automated visual inspection using inductive learning

Visual InspectionProduct reliability is of maximum importance

in most mass-production facilities.100% inspection of all parts, subassemblies, and

finished products. Therefore, the inspection process is normally

the largest single cost in manufacturing.Most difficult task for inspection is that of

inspecting for visual appearance. Visual inspection seeks to identify both functional

and cosmetic defects.Visual inspection in most cases depends

mainly on human inspectors.Slide 2Automated Visual

InspectionUsing Inductive Learning

Automated Visual InspectionHuman inspectors are slow compared to modern

production rates, and they make many errors. Automated visual inspection (AVI) is obviously the

alternative to the human inspector. Several practical reasons for automated

inspection include: Freeing humans from dull and routine. Saving human labor costs. Performing inspection in unfavorable environments. Reducing demand for highly skilled human inspectors. Analyzing statistics on test information and keeping records

for management decisions. Matching high-speed production with high-speed inspection.

Slide 3Automated Visual InspectionUsing Inductive Learning

Visual Inspection TechniquesThere are many techniques for automated visual

inspection: Image subtraction:

─ The inspected image to be is scanned and compared against the original image, which has been stored before.

─ The subtracted image is analyzed. ─ This method needs large reference data storage, accurate

alignment, sensitive lighting and scanner conditions. ─ Also many images may not match point-by-point identically even

when they are acceptable. Dimensional verification:

─ The distance between edges of geometric shapes is the primary feature of this inspection method.

─ The task is to make a determination for each measurement as to weather it falls within the previously established standards.


Visual Inspection Techniques (cont.) Syntactic approach:

─ Uses descriptions of a large set of complex objects using small sets of simple pattern primitive and structural rules.

─ Primitives are small number of unique elements, as lines or corners.

─ A structural description of the primitives and the relationships between them can be determined to form a string grammar.

Feature (Template) Matching:─ The inspected image is scanned and the required features are

extracted. ─ Then these features are compared with those defined for the

perfect pattern. ─ This method greatly compresses the image data for storage and

reduces the sensitivity of the input intensity data. ─ A number of predefined binary templates can be used to extract

the necessary features for images to be inspected. Slide 5Automated Visual


Template Matching TechniqueMask technique can be used, with number of

predefined binary templates, to extract the necessary features for inspected images.

The total number of 3x3 mask templates is 28. This number is calculated as follows:

The total number of black pixels in each mask is 3. The reason of using 8 is that, the central pixel is always

black. The rest of 8 pixels only 2 pixels can be black and the

rest must be white. Slide 6Automated Visual


28)!28!*(2

!828

CNumber of masks =

28 of 3x3 Masks

Automated Visual InspectionUsing Inductive Learning

Slide 7

Template Matching Technique (cont.)The reason for choosing 3x3 masks is to

reduce the processing time.It is possible to have 5x5, 7x7 or some other masks. If the size of the mask is bigger the accuracy may

increase but the processing time will also increase.

All 28 masks may not always be required to use for the applications. The experience from many applications shows that,

10 to 15 masks are good enough to be employed.

How to select the suitable masks for each application is an important problem.


28 of 3x3 Masks


Slide 9

R R

R

RR

RR

RR

R

R

20 of 3x3 Masks


Slide 10

Mask SelectionIn order to select a proper number of masks, the

following steps can be considered : Select a number of example images. Apply 28 masks and calculate the frequency of each. Find the average frequency of each mask. Sort the masks according to their average frequencies (from

biggest to smallest). Choose a number of them for the application.

Each mask must be applied to each image pixel-by-pixel from left to right and from top to bottom.

The frequencies may change from one image to another.

We can take the average of all frequencies for the same mask and consider it for selection.Automated Visual InspectionUsing Inductive Learning

Slide 11

Inductive LearningInduction can be considered as the process of

generalizing a procedural description from presented or observed examples.

Inductive inference is the method of moving from specific examples to general rules.

