Pan Ruru

Unsupervised Color Classification for Yarn-dyed Fabric Based on FCM Algorithm

A novel method for realizing the color classifying in yarn-dyed fabric is proposed in this paper. The color image of yarn-dyed fabric was obtained by a flat scanner, and then it is converted from RGB color space to Lab color space. By analyzing the difference among RGB, HSL and Lab color space, the advantages of Lab color are concluded. FCM was selected as the Color Cluster method. A better color classification quality in Lab color space is shown in the experiment. The color yarn number is detected based on the validity for FCM clusters. Experimental comparisons on RGB, HSL, and Lab color spaces show that the approach proposed in this article is more effective for color extracting and classifying in yarn-dyed fabric.

Development of image pattern for textile based on FFT

Purpose – The purpose of this paper is to research an auto generation method of developing FFT image and image pattern for textile based on FFT theory.Design/methodology/approach – In the research, a program was developed to generate FFT images using the FFT algorithm. The process of auto generation FFT image can be divided into the following steps: initializing the size of image, painting source image, giving the color pattern, transforming FFT image by FFT, designing mask template, and image pattern combining by point diagram. These image patterns can be used to apply on the textile.Findings – The results showed that the FFT images can be used for textile designer directly. The FFT images can also be used as elements for textile image design such as clothing. The auto generation FFT images by FFT reflect different modern sense of beauty from traditional geometric images.Research limitations/implications – There are many parameters that affect the art effect of FFT image generating by FFT algorithm. Howe...

Review of researches on fashion color prediction based on Grey systems theory

Present researches on fashion color prediction based on Grey systems theory; Introduce the research process, data processing method and the forecasting Grey model (GM (1, 1)); Discuss on the problems existing in the current researches, such as the data collecting, model optimization problems; Provide the current analysis result on historical data by our team; Finally, put forward the future suggestions that future researches could be based on more advanced and intelligent mathematical tools and forecasting theories, such as artificial neuro-fuzzy system, natural computation, genetic algorithm, support vector machine, in order to improve the accuracy of prediction.