To decrease number of samples for the implementation of color space transformation, a method for modeling the chromatic characterization of video cameras was proposed. An additional transformation was required to pred...To decrease number of samples for the implementation of color space transformation, a method for modeling the chromatic characterization of video cameras was proposed. An additional transformation was required to predict output RGB values for an input color. This additional transformation was based on spectral reflectance relationship. The transformed color coordinates were taken as inputs of a multilayer neural network. Based on network outputs, the RGB values to be predicted were calculated. Experimental results were given to illustrate the performance of the method. Even though much less number of training samples are used, this method can also perform well on this color space transformation.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
Image matching based on scale invariant feature transform（SIFT） is one of the most popular image matching algorithms, which exhibits high robustness and accuracy. Grayscale images rather than color images are genera...Image matching based on scale invariant feature transform（SIFT） is one of the most popular image matching algorithms, which exhibits high robustness and accuracy. Grayscale images rather than color images are generally used to get SIFT descriptors in order to reduce the complexity. The regions which have a similar grayscale level but different hues tend to produce wrong matching results in this case. Therefore, the loss of color information may result in decreasing of matching ratio. An image matching algorithm based on SIFT is proposed, which adds a color offset and an exposure offset when converting color images to grayscale images in order to enhance the matching ratio. Experimental results show that the proposed algorithm can effectively differentiate the regions with different colors but the similar grayscale level, and increase the matching ratio of image matching based on SIFT. Furthermore, it does not introduce much complexity than the traditional SIFT.展开更多
A method for shadow detection and compensation for color aerial images is presented.It is considered that the intensity value of each image pixel is the product of illumination function and ground object reflection,an...A method for shadow detection and compensation for color aerial images is presented.It is considered that the intensity value of each image pixel is the product of illumination function and ground object reflection,and the shadowed regions on the image are mainly caused by the short of illumination,so the information compensation for the shadowed regions should concentrate on the illumination adjustment of concerned area on the basis of the analysis of whole image.The shadow detection and compensation procedure proposed by this paper consists of four steps.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
Objective To determine the positions of marking in the presence of distracting shadows, highlight, pavement cracks, etc. Methods RGB color space is transformed into I 1 I 2 I 3 color space and I 2 ...Objective To determine the positions of marking in the presence of distracting shadows, highlight, pavement cracks, etc. Methods RGB color space is transformed into I 1 I 2 I 3 color space and I 2 component was used to form a new image with less effect of the clutter. Using an improved edge detection operator, an edge strength map was produced, and binarilized by adaptive thresholds. The binary image was labeled and circularity of all connected components is calculated. The Self Organizing Mapping is adopted to extract regions which imply potential marking. Finally the position of marking was obtained by curve fitting. Results Color information was utilized fully, all thresholds were set adaptively and lane marking could be detected in challenging images with shadows, highlight or other cars. Conclusion The method based on circularity of connected components shows its outstanding robustness to lane marking detection and has a wide variety of applications in the areas of vehicle autonomous navigation and driver assistance system.展开更多
Grayscale image colorization is an important computer graphics problem with a variety of applications. Recent fully automatic colorization methods have made impressive progress by formulating image colorization as a p...Grayscale image colorization is an important computer graphics problem with a variety of applications. Recent fully automatic colorization methods have made impressive progress by formulating image colorization as a pixel-wise prediction task and utilizing deep convolutional neural networks. Though tremendous improvements have been made, the result of automatic colorization is still far from perfect. Specifically, there still exist common pitfalls in maintaining color consistency in homogeneous regions as well as precisely distinguishing colors near region boundaries. To tackle these problems, we propose a novel fully automatic colorization pipeline which involves a boundary-guided CRF （conditional random field） and a CNN-based color transform as post-processing steps. In addition, as there usually exist multiple plausible colorization proposals for a single image, automatic evaluation for different colorization methods remains a challenging task. We further introduce two novel automatic evaluation schemes to efficiently assess colorization quality in terms of spatial coherence and localization. Comprehensive experiments demonstrate great quality improvement in results of our proposed colorization method under multiple evaluation metrics.展开更多
As one of the important vegetation parameters, vegetation fractional coverage （VFC） is more difficult to measure accurately among a good many parameters of plant communities. The temperate typical steppe in the nort...As one of the important vegetation parameters, vegetation fractional coverage （VFC） is more difficult to measure accurately among a good many parameters of plant communities. The temperate typical steppe in the north of China was chosen for investigation in the present study and a digital camera was used to measure herb community coverage in the field, adopting methods of ocular estimation, gridding measurement, visual interpretation, supervised classification, and information extraction of color spatial transformation to calculate the VFC of images captured by the digital camera. In addition VFC calculated by various methods was analyzed and compared VFC, enabling us to propose an effective method for measuring VFC using a digital camera. The results of the present study indicate that： （i） as two common useful and effective methods of measuring VFC with a digital camera, not only does the error of estimated values of visual estimation and supervised classification vary considerably, but the degree of automatization is very low and depends, to a great extent, on the manipulator; （ii） although the method of visual interpretation may assure the precision of the calculated VFC and enable the precision of results obtained using other methods to be determined, as far as large quantities of data are concerned, this method has the disadvantages of wasting time and energy, and the applications of this method are limited; （iii） the precision and stability of VFC calculated using the grid and node method are superior to those of visual estimation and supervised classification and inferior to those of visual interpretation, but, as for visual interpretation and supervised classification, gridding measurements are difficult to apply in practice because they are not time efficient; and （iv） in terms of the precision of calculation of the VFC, an information-extracting model based on an intensity, hue, saturation （IHS） color space-multi-component series segmentation strategy is superior to methods of ocular estimation, gridding measurement, and supervised classification. In terms of practical efficiency, the information-extracting model is superior to visual interpretation, supervised classification, and gridding measurement. It has been proven that estimating the VFC of the north temperate typical steppe using this model is feasible. This is very fundamental research work in grassland ecology.展开更多
文摘To decrease number of samples for the implementation of color space transformation, a method for modeling the chromatic characterization of video cameras was proposed. An additional transformation was required to predict output RGB values for an input color. This additional transformation was based on spectral reflectance relationship. The transformed color coordinates were taken as inputs of a multilayer neural network. Based on network outputs, the RGB values to be predicted were calculated. Experimental results were given to illustrate the performance of the method. Even though much less number of training samples are used, this method can also perform well on this color space transformation.
