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
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
Partitional clustering techniques such as K-Means(KM),Fuzzy C-Means(FCM),and Rough K-Means(RKM)are very simple and effective techniques for image segmentation.But,because their initial cluster centers are randomly det...Partitional clustering techniques such as K-Means(KM),Fuzzy C-Means(FCM),and Rough K-Means(RKM)are very simple and effective techniques for image segmentation.But,because their initial cluster centers are randomly determined,it is often seen that certain clusters converge to local optima.In addition to that,pathology image segmentation is also problematic due to uneven lighting,stain,and camera settings during the microscopic image capturing process.Therefore,this study proposes an Improved Slime Mould Algorithm(ISMA)based on opposition based learning and differential evolution’s mutation strategy to perform illumination-free White Blood Cell(WBC)segmentation.The ISMA helps to overcome the local optima trapping problem of the partitional clustering techniques to some extent.This paper also performs a depth analysis by considering only color components of many well-known color spaces for clustering to find the effect of illumination over color pathology image clustering.Numerical and visual results encourage the utilization of illumination-free or color component-based clustering approaches for image segmentation.ISMA-KM and“ab”color channels of CIELab color space provide best results with above-99%accuracy for only nucleus segmentation.Whereas,for entire WBC segmentation,ISMA-KM and the“CbCr”color component of YCbCr color space provide the best results with an accuracy of above 99%.Furthermore,ISMA-KM and ISMA-RKM have the lowest and highest execution times,respectively.On the other hand,ISMA provides competitive outcomes over CEC2019 benchmark test functions compared to recent well-established and efficient Nature-Inspired Optimization Algorithms(NIOAs).展开更多
Microscopic halftone image recognition and analysis can provide quantitative evidence for printing quality control and fault diagnosis of printing devices, while halftone image segmentation is one of the significant s...Microscopic halftone image recognition and analysis can provide quantitative evidence for printing quality control and fault diagnosis of printing devices, while halftone image segmentation is one of the significant steps during the procedure. Automatic segmentation on microscopic dots by the aid of the Fuzzy C-Means (FCM) method that takes account of the fuzziness of halftone image and utilizes its color information adequately is realized. Then some examples show the technique effective and simple with better performance of noise immunity than some usual methods. In addition, the segmentation results obtained by the FCM in different color spaces are compared, which indicates that the method using the FCM in the f 1f 2f 3 color space is superior to the rest.展开更多
Color image segmentation is crucial in image processing and computer vision.Most traditional segmentation methods simply regard an RGB color image as the direct combination of the three monochrome images and ignore th...Color image segmentation is crucial in image processing and computer vision.Most traditional segmentation methods simply regard an RGB color image as the direct combination of the three monochrome images and ignore the inherent color structures within channels,which contain some key feature information of the image.To better describe the relationship of color channels,we introduce a quaternion-based regularization that can reflect the image characteristics more intuitively.Our model combines the idea of the Mumford-Shah model-based two-stage segmentation method and the Saturation-Value Total Variation regularization for color image segmentation.The new strategy first extracts features from the color image and then subdivides the image in a new color feature space which achieves better performance than methods in RGB color space.Moreover,to accelerate the optimization process,we use a new primal-dual algorithm to solve our novel model.Numerical results demonstrate clearly that the performance of our proposed method is excellent.展开更多
Through the analysis and comparison of shortcomings and advantages of existing technologies on object modeling in 3D applications,we propose a new modeling method for virtual scene based on multi-view image sequence t...Through the analysis and comparison of shortcomings and advantages of existing technologies on object modeling in 3D applications,we propose a new modeling method for virtual scene based on multi-view image sequence to model irregular objects efficiently in 3D application.In 3D scene,this method can get better visual effect by tracking the viewer's real-time perspective position and projecting the photos from different perspectives dynamically.The philosophy of design,the steps of development and some other relevant topics are discussed in details,and the validity of the algorithm is analyzed.The results demonstrate that this method represents more superiority on simulating irregular objects by applying it to the modeling of virtual museum.展开更多
茶毫是红茶外形品质的重要评价指标,当前主要依赖于专业人员的感官评价,主观性强且评语抽象,缺乏客观化、数字化的品质评价手段。为构建茶毫品质数字化评价方法,采集3个不同茶毫品质等级的祁门红茶样品图像,采用HSV彩色图像分割技术对...茶毫是红茶外形品质的重要评价指标,当前主要依赖于专业人员的感官评价,主观性强且评语抽象,缺乏客观化、数字化的品质评价手段。为构建茶毫品质数字化评价方法,采集3个不同茶毫品质等级的祁门红茶样品图像,采用HSV彩色图像分割技术对感兴趣区域(Region of interest,ROI)提取HSV颜色空间分量特征,构建分割指数(Segmentation index,SI)检索得到茶毫、茶身和阴影的最佳分割阈值,采用掩膜法和像素点判别对图像分割效果进行定性和定量评价,并构建茶毫比例量化方法。结果表明,茶毫、茶身和阴影区域的平均分割准确率达到了98.70%,进一步通过茶毫比例量化结果获得祁门红茶3个茶毫品质等级(“显毫”“多毫”和“少毫”)的推荐毫量比例阈值。不同毫量梯度拼配茶样的线性回归分析(R2=0.958,P<0.01)及滇红、金骏眉的泛化应用效果表明,构建的茶毫品质数字化评价方法在不同毫量区间和不同红茶类别上具有较好的适应性。展开更多
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.
