Research On Guizhou Miao Costumes Pattern Segmentation Based On Active Contour Model

Ethnic costume is not only a daily necessity but also exist as a carrier of ethnic culture to a great extent.Our ethnic costumes have been greatly impacted by foreign cultures with the increasing globalization and modernization of our country,and the unique elements of Miao costumes are being lost.In order to protect the precious culture of China’s ethnic minorities,we urgently need to inheritance,protection,and development ethnic costumes.However,in the researches on the inheritance and protection of minority costumes culture,most of them still stay in the traditional research methods such as humanities and social sciences,and there are few researches on the application of nowadays popular technologies such as digitalization and computer vision to minority costumes.And affected by the Miao costume style,texture,color differences,and pattern types of diversity,the existing active contour model image segmentation technology for Miao costume image segmentation has not achieved good results.Therefore,research an active contour image segmentation method that is more suitable for the segmentation of Miao costume elements in China,to improve the segmentation effect of Miao costume images.To a certain extent,it provides a certain basis for the research of minority costume image segmentation algorithms.In this dissertation,based on the active contour model,aiming at the Miao costume images have problems such as embroidery line texture,complex shape,and large color difference,and the traditional active contour model is sensitive to initial contour curves and noise,we proposed a corresponding optimization model and the main work includes:(1)We proposed an image segmentation model of Miao costumes based on fuzzy fitting image.Firstly,the fuzzy local and global fitting images are defined in the image fuzzy region.Meanwhile,according to the nature of Kullback-Leibler divergence used to describe the difference between two probability distributions,the fuzzy energy function is constructed based on the image difference between the original image and the fuzzy local and global fitting images in the Kullback-Leibler divergence,which can drive the initial contour to the target boundary.Next,the adaptive weight is defined by using the normalized intra-class variances of the pixel grayscale inside and outside the contour curves of the image,which can automatically adjust the parameters between the local and global fuzzy energy terms.Finally,we add a regularization term and a length term in the energy function,and an edge detector is introduced into the regularization term and length term to smooth the edge of the image.In the experiment,the validity of the proposed model is verified by experiments on natural images.The similarity and sensitivity coefficients of the segmentation results are above 0.97 and 0.98,respectively.Experimental results on Miao costume images show that the proposed algorithm has better segmentation results and only requires a few number of iterations and segmentation time.In addition,the algorithm is robust to initial contour curves and Miao costume images segmentation.(2)We proposed an image segmentation model of Miao costumes guided by weighted signed pressure force(SPF)combined with coefficient of variation.Firstly,a novel coefficient of variation is defined by combining weighted average prototypes of inside and outside regions for the input image.Secondly,a new weighted SPF function is constructed based on the improved coefficient of variation and the definition of the SPF function,which can improve the stability of the model and lessens segmentation time.Next,a double-well potential function is introduced to the distance regularization term to effectively eliminate the need for re-initialization of the level set function.Finally,a force propagation function is defined by global and local image information,which automatically balances interior and exterior forces of the contour curve according to the image feature.Meanwhile,global and local normalized intra-class variances are used to automatically balance weights between the global and local information of an image.Experiments on medical and natural images verify the performance of the proposed algorithm,and the segmentation results show that the proposed algorithm is highly segmentation accurate and computationally efficient than the region-based active contour models.Furthermore,the proposed optimization algorithm is further applied to the experiment of Miao costume image,and the segmentation results show that the proposed optimization algorithm not only achieves good segmentation effect but also is strong robust to Miao costume image segmentation with noise and different initial contour curves.