is discriminate function find 
by the perception training shown in Fig.7. Perception Training
The perception training is the problem of finding if is discriminate function find by the perception training shown in Fig.7.
Fig.7 The perception training
A vector W that correctly separates all the training patterns. i.e.
are augmented training patterns, that is 
, if 
is a training pattern, from class 
.
is a (d+1) vector.
For example, if there are five 1-d patterns distributed on the feature space (Fig.8):
{2, 3, 4} : three training patterns from
{
-1,-0.5}: two training patterns from
Fig.8 the training patterns
Then we can obtain the augment training pattern:
as show in Fig.9.
Fig.9 the augmented pattern space
we can see that:
Fig.10 The weighting space
In Fig.10 we can obtain the allowable area of W. We can choose a suitable W to discriminate all patterns into groups of object and background[4]. Here we introduce a 7_7 discriminate mask operation as follows (Fig.11):
Fig.11 a 7 X 7 discriminate mask operation
1.notation
ROI: region of interest in LCD plate.
T1:the test region of a discriminate function.
g(.): to detect are there any spacers in T1
mark(.): to label the pixel
2.initialization
max=0 ,min= 255
3.recursion
repeat
for all
find out the maximum and minimum value of T1.
if g (max-min)==true
mark();
else
let T1 to next position
until T1 out of ROI
index