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.
基金supported by the National Natural Science Foundation of China(61271315)the State Scholarship Fund of China
文摘Image matching based on scale invariant feature transform（SIFT） is one of the most popular image matching algorithms, which exhibits high robustness and accuracy. Grayscale images rather than color images are generally used to get SIFT descriptors in order to reduce the complexity. The regions which have a similar grayscale level but different hues tend to produce wrong matching results in this case. Therefore, the loss of color information may result in decreasing of matching ratio. An image matching algorithm based on SIFT is proposed, which adds a color offset and an exposure offset when converting color images to grayscale images in order to enhance the matching ratio. Experimental results show that the proposed algorithm can effectively differentiate the regions with different colors but the similar grayscale level, and increase the matching ratio of image matching based on SIFT. Furthermore, it does not introduce much complexity than the traditional SIFT.
基金the Opening Research Foundation from LIESMARS(No.(01)0101)
文摘A method for shadow detection and compensation for color aerial images is presented.It is considered that the intensity value of each image pixel is the product of illumination function and ground object reflection,and the shadowed regions on the image are mainly caused by the short of illumination,so the information compensation for the shadowed regions should concentrate on the illumination adjustment of concerned area on the basis of the analysis of whole image.The shadow detection and compensation procedure proposed by this paper consists of four steps.
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.
文摘Objective To determine the positions of marking in the presence of distracting shadows, highlight, pavement cracks, etc. Methods RGB color space is transformed into I 1 I 2 I 3 color space and I 2 component was used to form a new image with less effect of the clutter. Using an improved edge detection operator, an edge strength map was produced, and binarilized by adaptive thresholds. The binary image was labeled and circularity of all connected components is calculated. The Self Organizing Mapping is adopted to extract regions which imply potential marking. Finally the position of marking was obtained by curve fitting. Results Color information was utilized fully, all thresholds were set adaptively and lane marking could be detected in challenging images with shadows, highlight or other cars. Conclusion The method based on circularity of connected components shows its outstanding robustness to lane marking detection and has a wide variety of applications in the areas of vehicle autonomous navigation and driver assistance system.
文摘Grayscale image colorization is an important computer graphics problem with a variety of applications. Recent fully automatic colorization methods have made impressive progress by formulating image colorization as a pixel-wise prediction task and utilizing deep convolutional neural networks. Though tremendous improvements have been made, the result of automatic colorization is still far from perfect. Specifically, there still exist common pitfalls in maintaining color consistency in homogeneous regions as well as precisely distinguishing colors near region boundaries. To tackle these problems, we propose a novel fully automatic colorization pipeline which involves a boundary-guided CRF （conditional random field） and a CNN-based color transform as post-processing steps. In addition, as there usually exist multiple plausible colorization proposals for a single image, automatic evaluation for different colorization methods remains a challenging task. We further introduce two novel automatic evaluation schemes to efficiently assess colorization quality in terms of spatial coherence and localization. Comprehensive experiments demonstrate great quality improvement in results of our proposed colorization method under multiple evaluation metrics.
文摘As one of the important vegetation parameters, vegetation fractional coverage （VFC） is more difficult to measure accurately among a good many parameters of plant communities. The temperate typical steppe in the north of China was chosen for investigation in the present study and a digital camera was used to measure herb community coverage in the field, adopting methods of ocular estimation, gridding measurement, visual interpretation, supervised classification, and information extraction of color spatial transformation to calculate the VFC of images captured by the digital camera. In addition VFC calculated by various methods was analyzed and compared VFC, enabling us to propose an effective method for measuring VFC using a digital camera. The results of the present study indicate that： （i） as two common useful and effective methods of measuring VFC with a digital camera, not only does the error of estimated values of visual estimation and supervised classification vary considerably, but the degree of automatization is very low and depends, to a great extent, on the manipulator; （ii） although the method of visual interpretation may assure the precision of the calculated VFC and enable the precision of results obtained using other methods to be determined, as far as large quantities of data are concerned, this method has the disadvantages of wasting time and energy, and the applications of this method are limited; （iii） the precision and stability of VFC calculated using the grid and node method are superior to those of visual estimation and supervised classification and inferior to those of visual interpretation, but, as for visual interpretation and supervised classification, gridding measurements are difficult to apply in practice because they are not time efficient; and （iv） in terms of the precision of calculation of the VFC, an information-extracting model based on an intensity, hue, saturation （IHS） color space-multi-component series segmentation strategy is superior to methods of ocular estimation, gridding measurement, and supervised classification. In terms of practical efficiency, the information-extracting model is superior to visual interpretation, supervised classification, and gridding measurement. It has been proven that estimating the VFC of the north temperate typical steppe using this model is feasible. This is very fundamental research work in grassland ecology.
