segmentation, as a basic building block for many high-level image analysis problems, has attracted many research attentions over years. Existing approaches, however, are mainly focusing on the clustering analysis in the single channel information, i.e. , either in color or spatial space, which may lead to unsatisfactory segmentation performance. Considering the spatial and color spaces jointly, this paper propases a new hierarchical image segmentation algorithm, which alternately cluster.s the image regions in color and spatial spaces in a fine to coarse manner. Without losing the perceptual consistence, the proposed algorithm achieves the segmentation result using only very few number of colors according to user specification.展开更多
The identification of targets varies in different surge tests. A multi-color space threshold segmentation and self-learning k-nearest neighbor algorithm （k-NN） for equipment under test status identification was prop...The identification of targets varies in different surge tests. A multi-color space threshold segmentation and self-learning k-nearest neighbor algorithm （k-NN） for equipment under test status identification was proposed after using feature matching to identify equipment status had to train new patterns every time before testing. First, color space （L＊a＊b＊, hue saturation lightness （HSL）, hue saturation value （HSV）） to segment was selected according to the high luminance points ratio and white luminance points ratio of the image. Second, the unknown class sample Sr was classified by the k-NN algorithm with training set T~ according to the feature vector, which was formed from number ofpixels, eccentricity ratio, compact- ness ratio, and Euler＇s numbers. Last, while the classification confidence coefficient equaled k, made Sr as one sample ofpre-training set Tz＇. The training set Tz increased to Tz＋1 by Tz＇ if Tz＇ was saturated. In nine series of illuminant, indicator light, screen, and disturbances samples （a total of 21600 frames）, the algorithm had a 98.65% identification accuracy, also selected five groups of samples to enlarge the training set from To to T5 by itself. Keywords multi-color space, k-nearest neighbor algorithm （k-NN）, self-learning, surge test展开更多
To transfer the color data from a device (video camera) dependent color space into a device? independent color space, a multilayer feedforward network with the error backpropagation (BP) learning rule, was regarded ...To transfer the color data from a device (video camera) dependent color space into a device? independent color space, a multilayer feedforward network with the error backpropagation (BP) learning rule, was regarded as a nonlinear transformer realizing the mapping from the RGB color space to CIELAB color space. A variety of mapping accuracy were obtained with different network structures. BP neural networks can provide a satisfactory mapping accuracy in the field of color space transformation for video cameras.展开更多
To deduce a new color difference formula based on CIE 1997 Color Appearance Model(CIECAM97s), a color space J a 1 b 1 is first constructed with color appearance descriptors J,a,b in CIECAM97s. The new f...To deduce a new color difference formula based on CIE 1997 Color Appearance Model(CIECAM97s), a color space J a 1 b 1 is first constructed with color appearance descriptors J,a,b in CIECAM97s. The new formula is then deduced in the space and named CDF CIECAM97s. The factors for lightness, chroma and hue correction in the formula are derived by linear regression according to BFD? CP data sets. It is found by statistical analysis that CDF CIECAM97s is in closer accordance with the visual assessments when compared with CMC(1∶1), CIE94 and CIE L *a *b * color difference formulae. Based on color appearance model, the new color difference formula can be used to predict color difference perception in a varity of different viewing conditions.展开更多
Green,energy conservation and environmental protection have increasingly become the theme of the sustained and healthy development of cities against the background of new urbanization,which indicates that the problem ...Green,energy conservation and environmental protection have increasingly become the theme of the sustained and healthy development of cities against the background of new urbanization,which indicates that the problem of building energy consumption has received growing attention.This paper explores the impact of energy-saving decorations in flexible interior space on energy-saving effect of buildings so as to broaden the horizon of energy conservation in building,thereby alleviating the problem of energy shortage in China.展开更多
