In order to realize the auto-generation of clothing paper pattern making and reduce the reliance on the experience of clothing pattern makers,by simulating the experience of the clothing pattern maker through back pro...In order to realize the auto-generation of clothing paper pattern making and reduce the reliance on the experience of clothing pattern makers,by simulating the experience of the clothing pattern maker through back propagation(BP)neural network,400 children’s body measurements are collected and drawn into the clothing paper pattern,and the children’s body measurements and the pattern sizes generated through the children’s clothing structure design rules are imported into MATLAB neural network toolbox and a neural network model is established to automatically become the predicted pattern size.Then the parametric mathematical model of children’s clothing paper pattern is established and the children’s body measurements is imported into Auto-CAD parametric function to generate children’s clothing paper pattern automatically.The experimental interface and the virtual try-on interface are demonstrated and their effects are evaluated.The results show that the production rate of clothing paper patterns is improved by the auto-generation method,which is of positive significance to the intelligent production of clothing enterprises.展开更多
Extracting moving targets from video accurately is of great significance in the field of intelligent transport.To some extent,it is related to video segmentation or matting.In this paper,we propose a non-interactive a...Extracting moving targets from video accurately is of great significance in the field of intelligent transport.To some extent,it is related to video segmentation or matting.In this paper,we propose a non-interactive automatic segmentation method for extracting moving targets.First,the motion knowledge in video is detected with orthogonal Gaussian-Hermite moments and the Otsu algorithm,and the knowledge is treated as foreground seeds.Second,the background seeds are generated with distance transformation based on foreground seeds.Third,the foreground and background seeds are treated as extra constraints,and then a mask is generated using graph cuts methods or closed-form solutions.Comparison showed that the closed-form solution based on soft segmentation has a better performance and that the extra constraint has a larger impact on the result than other parameters.Experiments demonstrated that the proposed method can effectively extract moving targets from video in real time.展开更多
文摘In order to realize the auto-generation of clothing paper pattern making and reduce the reliance on the experience of clothing pattern makers,by simulating the experience of the clothing pattern maker through back propagation(BP)neural network,400 children’s body measurements are collected and drawn into the clothing paper pattern,and the children’s body measurements and the pattern sizes generated through the children’s clothing structure design rules are imported into MATLAB neural network toolbox and a neural network model is established to automatically become the predicted pattern size.Then the parametric mathematical model of children’s clothing paper pattern is established and the children’s body measurements is imported into Auto-CAD parametric function to generate children’s clothing paper pattern automatically.The experimental interface and the virtual try-on interface are demonstrated and their effects are evaluated.The results show that the production rate of clothing paper patterns is improved by the auto-generation method,which is of positive significance to the intelligent production of clothing enterprises.
基金Project (No. 61033003) supported by the National Natural Science Foundation of China
文摘Extracting moving targets from video accurately is of great significance in the field of intelligent transport.To some extent,it is related to video segmentation or matting.In this paper,we propose a non-interactive automatic segmentation method for extracting moving targets.First,the motion knowledge in video is detected with orthogonal Gaussian-Hermite moments and the Otsu algorithm,and the knowledge is treated as foreground seeds.Second,the background seeds are generated with distance transformation based on foreground seeds.Third,the foreground and background seeds are treated as extra constraints,and then a mask is generated using graph cuts methods or closed-form solutions.Comparison showed that the closed-form solution based on soft segmentation has a better performance and that the extra constraint has a larger impact on the result than other parameters.Experiments demonstrated that the proposed method can effectively extract moving targets from video in real time.
