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SEMI T10-0701 © SEMI 2001 4 7 Procedure 7.1 Location and Or ientation of th e Da ta Matri x Symbol 7.1.1 Data Matrix Location Descrip t o r s 7.1.1.1 T he implementation of the sym b o l q uality assessm ent depends on k…

SEMI T10-0701 © SEMI 20013

# of Pixels

Grayscale Value

Figure 2

Pixel Histogram

5.1.18 quiet zone  areas of space surrounding the

machine-readable symbol. Quiet zone requirements

may be found in application and symbology

specifications. Sometimes called “Clear Area” or

“Margin.”

5.1.19 space



an unmarked cell or area of a Data

Matrix Symbol.

5.1.20 symbol  a machine-readable pattern

comprised of a quiet zone, finder pattern, symbology

characters (which include special functions and error

detection and/ or correction characters) required by a

particular symbology.

5.1.21 symbol contrast  the difference in grayscale

values between the marked and unmarked areas of a

Data Matrix symbol.

6 Summary of Method and R equirements

6.1 Test Image

6.1.1 A test image of the mark shall be obtained in a

configuration that mimics the typical reading

configuration for that mark, at substantially the same

resolution, illumination, optical focus, image exposure,

gain or other image signal preprocessing settings that

are used during reading. Individual applications may

dictate a specific wavelength or color temperature and

spatial construction of illumination, image resolution,

optical focus, image exposure, etc. The mark/reader

configuration shall be consistent to the extent that

repeatable results can be obtained from a single sample

over many instances of one mark/reader configuration.

6.1.2 The intent of the assessment p rocedure is to

regard a test image from the actual mark/reader

configuration as the source for all assessment.

Therefore, the assessment criteria and the algorithms

used to carry out each assessment should be resident in

the actual reading system or equivalent that is used to

read the mark. If a separate assessment or verification

system, which has different illumination characteristics,

resolution, etc., is used, uncorrelated results may occur.

6.2 Edge Detection Method

6.2.1 Many of the assessment proce dures depend

significantly on the type of edge detection method that

is employed to locate edges in the test image which

define the bounds of the entire Data Matrix symbol as

well as individual cells within the matrix. The edge

detection method used should be consistent throughout

the assessment process.

SEMI T10-0701 © SEMI 2001 4

7 Procedure

7.1 Location and Orientation of the Data Matrix

Symbol

7.1.1 Data Matrix Location Descrip tors

7.1.1.1 The implementation of the symbol quality

assessment depends on knowledge of the location and

orientation of the Data Matrix symbol in the image,

which consists of the 4 image coordinate values of the

matrix corner points, P1, P2, P3, and P4 (see Figure 3).

In addition, the number of rows M and the number of

columns N in the Data Matrix symbol is also required

(see Figure 3). Determine the image coordinate values

and the number of rows and columns using a pre-

processing decoding procedure resident in the reading

system. Should this process either fail or not be

available, determine these values by inspection of the

test image. Report the coordinate values of each matrix

corner point and the number of rows and columns.

7.1.2 The Data Matrix Grid

7.1.2.1 Based on the location of the 4 matrix corner

points and the number of rows and columns in the

matrix establish a geometrical 2 dimensional matrix

grid. Establish line segment P1P2 using points P1 and

P2, line segment P2P3 using P2 and P3, line segment

P3P4 using P3 and P4, and finally line segment P1P4,

using P4 and P1. Divide the number of rows M into the

P1P2 line segment, producing M equally sized line

segments. Divide the number of rows M into the P3P4

line segment producing a similar result. Divide the

number of columns N into P1P4 line segment,

producing N equal sized line segments. Divide the

number of columns N into the P2P3 line segment,

producing a similar result. Connect the corresponding

new line segment endpoints between P1P2 and P3P4.

Connect the corresponding new line segment endpoints

between P1P4 and P2P3. This results in a 2

dimensional matrix grid similar to that shown in Figure

3. This grid is generally quadrilateral in shape,

practically rectangular and ideally square.

7.1.3 Data Matrix Cell Center Point s

7.1.3.1 Determine the ideal geometric al center points

in each matrix cell in the following manner. Establish a

new set of points at the bisection of each row and

column line segment used to form the matrix grid.

Form line segments between each corresponding row

bisection point in P1P2 and P3P4. Do the same for the

column bisection points in P1P4 and P2P3. The

intersection points between this new set of lines in each

matrix cell is the ideal geometrical matrix cell center

point (see Figure 4).

Figure 3

Data Matrix Corner Points and Mark Growth Scanline

SEMI T10-0701 © SEMI 20015

Figure 4

Matrix Cell Center Locations

7.2 Symbol Contrast

7.2.1 Symbol Contrast measures the distinctiveness of

the two reflective states in the symbol, namely light and

dark. Many aspects of the mark/reader configuration

affect the resulting symbol contrast in the image.

7.2.2 Within the grayscale image, se lect all of the

image pixels, which fall within the area of the symbol,

extending outward to the limits of any required quiet

zones. Sort the selected pixels by their reflectance

values into a histogram. Calculate the arithmetic mean

of the reflectance histogram. Use this mean value to

separate the reflectance histogram into 2 sections or

sub-histograms, which represent approximate

reflectance histograms for the dark and light contrasts

of the symbol. Calculate the arithmetic mean of each

sub-histogram. The lower grayscale value of the two

means represents the dark grayscale value G

and the

higher value represents the light grayscale value G

Use these two grayscale values to calculate a new

symbol contrast. The difference of the grayscale levels

divided by the full range of grayscale (255 in the case

of 8 bit A/D Sampling) is the Symbol Contrast. Report

the Symbol Contrast value.

100%

Range(255)Grayscale

SC ×

−

= (1)

7.3

Symbol Contrast Signal to Noi se Ratio (SNR)

7.3.1 The Symbol Contrast Signal to Noise Ratio

(SNR) is a relative measure of the symbol contrast

(signal) to the maximum deviation in the light or dark

grayscale level in the symbol (noise). Using the sub-

histograms and the two grayscale values G

and G

found in the Symbol Contrast calculation, calculate the

deviation of each sub-histogram about each respective

grayscale value. Two values will result: the deviation

of light pixels D

and the deviation of dark pixels D

Calculate a symbol contrast difference in grayscale

value using the grayscale values. Divide this contrast

difference by the maximum deviation calculated for the

individual light and dark sections of the histogram.

This ratio is the Symbol Contrast SNR. Report the

symbol contrast SNR.

()

DMax

SNRContrastSymbol

−

= (2)