Conventional color-printing systems often use inks of three hues, such as CMY, CMYK and CMYKLcLm, but in order to obtain more realistic color reproductions, the ink set of more than three hues has been adopted by some...Conventional color-printing systems often use inks of three hues, such as CMY, CMYK and CMYKLcLm, but in order to obtain more realistic color reproductions, the ink set of more than three hues has been adopted by some color-printing systems. It is difficult, however, to model the composed color with the multiple inks when the number of the output ink hues exceeds three due to the none-unique mapping between the color spaces of the CIE Lab and the multi-color printing device. In this paper, we propose a fine color-printing method for multi-color printing device with the ink set of more than three hues. The proposed approach has good color expression ability and provides fine control of the printed color. By dividing the output color space into several subspaces, our method allows one-to-one mapping between the standard color space and the multi-color output color space. It has been proved effective when applied to the digital inkjet printer—Mutoh8000.展开更多
Capturing leaf color variances over space is important for diagnosing plant nutrient and health status,estimating water availability as well as improving ornamental and tourism values of plants.In this study,leaf colo...Capturing leaf color variances over space is important for diagnosing plant nutrient and health status,estimating water availability as well as improving ornamental and tourism values of plants.In this study,leaf color variances of the Eurasian smoke tree,Cotinus coggygria were estimated based on geographic and climate variables in a shrub community using generalized elastic net(GELnet)and support vector machine(SVM)algorithms.Results reveal that leaf color varied over space,and the variances were the result of geography due to its effect on solar radiation,temperature,illumination and moisture of the shrub environment,whereas the influence of climate were not obvious.The SVM and GELnet algorithm models were similar estimating leaf color indices based on geographic variables,and demonstrates that both techniques have the potential to estimate leaf color variances of C.coggygria in a shrubbery with a complex geographical environment in the absence of human activity.展开更多
Soil water content(SWC)is one of the critical indicators in various fields such as geotechnical engineering and agriculture.To avoid the time-consuming,destructive,and laborious drawbacks of conventional SWC measureme...Soil water content(SWC)is one of the critical indicators in various fields such as geotechnical engineering and agriculture.To avoid the time-consuming,destructive,and laborious drawbacks of conventional SWC measurements,the image-based SWC prediction is considered based on recent advances in quantitative soil color analysis.In this study,a promising method based on the Gaussian-fitting gray histogram is proposed for extracting characteristic parameters by analyzing soil images,aiming to alleviate the interference of complex surface conditions with color information extraction.In addition,an identity matrix consisting of 32 characteristic parameters from eight color spaces is constituted to describe the multi-dimensional information of the soil images.Meanwhile,a subset of 10 parameters is identified through three variable analytical methods.Then,four machine learning models for SWC prediction based on partial least squares regression(PLSR),random forest(RF),support vector machines regression(SVMR),and Gaussian process regression(GPR),are established using 32 and 10 characteristic parameters,and their performance is compared.The results show that the characteristic parameters obtained by Gaussian-fitting can effectively reduce the interference from soil surface conditions.The RGB,CIEXYZ,and CIELCH color spaces and lightness parameters,as the inputs,are more suitable for the SWC prediction models.Furthermore,it is found that 10 parameters could also serve as optimal and generalizable predictors without considerably reducing prediction accuracy,and the GPR model has the best prediction performance(R^(2)≥0.95,RMSE≤2.01%,RPD≥4.95,and RPIQ≥6.37).The proposed image-based SWC predictive models combined with effective color information and machine learning can achieve a transient and highly precise SWC prediction,providing valuable insights for mapping soil moisture fields.展开更多
基金The National Basic Research Program of China(No.2011CB707904)the National Natural Science Foundation of China(No.61201344,61271312,11301074)+1 种基金the Natural Science Foundation of Jiangsu Province(No.BK2012329)the Specialized Research Fund for the Doctoral Program of Higher Education(No.20110092110023,20120092120036)
文摘The optimized color space is searched by using the wavelet scattering network in the KTH_TIPS_COL color image database for image texture classification. The effect of choosing the color space on the classification accuracy is investigated by converting red green blue （RGB） color space to various other color spaces. The results show that the classification performance generally changes to a large degree when performing color texture classification in various color spaces, and the opponent RGB-based wavelet scattering network outperforms other color spaces-based wavelet scattering networks. Considering that color spaces can be changed into each other, therefore, when dealing with the problem of color texture classification, converting other color spaces to the opponent RGB color space is recommended before performing the wavelet scattering network.
