Date post: | 03-Jan-2016 |
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CuDDICopper Digital Detection Imaging
Reasons for Development
• One of the last… • Remove BIAS from Operator to Operator• Digital Recording of Images• LIMS Integration• Standardization on Light Source• Magnification of Sample Strip• Strip Size Determination• Current Chart – NOT REAL• Automatic Measurement• A better rating…
What Color Is it?
Design
*Patent Pending
Vision Algorithm• Define Strip Area –
Automatically• Inspection Area
Determination• 8x8 Pixel scan, ~96,000
points total• Accumulate Results• Calculate Percentage of
each Rating• Pass values through
Support Vector Machine and Algorithm for Categorizing
• Ability to “Train” and Adopt Model
Step 1)Locate Strip and verify dimensions:Must be > Nominal – (Tolerance * 2)e.g. Height = 75mm +/- 5mm, so minimum height must be greater than 65mm. *account for angles
Step 2) Location results are used to define the color inspection region. The region is always slightly smaller (5-20 pixels) than the actual strip, to avoid any unwanted shadows and/or round edges.Black rectangle represents the inspection region.
Step 3)Segment entire search region into small sections (8 by 8 pixels each), and run each sample through the Classifier.About 46,000 total samples per strip.Results are accumulated.
ClassifierDetermine
s which rating the
sample belongs
to.
Extract Color from Sample
Totalize results
Step 4) Calculate percentage of each and determine the most corrosive and the most dominant result using both sides of the strip, and provide analytics:• Highest Ranking• Most Corrosive Ranking• A breakdown (in percentages) of each side of the strip
Rank
Hue Saturation Intensity Image
1a 29.5 Compare 242.3
Compare 225.7
Ignore HDR Dark
1b 42.8 Compare 252.4
Compare 231.5
Ignore HDR Dark
2a 9.7 Compare 172.8
Compare 141.9
Ignore HDR Med.
2b 9.6 Compare 150.9
Compare 191.5
Ignore HDR Med.
…
4a 17.8 Ignore 51.7 Ignore 113.0
Compare
HDR Med.
4b 18.1 Ignore 76.5 Ignore 61.5 Compare
HDR Med.
4c 187.1
Ignore 41.2 Ignore 41.1 Compare
HDR Med.
The ClassifierGiven a set of trained models, color samples are compared against Hue, Saturation, and Intensity.
Allows multiple samples to be trained.
A portable Color Model Library (saved in xml), allows for different color libraries to be used.
Result = 1a
Satu
rati
on
255
0
0
1a
Hue 360
1b
2a2b
Color Model Library
Data Output• PDF Reports• Email• LIMS Output• Raw Images• Log to Network
Share• USB• Print
Manual 1b 1b 4c 2c 2cCuDDI 1a 1b 4b 3a 3a
Dim. 13.11 x 73.70mm
12.76 x 73.24mm
12.64 x 76.95mm
11.25 x 71.61mm
11.78 x 74.96mm
Data 0 – 2%1a - 89%1b – 7%2e – 2%
1a - 65%1b – 26%2e – 7%
3b – 1%4a – 2%
4b – 93%
0 – 1%2a – 10%2c – 20%2e – 23%3a – 34%4a – 9%
2a – 1%2b – 6%2c – 31%3b – 37%4a – 23%
Notes Over-Rated 4a – 4b Common Missed Green
Data
Sample ID: Manual CUDDI
1 1B 1B
2 3A 2B
3 2A 2A (Highest)3B (Most Corrosive)
4 1A 1A
5 2E 2E (Highest)4A (Most Corrosive)
6 4C 4C
7 2B 4A
8 1B 1B (Highest)2A (Most Corrosive)
9 2C 2C
10 4A 4A
11 3B 3B
12 4C 4A
13 2A 2A
14 2C 2C (Highest)4A (Most Corrosive)
Manual Rating CuDDI Method
Human Eye Digital CMOS Camera
BIASED Removes BIAS
Variable Lighting Fixed Standardized Lighting
Decision Making Fixed Algorithm*
No Digital Record Logs Results with Images
Flat Viewing Tube or Hands Innovative Holder for Strip
Differences
• Lighting• Fingerprints• Dimensions• Proper Polishing• Holes in D1838• Water Spots – LPG Samples• 2a to 3a Logic Handling • 2c to 3b Logic Handling
Critical Points
• Round Robin• D130• D1838• D4048• EN & IP Methods
• Demo Units Available• ANNEX in Current Methods?• Alternate Procedure? … lighting only?• Feedback from Committee Groups• Request
Plans & Future
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CuDDI
by:
Juan J Ayala+1 (312) 622-7520
THANK YOU! QUESTIONS?