Computer Image Analysis Of Fabric Defect Detection And Assessment

In this thesis, recurring to MATLAB, the detection of fabric defects using digital image processing and neural network technique is studied. A new way of detecting fabric detects which based on image processing and accomplish detect classification by means of neural network is put forward. Furthermore, the feasibility of this way is testified.Recurring to three MATLAB toolbox: image processing toolbox, wavelet toolbox, neural network toolbox, by a mass of experimental comparisons and validations, the image processing methods which applies to detect analysis, the best feature extraction of fabric detects and the efficient and exact detect recognition pattern are found; the systematization of the whole image processing flow is achieved for coping with the different kinds of detects in image processing, accordingly, a integrated system recognizing detects automatically which consists of five key steps: image preprocessing of fabric images with detects, two-value image segmentation, image processing after segmenting, feature extraction of fabric detects and the recognition and classification of fabric detects by means of neural network model is formed. The experimental evaluation of this system is also made and it is testified that this system can quickly and exactly recognize detects in in-gray images which have simple fabric weaves to one of four classes: warp direction detects, weft direction defects, regional defects and discrete defects when the lightness and grounding color are similar.In the process of achieving image processing, the intent of getting the exact sizes of detects is discarded and some important hypotheses are made to classify detects quickly and precisely. And it is testified that these hypotheses according with the fact basically can improve effect and simplify arithmetic greatly and do not counteract classifying , orientating and getting the number of detects.