Objective Evaluation Of Cloth Smoothness Grade On Point-Sampled Model

For garment or fabric appearance, the cloth smoothness grade is one of the most important performance factors.Traditionally, the grade process is manually performed. After repeating home laundering, the fabric sample and appropriate reference standards are put side by side under a standard lighting and viewing area. The judges rate the fabric appearance by comparing the sample with the references. The results are usually affected by the rating surroundings, and the statuses of judges are highly subjective and non-repeatable.Development of vision systems that can be used to evaluate fabric smoothness has also preoccupied many researchers. In this thesis, practicably image processing techniques and laser scanning methods to perform smoothness grading that had been researched and developed in the past decade are described first. Compared with different kinds of mathematical expressions, point-sampled model which contains a large number of three-dimensional coordinate points and indirectly represents model surface is quite suitable for the expression of the irregular fabric surface. The point-sampled model is convenient to re-form the fabric wrinkle modality and can provide reliable items for fabric smoothness objective evaluation. A 3D non-contact measurement system is constructed to obtain these coordinate points. This system utilizes two charge coupled devices (CCD) and a grating projecting unit to sense the 3D topography of the fabric surface. The technique bases of the system are structured lighting, trigonometry and phase-shifting. Two images captured by different CCD compensate each other and reduce the influence of noises. The design of the system assures the fabric original and natural state and is insensitive to fabric colors and patterns.Subsequently, taken the AATCC-124 replicas' point-sampled models as study objects, four statistical parameters based on the Z ordinates of the scatter points were established to characterize smoothness appearance. They were inter-quartile range R_d, arithmetic average deviation R_a, root mean square deviation R_q and Kurtosis value R_k. The discussions on the discrete tendency and distributing modality of scatter points well revealed the entity bending performance which was in existence as point-sampled model. Afterward, the principle of discrete differential geometry was applied to the replicas point-sampled models. Each vertex and its neighborhood were grabbled. MLS surface at vertex was constructed. These calculations made the following three geometry parameters decided: vertices densityρ_c, vertices height Z and vertices mean curvature H. The discussions on the significantvertices and their reconstructed surfaces well explained the surface local bending performance. All of the statistical and geometry characterizations are closely correlative to smoothness grades. Actually, no wrinkle modality is similar to each other even though they are on the same standard smoothness replica. So, no single characterization can be used alone to forecast the entity's smoothness grade.Rough Set (RS) is one of the soft-computing methods dealing with indefiniteness and incompleteness. It can find the relationship between the data, pick up the useful characters and reduce the information process. In recent years, it has been successfully applied in data mining, knowledge acquisition, algorithm research, decision support systems and pattern recognition. In this thesis, for the first time, RS method was employed in the objective evaluation of fabric smoothness grade. The objective smoothness grading model based on RS theory took all the seven characterizations of 120 replicas' point-sampled models as the inputs. The grading model was expressed as simple and intuitional classification rules.Artificial neural network (ANN) is also one of the popular computing methods dealing with indefiniteness and incompleteness. It has the essential nonlinear character, parallel processing ability and the ability of self organization and self-learning. The back-propagation (BP) algorithm was implemented in this thesis to train a feed-forward ANN which also took all the seven smoothness characterizations of 120 replicas' point-sampled models as the inputs. The correlative coefficient between input smoothness grades and training grades was 94.84%.RS and ANN are both played important roles in intelligent computing methods. They are complementary, so the integration of RS and ANN is feasible. In this thesis, for the first time, an approach of data mining integrated RS and ANN was presented. It fully developed two methods' advantages. RS efficiently processed the reduction of the seven smoothness characterizations, simplified the network's structure, reduced the network's training epochs and improved the judgment accuracy. The correlative coefficient between input smoothness grades and training grades based on RS-BP ANN was high up to 97.19%. Compared with the smoothness grading models which were based on RS or ANN alone, the RS-BP ANN smoothness grading model had the highest tolerance fault, disturbance resistibility, forecast precession and shortest training time.Simulation experiments were executed to verify the validity of RS-BP ANN smoothness grading model based on point-sampled model. 30 pieces of 100% cotton garment cloths with different color, printed pattern or structure were chosen. The experimental grades provided by the RS-BP ANN were highly consistent with the subjective results, and the correlative coefficient between objective and subjective evaluation results was 93.45%. Discussions were made about the influences to the objective grading results which include data acquirement, swatches color, pattern, thickness and structure. We came to the conclusion that the RS-BP ANN grading results were more believable and closer to real grades especially in the case of fabrics with color or pattern.Finally, based on virtual instrument technique, a fabric smoothness grade system was established. The system performs the functions of data acquirement, transmission, analyzing and fabric smoothness grade assessing. It makes full use of the advantages of LabVIEW and has a friendly interface. It was testified that the virtual system built in this thesis had good performance in running and smoothness grade assessing.