The aims of this study are to develop the color density concept and to propose the color density based color difference formulas.The color density is defined using the metric coefficients that are based on the discrim...The aims of this study are to develop the color density concept and to propose the color density based color difference formulas.The color density is defined using the metric coefficients that are based on the discrimination ellipses and the locations of the colors in the color space.The ellipse sets are the MacAdam ellipses in the CIE 1931 xy-chromaticity diagram and the chromaticity-discrimination ellipses in the CIELAB space.The latter set was originally used to develop the CIEDE2000 color difference formula.The color difference can be calculated from the color density for the two colors under consideration.As a result,the color density represents the perceived color difference more accurately,and it could be used to characterize a color by a quantity attribute matching better to the perceived color difference from this color.Resulting from this,the color density concept provides simply a correction term for the estimation of the color differences.In the experiments,the line element formula and the CIEDE2000 color difference formula performed better than the color density based difference measures.The reason behind this is in the current modeling of the color density concept.The discrimination ellipses are typically described with three-dimensional data consisting of two axes,the major and the minor,and the inclination angle.The proposed color density is only a one-dimensional corrector for color differences;thus,it cannot capture all the details of the ellipse information.Still,the color density gives clearly more correct estimations to perceived color differences than Euclidean distances using directly the coordinates of the color space.展开更多
Fine and sparse details appear in many quality inspection applications requiring machine vision.Especially on flat surfaces,such as paper or board,the details can be made detectable by oblique illumination.In this stu...Fine and sparse details appear in many quality inspection applications requiring machine vision.Especially on flat surfaces,such as paper or board,the details can be made detectable by oblique illumination.In this study,a general definition of such details is given by defining sufficient statistical properties from histograms.The statistical model allows simulation of data and comparison of methods designed for detail detection.Based on the definition,utilization of the existing thresholding methods is shown to be well motivated.The comparison shows that minimum error thresholding outperforms the other standard methods.Finally,the results are successfully applied to a paper printability inspection application,and the IGT picking assessment,in which small surface defects must be detected.The provided method and measurement system prototype provide automated assessment with results comparable to manual expert evaluations in this laborious task.展开更多
文摘The aims of this study are to develop the color density concept and to propose the color density based color difference formulas.The color density is defined using the metric coefficients that are based on the discrimination ellipses and the locations of the colors in the color space.The ellipse sets are the MacAdam ellipses in the CIE 1931 xy-chromaticity diagram and the chromaticity-discrimination ellipses in the CIELAB space.The latter set was originally used to develop the CIEDE2000 color difference formula.The color difference can be calculated from the color density for the two colors under consideration.As a result,the color density represents the perceived color difference more accurately,and it could be used to characterize a color by a quantity attribute matching better to the perceived color difference from this color.Resulting from this,the color density concept provides simply a correction term for the estimation of the color differences.In the experiments,the line element formula and the CIEDE2000 color difference formula performed better than the color density based difference measures.The reason behind this is in the current modeling of the color density concept.The discrimination ellipses are typically described with three-dimensional data consisting of two axes,the major and the minor,and the inclination angle.The proposed color density is only a one-dimensional corrector for color differences;thus,it cannot capture all the details of the ellipse information.Still,the color density gives clearly more correct estimations to perceived color differences than Euclidean distances using directly the coordinates of the color space.
基金The work was partially supported by the following projects:PapVision(http://www.it.lut.fi/project/papvision/)financed by the European Union(Tekes project No.70049/03 and 70056/04)the Academy of Finland(Project 204708).
文摘Fine and sparse details appear in many quality inspection applications requiring machine vision.Especially on flat surfaces,such as paper or board,the details can be made detectable by oblique illumination.In this study,a general definition of such details is given by defining sufficient statistical properties from histograms.The statistical model allows simulation of data and comparison of methods designed for detail detection.Based on the definition,utilization of the existing thresholding methods is shown to be well motivated.The comparison shows that minimum error thresholding outperforms the other standard methods.Finally,the results are successfully applied to a paper printability inspection application,and the IGT picking assessment,in which small surface defects must be detected.The provided method and measurement system prototype provide automated assessment with results comparable to manual expert evaluations in this laborious task.