文摘In this paper an evaluation of the influence of luminance L* at the L*a*b* color space during color segmentation is presented. A comparative study is made between the behavior of segmentation in color images using only the Euclidean metric of a* and b* and an adaptive color similarity function defined as a product of Gaussian functions in a modified HSI color space. For the evaluation synthetic images were particularly designed to accurately assess the performance of the color segmentation. The testing system can be used either to explore the behavior of a similarity function (or metric) in different color spaces or to explore different metrics (or similarity functions) in the same color space. From the results is obtained that the color parameters a* and b* are not independent of the luminance parameter L* as one might initially assume.
基金This research was supported by The People’s Republic of China Ministry of Science and Technology[2018YFF0213606-03(Mu Y.,Hu T.L.,Gong H.,Li S.J.and Sun Y.H.)http://www.most.gov.cn]the Science and Technology Department of Jilin Province[20160623016TC,20170204017NY,20170204038NY(Hu T.L.,Gong H.and Li S.J.)http://kjt.jl.gov.cn],and the ScienceTechnology Bureau of Changchun City[18DY021(Mu Y.,Hu T.L.,Gong H.,and Sun Y.H.)http://kjj.changchun.gov.cn].
文摘In large-scale deer farming image analysis,K-means or maximum between-class variance(Otsu)algorithms can be used to distinguish the deer from the background.However,in an actual breeding environment,the barbed wire or chain-link fencing has a certain isolating effect on the deer which greatly interferes with the identification of the individual deer.Also,when the target and background grey values are similar,the multiple background targets cannot be completely separated.To better identify the posture and behaviour of deer in a deer shed,we used digital image processing to separate the deer from the background.To address the problems mentioned above,this paper proposes an adaptive threshold segmentation algorithm based on color space.First,the original image is pre-processed and optimized.On this basis,the data are enhanced and contrasted.Next,color space is used to extract the several backgrounds through various color channels,then the adaptive space segmentation of the extracted part of the color space is performed.Based on the segmentation effect of the traditional Otsu algorithm,we designed a comparative experiment that divided the four postures of turning,getting up,lying,and standing,and successfully separated multiple target deer from the background.Experimental results show that compared with K-means,Otsu and hue saturation value(HSV)+K-means,this method is better in performance and accuracy for adaptive segmentation of deer in artificial breeding scenes and can be used to separate artificially cultivated deer from their backgrounds.Both the subjective and objective aspects achieved good segmentation results.This article lays a foundation for the effective identification of abnormal behaviour in sika deer.
基金the National Natural Science Foundation of China (No. 60675023)
文摘A support vector regression(SVR) based color image restoration algorithm is proposed.The test color images are firstly mapped into the YUV color space,and then SVR is applied to build up a theoretical model between the degraded images and the original one.Performance comparisons of the proposed algorithm versus traditional filtering algorithms are given.Experimental results show that the proposed algorithm has better performance than traditional filtering algorithms and has less computation time than iterative blind deconvolution algorithm.
文摘Machine vision has been recently utilized for quality control of food and agricultural products, which was traditionally done by manual inspection. The present study was an attempt for automatic defect detection and sorting of some single-color fruits such as banana and plum. Fruit images were captured using a color digital camera with capturing direction of zero degree and under illuminant D65. It was observed that growing decay and time-aging made surface color changes in bruised parts of the object. 3D RGB and HSV color vectors as well as a single channel like H （hue）, S （saturation）, V （value） and grey scale images were applied for color quantization of the object. Results showed that there was a distinct threshold in the histogram of the S channel of images which can be applied to separate the object from its background. Moreover, the color change via the defect and time-aging is correctly distinguishable in the hue channel image. The effect of illumination, gloss and shadow of 3D image processing is less noticeable for hue data in comparison to saturation and value. The value of H channel was quantized to five groups based on the difference between each pixel value and the H value of a healthy object. The percentage of different degree of defects can be computed and used for grading the fruits.
文摘A method based on HSI color space is presented to solve the problem of circle detection from color images. In terms of the evaluation to the edge detection method based on intensity, the edge detection based on hue is chosen to process the color image, and the simplified calculation of hue transform is discussed. Then the algorithm of circle detection based on Canny edge detection is proposed. Due to the dispersive distribution of the detected result, Hough transformation and template smooth are used in circle detection, and the proposed method gives a quite good result.
