Automatically assessing fabric smoothness grade is very important in the evaluation of fabric appearance.A system for objectively evaluating the fabric smoothness grade based on a grating projection unit and double co...Automatically assessing fabric smoothness grade is very important in the evaluation of fabric appearance.A system for objectively evaluating the fabric smoothness grade based on a grating projection unit and double colored CCD(short form of charge coupled device) was constructed in this paper.Two images captured by different CCD compensated each other which reduced the influence of noises.The application of the four-step phase-shifting method enabled the calculation of the exact phase in a point easy and quick.A large amount of 3D points with three coordinates X,Y and Z were obtained precisely making the definition and calculation of fabric smoothness characters easy.Then four parameters which intuitively denoted the fabric smoothness degree were obtained.Finally,a proper neural network was built,which successfully performed the fabric smoothness classification.The experimental results show that the system is applicable for all the fabric whatever pattern or color.The experimental grades provided by this grating projection system are also highly consistent with the subjective results.展开更多
For garment or fabric appearance, the cloth smoothness grade is one of the most important performance factors in textile and garment community. In this paper, on the base of Rough Set Theory,a new objective method for...For garment or fabric appearance, the cloth smoothness grade is one of the most important performance factors in textile and garment community. In this paper, on the base of Rough Set Theory,a new objective method for fabric smoothness grade evaluation was constructed. The objective smoothness grading model took the parameters of 120 AATCC replicas' point-sampled models as the conditional attributes and formed the smoothness grading decision table. Then, NS discretization method and genetic algorithm reduction method were used in the attributes discretization and feature reduction. Finally, the grading model was expressed as simple and intuitional classification rules. The simulation results show the validity of the fabric smoothness grading system which is built on the use of rough sets.展开更多
文摘Automatically assessing fabric smoothness grade is very important in the evaluation of fabric appearance.A system for objectively evaluating the fabric smoothness grade based on a grating projection unit and double colored CCD(short form of charge coupled device) was constructed in this paper.Two images captured by different CCD compensated each other which reduced the influence of noises.The application of the four-step phase-shifting method enabled the calculation of the exact phase in a point easy and quick.A large amount of 3D points with three coordinates X,Y and Z were obtained precisely making the definition and calculation of fabric smoothness characters easy.Then four parameters which intuitively denoted the fabric smoothness degree were obtained.Finally,a proper neural network was built,which successfully performed the fabric smoothness classification.The experimental results show that the system is applicable for all the fabric whatever pattern or color.The experimental grades provided by this grating projection system are also highly consistent with the subjective results.
文摘For garment or fabric appearance, the cloth smoothness grade is one of the most important performance factors in textile and garment community. In this paper, on the base of Rough Set Theory,a new objective method for fabric smoothness grade evaluation was constructed. The objective smoothness grading model took the parameters of 120 AATCC replicas' point-sampled models as the conditional attributes and formed the smoothness grading decision table. Then, NS discretization method and genetic algorithm reduction method were used in the attributes discretization and feature reduction. Finally, the grading model was expressed as simple and intuitional classification rules. The simulation results show the validity of the fabric smoothness grading system which is built on the use of rough sets.