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.
基金This work has been partially supported with the grant received in research project under RUSA 2.0 component 8,Govt.of India,New Delhi.
文摘Partitional clustering techniques such as K-Means(KM),Fuzzy C-Means(FCM),and Rough K-Means(RKM)are very simple and effective techniques for image segmentation.But,because their initial cluster centers are randomly determined,it is often seen that certain clusters converge to local optima.In addition to that,pathology image segmentation is also problematic due to uneven lighting,stain,and camera settings during the microscopic image capturing process.Therefore,this study proposes an Improved Slime Mould Algorithm(ISMA)based on opposition based learning and differential evolution’s mutation strategy to perform illumination-free White Blood Cell(WBC)segmentation.The ISMA helps to overcome the local optima trapping problem of the partitional clustering techniques to some extent.This paper also performs a depth analysis by considering only color components of many well-known color spaces for clustering to find the effect of illumination over color pathology image clustering.Numerical and visual results encourage the utilization of illumination-free or color component-based clustering approaches for image segmentation.ISMA-KM and“ab”color channels of CIELab color space provide best results with above-99%accuracy for only nucleus segmentation.Whereas,for entire WBC segmentation,ISMA-KM and the“CbCr”color component of YCbCr color space provide the best results with an accuracy of above 99%.Furthermore,ISMA-KM and ISMA-RKM have the lowest and highest execution times,respectively.On the other hand,ISMA provides competitive outcomes over CEC2019 benchmark test functions compared to recent well-established and efficient Nature-Inspired Optimization Algorithms(NIOAs).
文摘Microscopic halftone image recognition and analysis can provide quantitative evidence for printing quality control and fault diagnosis of printing devices, while halftone image segmentation is one of the significant steps during the procedure. Automatic segmentation on microscopic dots by the aid of the Fuzzy C-Means (FCM) method that takes account of the fuzziness of halftone image and utilizes its color information adequately is realized. Then some examples show the technique effective and simple with better performance of noise immunity than some usual methods. In addition, the segmentation results obtained by the FCM in different color spaces are compared, which indicates that the method using the FCM in the f 1f 2f 3 color space is superior to the rest.
文摘Color image segmentation is crucial in image processing and computer vision.Most traditional segmentation methods simply regard an RGB color image as the direct combination of the three monochrome images and ignore the inherent color structures within channels,which contain some key feature information of the image.To better describe the relationship of color channels,we introduce a quaternion-based regularization that can reflect the image characteristics more intuitively.Our model combines the idea of the Mumford-Shah model-based two-stage segmentation method and the Saturation-Value Total Variation regularization for color image segmentation.The new strategy first extracts features from the color image and then subdivides the image in a new color feature space which achieves better performance than methods in RGB color space.Moreover,to accelerate the optimization process,we use a new primal-dual algorithm to solve our novel model.Numerical results demonstrate clearly that the performance of our proposed method is excellent.
文摘Through the analysis and comparison of shortcomings and advantages of existing technologies on object modeling in 3D applications,we propose a new modeling method for virtual scene based on multi-view image sequence to model irregular objects efficiently in 3D application.In 3D scene,this method can get better visual effect by tracking the viewer's real-time perspective position and projecting the photos from different perspectives dynamically.The philosophy of design,the steps of development and some other relevant topics are discussed in details,and the validity of the algorithm is analyzed.The results demonstrate that this method represents more superiority on simulating irregular objects by applying it to the modeling of virtual museum.
文摘茶毫是红茶外形品质的重要评价指标,当前主要依赖于专业人员的感官评价,主观性强且评语抽象,缺乏客观化、数字化的品质评价手段。为构建茶毫品质数字化评价方法,采集3个不同茶毫品质等级的祁门红茶样品图像,采用HSV彩色图像分割技术对感兴趣区域(Region of interest,ROI)提取HSV颜色空间分量特征,构建分割指数(Segmentation index,SI)检索得到茶毫、茶身和阴影的最佳分割阈值,采用掩膜法和像素点判别对图像分割效果进行定性和定量评价,并构建茶毫比例量化方法。结果表明,茶毫、茶身和阴影区域的平均分割准确率达到了98.70%,进一步通过茶毫比例量化结果获得祁门红茶3个茶毫品质等级(“显毫”“多毫”和“少毫”)的推荐毫量比例阈值。不同毫量梯度拼配茶样的线性回归分析(R2=0.958,P<0.01)及滇红、金骏眉的泛化应用效果表明,构建的茶毫品质数字化评价方法在不同毫量区间和不同红茶类别上具有较好的适应性。
