mmdpieces
Classify two dimensional pieces.

The binary image of the pieces is read.

a = mmreadgray('pieces_bw.tif');

mmshow(a);

a

A thickening that preserves the number of connected objects is applied to reduce contour noise.

seA = mmimg2se(mmbinary([[0,1,0],[1,0,1],[0,0,0]]))

seB = mmimg2se(mmbinary([[0,0,0],[0,1,0],[0,0,0]]))

iAB = mmse2hmt(seA,seB);

print mmintershow(iAB)

. 1 . 
1 0 1 
. . .

b = mmthick(a, iAB);

mmshow(b);

b

The homotopic skeleton by thinning is created.

c = mmthin(b);

mmshow(c);

c

The open lines of the skeleton are pruned by the end point thinning. The remaining skeleton components will be loops, identifying the rings.

d = mmthin(c,mmendpoints());

mmshow(c,d);

c,d

Extraction of the rings by reconstruction of the thicked image from the filtered skeleton.

e = mminfrec(d,b);

mmshow(e);

e

Restriction of the objects detected to the input-image.

f = mmintersec(a,e);

mmshow(f);

f

It eliminates the skeleton of the rings.

g = mmsubm(c,e);

mmshow(g);

g

It removes sucessively 4 end-points to let T junctions just on T-pins.

h = mmthin(g, mmendpoints(), 4);

mmshow(h);

h

It detects triple points, applying the union of matchings with the template rotated 90 degrees. These points will identify (mark) the T-pins.

seA2 = mmimg2se(mmbinary([[0,1,0],[1,1,1],[0,0,0]]))

seB2 = mmimg2se(mmbinary([[1,0,1],[0,0,0],[0,1,0]]))

i = mmsupcanon(h, mmse2hmt(seA2,seB2));

mmshow(h,mmdil(i,mmsedisk(2)));

h,mmdil(i,mmsedisk(2))

Detection of the T-pins by reconstruction of the ticked image from the T-pin markers.

j = mminfrec(i,b,mmsebox());

mmshow(j);

j

Restriction of the objects detect to the input image

k = mmintersec(a,j);

mmshow(k);

k

The nails are imediatly detected by the subtration of the images of the rings and T-pints from the input image.

l = mmsubm(mmsubm(a,f),k);

mmshow(l);

l

The result of the classification is presented in a pseudo color image.

m = mmgray(f,'uint8',1);

n = mmgray(k,'uint8',2);

o = mmgray(l,'uint8',3);

p = mmunion(m,n,o);

mmlblshow(p);

p