Image Processing in Textile Dariush SemnaniMorteza Vadood Isfahan University of Technology

I MAGE PROCESSING 2 Colorful image (RGB) Grayscale imageBinary image

Application of image processing to determine the properties of the non-woven and nano-fibers Lighting Light source with low wave length, laser and light emitting diodes Magnification Preparation Value of the each pixel based on the adjacent values 3

S KELETONIZING OR T HINNING Replacing each object with a narrow line (thickness: 1 pixel) Morphological or Pruning 4

F IBER O RIENTATION D ISTRIBUTION (FOD) Orientation distribution function α: the angle between fiber and horizontal axis 5 Fibers were made from direct and short lines Creating artificial images and testing algorithms Comparing obtained results

6 Direct tracking Furrior transform Hough transform Flow Field Analys Orientation measuring methods

D IRECT T RACKING S EARCH Using Morphological or Pruning methods Every pixel has 8 adjacent pixels 7

D IRECT T RACKING S EARCH It is assumed that the fibers are one pixel thick and have not severe disruptions or kinks or bends within one pixel distance. 8

F OURIOR T RANSFORM An image web was formed from light cycles (dark to white and vice versa) u : frequency in X axis v : frequency in Y axis 9

F URRIOR T RANSFORM Power Spectrum Function If fiber are orientated in a special direction so frequency in same direction is low and in perpendicular direction is high. 10

F URRIOR T RANSFORM Evaluating image by special radius and loop thickness 11

F URRIOR T RANSFORM If the image is not periodic, then discontinuation points appear in transformed image. 12

F LOW F IELD A NALISYS The edges of image present the field orientations stages 1. Morphological operation 2. Calculating gradient vector for all points 3. Dividing image to the small images 4. Determining the mean orientation of fields in each small image 5. Calculating the final image orientation by using mean orientations of small images 13

G AUSSIAN F ILTER Replacing each point by regarding adjacent points H and W : size of kernal matrice 14

G RADIENT Sobel matrice 15 Gy Gy Gx Gx x7x7 x4x4 x1x1 i-1 x8x8 x5x5 x2x2 i x9x9 x6x6 x3x3 i+1 j+1jj-1

F LOW F IELD A NALISYS 16

H OUGH T RANSFORM 17

H OUGH T RANSFORM 18

C OMPARING M ETHODS Direct Tracking is the best method for on-line controlling but it has low speed process because of loops in its algorithm. Flow Field Analisys evaluate the STD lower than the other methods and can be used in on-line controlling. Furrior Transform is the best choice to non-on-line controlling. The results of Hough and Furrior Transform is so close. 19 Direct Tracking Furrior Transform Flow Field Analisys Accuracy ranking

O RIENTATION IN R EAL W EB The best image will be one that represents the entire field as a two dimensional projection. 20

E DGE T HRESHOLDING 21

F IBER D IAMETER D ISTRIBUTION 22

F IBER D IAMETER D ISTRIBUTION 23

Threshold 2Threshold 1Threshold 3 M EASURING THE P OROSITY OF V ARIOUS S URFACE L AYERS 24 Threshold 1 : Threshold 2 : Threshold 3 :

M EASURING THE P OROSITY OF V ARIOUS S URFACE L AYERS 25 n :Number white points N : Number of all points p : Porosity percentage

26 C ALCULATING THE P OROSITY

L AYER U NIFORMITY 27

28 L AYER U NIFORMITY

M EASURING L AYER W EIGHT 29