文摘Colors of textile materials are the first parameter of quality evaluated by consumers and a key component considered in selecting printed fabric. In the textiles industry, digital printed fabric analysis is one of the basic elements in successfully utilizing a color mechanism scheme and objectively evaluating fabric color alterations. Precise color measurement, however, is mostly used in sample analysis and quality inspection which help to produce reproducible or similar product. It is important that for quality inspection, the color of the product should be measured as a necessary requirement of quality control whether the product is to be accepted or not. Presented in this study is an unsupervised segmentation of printed fabrics patterns using mean shift algorithm and color measurements over the segmented regions of printed fabric patterns. The results established a consistent and reliable color measurement of multiple color patterns and appearance with the established range without any interactions.
文摘Quantitative imaging of luminescent signals,ranging from electrochemiluminescence(ECL)and chemiluminescence to colorimetric assays,is increasingly performed using consumer-grade digital cameras and smartphones.However,device-dependent variability,nonlinear signal encoding,and the absence of standardized workflows hinder reproducibility and quantification accuracy.This work presents a generalized methodology for robust signal quantification in luminescent systems using digital imaging,with ECL as a model case.By combining synchronized electrochemical control,manual optimization of imaging parameters,gamma correction,and color space transformations,accurate device-independent analysis is enabled.Using Ru(bpy)_(3)^(2+)/TPrA as a test system,we evaluate RGB,CIEXYZ,and CIELAB color spaces,identifying optimal channels for sensitivity and dynamic range.Our performance assessment underscores the importance of transfer function selection and supports both linear and nonlinear quantification models.Results show that linearized r and X color channels offer broad dynamic ranges with moderate sensitivity,while encoded R and a*channels provide higher sensitivity at low concentrations,requiring nonlinear modeling to extend their quantification range.This scalable approach enables standardized,high-throughput optical analysis using low-cost camera platforms,with broad applications in diagnostics,biosensing,and analytical chemistry.
基金supported by the Science and Technology Development Fund of Macao,Macao SAR(Nos.0021/2023/RIA1 and 0046/2021/A)the Faculty Research Grant of Macao University of Science and Technology(No.FRG-22-103-FIE)supported by the National Natural Science Foundation of China(Nos.61872167 and 61502205).
文摘Although Convolutional Neural Networks(CNNs)have achieved remarkable success in image classification,most CNNs use image datasets in the Red-Green-Blue(RGB)color space(one of the most commonly used color spaces).The existing literature regarding the influence of color space use on the performance of CNNs is limited.This paper explores the impact of different color spaces on image classification using CNNs.We compare the performance of five CNN models with different convolution operations and numbers of layers on four image datasets,each converted to nine color spaces.We find that color space selection can significantly affect classification accuracy,and that some classes are more sensitive to color space changes than others.Different color spaces may have different expression abilities for different image features,such as brightness,saturation,hue,etc.To leverage the complementary information from different color spaces,we propose a pseudo-Siamese network that fuses two color spaces without modifying the network architecture.Our experiments show that our proposed model can outperform the single-color-space models on most datasets.We also find that our method is simple,flexible,and compatible with any CNN and image dataset.
基金This work was financially supported by the National Natural Science Foundation of China(Grant No.61601034)and the National Natural Science Foundation of China(Grant No.31871525)The authors acknowledge Kimberly Moravec,PhD,from Liwen Bianji,Edanz Editing China(www.liwenbianji.cn/ac),for editing the English text of a draft of this manuscript.
文摘Recognition and counting of greenhouse pests are important for monitoring and forecasting pest population dynamics.This study used image processing techniques to recognize and count whiteflies and thrips on a sticky trap located in a greenhouse environment.The digital images of sticky traps were collected using an image-acquisition system under different greenhouse conditions.If a single color space is used,it is difficult to segment the small pests correctly because of the detrimental effects of non-uniform illumination in complex scenarios.Therefore,a method that first segments object pests in two color spaces using the Prewitt operator in I component of the hue-saturation-intensity(HSI)color space and the Canny operator in the B component of the Lab color space was proposed.Then,the segmented results for the two-color spaces were summed and achieved 91.57%segmentation accuracy.Next,because different features of pests contribute differently to the classification of pest species,the study extracted multiple features(e.g.,color and shape features)in different color spaces for each segmented pest region to improve the recognition performance.Twenty decision trees were used to form a strong ensemble learning classifier that used a majority voting mechanism and obtains 95.73%recognition accuracy.The proposed method is a feasible and effective way to process greenhouse pest images.The system accurately recognized and counted pests in sticky trap images captured under real greenhouse conditions.
