Enhancement And Identification Of Mongolian Furniture Pattern Based On AGC-Quantile

Mongolian furniture pattern recognition classification mainly depends on the human visual sense,but as a result of the change of history,sampling equipment,the effect of environmental conditions and other external factors,the influence of the Mongolian furniture patterns affected,in order to protect and inheriting the Mongolian traditional furniture pattern,at the same time to provide theoretical basis for study of the Mongolian patterns of the furniture,the patterns of the Mongolian furniture computer automation applications.In order to realize the enhancement and recognition of Mongolian furniture patterns,this paper takes three kinds of patterns of plants,animals and clouds as the research objects,and proposes an enhancement algorithm based on Adaptive Gamma Correction-Quantile(Quantile),which can improve the recognition rate of Mongolian furniture patterns.This algorithm converts the input Mongolian furniture pattern RGB color space into HSV color space.Since H and S components do not directly affect human perception of image contrast,only V component is applied quantile algorithm.Secondly,in order to highlight the important details in the pattern,the overall brightness of V component is improved by the adaptive gamma correction algorithm.Finally,the processed V component is combined with the unprocessed H and S components and converted back to the RGB color space to obtain the enhanced image.(1)Three Mongolian furniture patterns were enhanced based on Gamma Correction and Histogram Equalization algorithm.(2)in the Quantile algorithm and adaptive gamma correction algorithm,on the basis of AGC-Quantile algorithm is proposed,and the algorithm with adaptive gamma correction algorithm,Quantile image enhancement algorithm,based on gamma correction and histogram equalization image enhancement algorithms are compared and objective analysis,application of the evaluation indexes for the Peak signal-to-noise Ratio(Peak Signal Noise thewire,PSNR),Structural SIMilarity,Structural SIMilarity,SSIM)and Mean Square Error(Mean Square Error,MSE).(3)with the Mongolian furniture patterns of plants,animals,clouds three patterns as samples,the proposed algorithm in this paper,respectively,and comparison of the other three algorithms for preprocessing of three kinds of patterns and applications use of luminance variance,Angle variance,brightness gradient variance and mean square than four identification parameters and Support Vector Machine(SVM)(Support Vector Machine,SVM)gaussian kernel function to the enhanced patterns for identification.In this paper,a Mongolian furniture pattern enhancement method based on AGC-Quantile is proposed.Compared with the other three methods,the algorithm is superior to the other methods in terms of standardization,completeness and final enhancement effect.Compared with other traditional met hods,the improved pattern recognition rate of the proposed method has been greatly improved.