One of the visual pattern recognition goals is developing a system that can learn to classify patterns. First, the system should be trained using a set of

training examples. Then it should use knowledge gained in the training

session to automatically classify new examples. Automated Visual InspectionUsing Inductive Learning

Slide 12

Training SessionA training process proceeds follows:

A number of good parts (examples) are shown to the system.

The frequencies of 20 3x3-masks are calculated. Then an induction algorithm is used to extract the

necessary rules. ─ The extracted set of rules represents the good parts.

When a pattern is shown to the system, using the extracted rules, it can decide whether it is good.

If the pattern cannot be decided as good it means that the pattern is bad (defected). ─ The system does not need to learn bad patterns.


Slide 13

Example Application

Five types of cups were selected.

The pattern is scanned pixel by pixel from left to right and from top to bottom using the 20 masks in order to calculate the frequency of each mask.

For example, the frequencies for Cup-l were calculated as follows:

112, 1423, 31, 27, 56, 55, 57, 56, 262, 267, 265, 261, 195, 196, 197, 201, 5, 5, 208, 218, Cup-lAutomated Visual


Slide 14

• Inspection of water glass cups:

Example Application (cont.)Set of examples for the five cups:

112, 1423, 31, 27, 56, 55, 57, 56, 262, 267, 265, 261, 195, 196, 197, 201, 5, 5, 208, 218, Cup-1

622, 840, 27, 39, 155, 154, 162, 158, 102, 104, 103, 101, 31, 29, 34, 36, 26, 28, 107, 111, Cup-2

230, 621, 37, 40, 22, 22, 22, 22, 116, 109, 109, 116, 18, 18, 19, 19, 45, 45, 18, 18, Cup-3

697 ,715, 2, 1, 10, 10, 10, 10, 91, 94, 91, 94, 10, 11, 9, 11, 4, 3, 1, 2, Cup-4

3739, 622, 72, 77, 557, 575, 560, 579, 144, 155, 154, 138, 521, 533, 523, 533, 542, 543, 110, 113, Cup-5


Slide 15

Example Application (cont.)Rule 1

IF 112 =< Ml < 303 AND 1395 =< M2 < 1438 AND 30 =< M3 < 34 AND 25 =< M4 < 29 AND 39 =< M5 < 68 AND 179 =< MI6 < 207 AND 218 =< M20 < 230 THEN CLASS IS Cup-1

Rule 2 IF 494 =< M1 < 685 AND 836 =< M2 < 879 AND 26 =< M3 <30 AND 37 =< M4 < 41 AND

155 =< M5 < 184 AND 130 =< M8 < 160 AND 100 =< M9 < 109 THEN CLASS IS Cup-2

Rule3 IF 112 =< M1 < 303 AND 621 =< M2 < 664 AND 34 =< M3 < 38 AND 37 =< M4 < 41 AND

10 =< M5 < 39 AND 33 =< M17 < 62 AND 32 =< M18 < 61 THEN CLASS IS Cup- 3

Rule 4 IF 685 =< Ml < 876 AND 707 =< M2 < 750 AND 2 =< M3 < 6 AND 1 =< M4 < 5 AND 10=<

M5 < 39 AND 91 =< M11 < 101 AND 2 =< M20 < 14 THEN CLASS IS Cup-4

Rule 5 IF 3550 =< M1 < 3741 AND 621 =< M2 < 664 AND 70 =< M3 < 74 AND 73 =< M4 < 77 AND

532 =< M5 < 561 AND 526 =< M17 < 555 AND 525 =< M18 < 554 THEN CLASS IS Cup-5


Slide 16

Visual Inspection ApplicationsAutomated visual inspection has very

large application areas:Banknote recognition Signature recognition Fingerprint recognition Number-plate recognition Barcode recognition Inspection of all parts, subassemblies,

and finished products in mass production.Slide 17Automated Visual


automated visual inspection using inductive learning

Documents

automated inspection

inspection method

highspeed inspection

inspection process

visual appearance

image data

image subtraction

original image