基金This study was supported by the National Natural Science Foundation of China(Grant No.82074333)Shanghai TCM Science and Technology Innovation Program(Grant No.ZYKC201701017)Shanghai Key Laboratory of Health Identification and Assessment(Grant No.21DZ2271000).
文摘Objective To analyze the characteristics of tongue imaging color parameters in patients treated with percutaneous coronary intervention(PCI)and non-PCI for coronary atherosclerotic heart disease(CHD),and to observethe effects of PCI on the tongue images of patients as a basis for the clinical diagnosis and treatment of patientswith CHD.Methods This study used a retrospective cross-sectional survey to analyze tongue photographs and medicalhistory information from 204 patients with CHD between November 2018 and July 2020.Tongue images ofeach subject were obtained using the Z-BOX Series traditional Chinese medicine(TCM)intelligent diagnosisinstruments,the SMX System 2.0 was used to transform the image data into parameters in the HSV color space,and finally the parameters of the tongue image between patients in the PCI-treated and non-PCI-treated groupsfor CHD were analyzed.Results Among the 204 patients,112 were in the non-PCI treatment group(38 men and 74 women;average age of(68.76±9.49)years),92 were in the PCI treatment group(66 men and 26 women;average age of(66.02±10.22)years).In the PCI treatment group,the H values of the middle and tip of the tongue and the overall coating of thetongue were lower(P<0.05),while the V values of the middle,tip,both sides of the tongue,the whole tongueand the overall coating of the tongue were higher(P<0.05).Conclusion The color parameters of the tongue image could reflect the physical state of patients treated withPCI,which may provide a basis for the clinical diagnosis and treatment of patients with CHD.
文摘Image segmentation, as a basic building block for many high-level image analysis problems, has attracted many research attentions over years. Existing approaches, however, are mainly focusing on the clustering analysis in the single channel information, i.e. , either in color or spatial space, which may lead to unsatisfactory segmentation performance. Considering the spatial and color spaces jointly, this paper propases a new hierarchical image segmentation algorithm, which alternately cluster.s the image regions in color and spatial spaces in a fine to coarse manner. Without losing the perceptual consistence, the proposed algorithm achieves the segmentation result using only very few number of colors according to user specification.
文摘The identification of targets varies in different surge tests. A multi-color space threshold segmentation and self-learning k-nearest neighbor algorithm （k-NN） for equipment under test status identification was proposed after using feature matching to identify equipment status had to train new patterns every time before testing. First, color space （L＊a＊b＊, hue saturation lightness （HSL）, hue saturation value （HSV）） to segment was selected according to the high luminance points ratio and white luminance points ratio of the image. Second, the unknown class sample Sr was classified by the k-NN algorithm with training set T~ according to the feature vector, which was formed from number ofpixels, eccentricity ratio, compact- ness ratio, and Euler＇s numbers. Last, while the classification confidence coefficient equaled k, made Sr as one sample ofpre-training set Tz＇. The training set Tz increased to Tz＋1 by Tz＇ if Tz＇ was saturated. In nine series of illuminant, indicator light, screen, and disturbances samples （a total of 21600 frames）, the algorithm had a 98.65% identification accuracy, also selected five groups of samples to enlarge the training set from To to T5 by itself. Keywords multi-color space, k-nearest neighbor algorithm （k-NN）, self-learning, surge test
文摘To transfer the color data from a device (video camera) dependent color space into a device? independent color space, a multilayer feedforward network with the error backpropagation (BP) learning rule, was regarded as a nonlinear transformer realizing the mapping from the RGB color space to CIELAB color space. A variety of mapping accuracy were obtained with different network structures. BP neural networks can provide a satisfactory mapping accuracy in the field of color space transformation for video cameras.
