Background dominant colors extraction method based on color image quick fuzzy c-means clustering algorithm

A quick and accurate extraction of dominant colors of background images is the basis of adaptive camouflage design.This paper proposes a Color Image Quick Fuzzy C-Means(CIQFCM)clustering algorithm based on clustering spatial mapping.First,the clustering sample space was mapped from the image pixels to the quantized color space,and several methods were adopted to compress the amount of clustering samples.Then,an improved pedigree clustering algorithm was applied to obtain the initial class centers.Finally,CIQFCM clustering algorithm was used for quick extraction of dominant colors of background image.After theoretical analysis of the effect and efficiency of the CIQFCM algorithm,several experiments were carried out to discuss the selection of proper quantization intervals and to verify the effect and efficiency of the CIQFCM algorithm.The results indicated that the value of quantization intervals should be set to 4,and the proposed algorithm could improve the clustering efficiency while maintaining the clustering effect.In addition,as the image size increased from 128×128 to 1024×1024,the efficiency improvement of CIQFCM algorithm was increased from 6.44 times to 36.42 times,which demonstrated the significant advantage of CIQFCM algorithm in dominant colors extraction of large-size images.

Soft measurement for component content based on adaptive model of Pr/Nd color features

For measurement of component content in the extraction and separation process of praseodymium/neodymium(Pr/Nd), a soft measurement method was proposed based on modeling of ion color features, which is suitable for fast estimation of component content in production field. Feature analysis on images of the solution is conducted,which are captured from Pr/Nd extraction/separation field. H/S components in the HSI color space are selected as model inputs, so as to establish the least squares support vector machine(LSSVM) model for Nd(Pr) content,while the model parameters are determined with the GA algorithm. To improve the adaptability of the model,the adaptive iteration algorithm is used to correct parameters of the LSSVM model, on the basis of model correction strategy and new sample data. Using the field data collected from rare earth extraction production, predictive methods for component content and comparisons are given. The results indicate that the proposed method presents good adaptability and high prediction precision, so it is applicable to the fast detection of element content in the rare earth extraction.

Detecting maize leaf water status by using digital RGB images

To explore the correlation between crop leaf digital RGB(Red,Green and Blue)image features and the corresponding moisture content of the leaf,a Canon digital camera was used to collect image information from detached leaves of heading-stage maize.A drying method was adopted to measure the moisture content of the leaf samples,and image processing technologies,including gray level co-occurrence matrices and grayscale histograms,was used to extract the maize leaf texture feature parameters and color feature parameters.The correlations of these feature parameters with moisture content were analyzed.It is found that the texture parameters of maize leaf RGB images,including contrast,correlation,entropy and energy,were not significantly correlated with moisture content.Thus,it was difficult to use these features to predict moisture content.Of the six groups of eigenvalues for the leaf color feature parameters,including mean,variance,energy,entropy,kurtosis and skewness,mean and kurtosis were found to be correlated with moisture content.Thus,these features could be used to predict the leaf moisture content.The correlation coefficient(R2)of the mean-moisture content relationship model was 0.7017,and the error of the moisture content prediction was within±2%.The R2 of the kurtosis-moisture content relationship model was 0.7175,and the error of the moisture content prediction was within±1.5%.The study results proved that RGB images of crop leaves could be used to measure moisture content.