文摘To deduce a new color difference formula based on CIE 1997 Color Appearance Model(CIECAM97s), a color space J a 1 b 1 is first constructed with color appearance descriptors J,a,b in CIECAM97s. The new formula is then deduced in the space and named CDF CIECAM97s. The factors for lightness, chroma and hue correction in the formula are derived by linear regression according to BFD? CP data sets. It is found by statistical analysis that CDF CIECAM97s is in closer accordance with the visual assessments when compared with CMC(1∶1), CIE94 and CIE L *a *b * color difference formulae. Based on color appearance model, the new color difference formula can be used to predict color difference perception in a varity of different viewing conditions.
基金Sponsored by Education Science Project of the 13th Five-Year Plan of Jiangxi Province(16YB041)
文摘Green,energy conservation and environmental protection have increasingly become the theme of the sustained and healthy development of cities against the background of new urbanization,which indicates that the problem of building energy consumption has received growing attention.This paper explores the impact of energy-saving decorations in flexible interior space on energy-saving effect of buildings so as to broaden the horizon of energy conservation in building,thereby alleviating the problem of energy shortage in China.
基金Project (No. M603034) supported by the Natural Science Foundationof Zhejiang Province, China
文摘Conventional color-printing systems often use inks of three hues, such as CMY, CMYK and CMYKLcLm, but in order to obtain more realistic color reproductions, the ink set of more than three hues has been adopted by some color-printing systems. It is difficult, however, to model the composed color with the multiple inks when the number of the output ink hues exceeds three due to the none-unique mapping between the color spaces of the CIE Lab and the multi-color printing device. In this paper, we propose a fine color-printing method for multi-color printing device with the ink set of more than three hues. The proposed approach has good color expression ability and provides fine control of the printed color. By dividing the output color space into several subspaces, our method allows one-to-one mapping between the standard color space and the multi-color output color space. It has been proved effective when applied to the digital inkjet printer—Mutoh8000.
基金supported by the Fundamental Research Funds for the Central Universities(Grant No.XDJK2019D041)the Research Innovation Programs for graduate student of Chongqing,China(Grant No.CYS19123)the National Undergraduate Innovation and Entrepreneurship Training Programs(Grant No.201810635015).
文摘Capturing leaf color variances over space is important for diagnosing plant nutrient and health status,estimating water availability as well as improving ornamental and tourism values of plants.In this study,leaf color variances of the Eurasian smoke tree,Cotinus coggygria were estimated based on geographic and climate variables in a shrub community using generalized elastic net(GELnet)and support vector machine(SVM)algorithms.Results reveal that leaf color varied over space,and the variances were the result of geography due to its effect on solar radiation,temperature,illumination and moisture of the shrub environment,whereas the influence of climate were not obvious.The SVM and GELnet algorithm models were similar estimating leaf color indices based on geographic variables,and demonstrates that both techniques have the potential to estimate leaf color variances of C.coggygria in a shrubbery with a complex geographical environment in the absence of human activity.
文摘Soil water content(SWC)is one of the critical indicators in various fields such as geotechnical engineering and agriculture.To avoid the time-consuming,destructive,and laborious drawbacks of conventional SWC measurements,the image-based SWC prediction is considered based on recent advances in quantitative soil color analysis.In this study,a promising method based on the Gaussian-fitting gray histogram is proposed for extracting characteristic parameters by analyzing soil images,aiming to alleviate the interference of complex surface conditions with color information extraction.In addition,an identity matrix consisting of 32 characteristic parameters from eight color spaces is constituted to describe the multi-dimensional information of the soil images.Meanwhile,a subset of 10 parameters is identified through three variable analytical methods.Then,four machine learning models for SWC prediction based on partial least squares regression(PLSR),random forest(RF),support vector machines regression(SVMR),and Gaussian process regression(GPR),are established using 32 and 10 characteristic parameters,and their performance is compared.The results show that the characteristic parameters obtained by Gaussian-fitting can effectively reduce the interference from soil surface conditions.The RGB,CIEXYZ,and CIELCH color spaces and lightness parameters,as the inputs,are more suitable for the SWC prediction models.Furthermore,it is found that 10 parameters could also serve as optimal and generalizable predictors without considerably reducing prediction accuracy,and the GPR model has the best prediction performance(R^(2)≥0.95,RMSE≤2.01%,RPD≥4.95,and RPIQ≥6.37).The proposed image-based SWC predictive models combined with effective color information and machine learning can achieve a transient and highly precise SWC prediction,providing valuable insights for mapping soil moisture fields.
