1. Machine Vision for the Life Sciences
Presented by:
Niels Wartenberg
June 12, 2012
Track, Trace & Control Solutions

2. Niels Wartenberg Microscan Sr. Applications Engineer, Clinical
Senior Applications Engineer on Microscan's Clinical Team and regular instructor of identification technology courses, Mr. Wartenberg has been part of the Microscan Team since 2000.
Prior to joining Microscan he gathered over 8 years experience implementing solutions in clinical laboratory systems.

3. Machine Vision
is increasingly adopted as an effective means of automating critical processes and increasing laboratory throughput

4. Faster
More Repeatable

5. Machine Vision and Auto ID Converge
Microscan legacy:
30+ years in Auto ID
30+ years in Machine Vision
Read bar codes, PLUS:
Measure Vials
Check Fill Level
Verify Cap Alignment
…and More

6. Agenda System Configurations Machine Vision Basics Software Tools
Definitions
Uses in the Life Sciences
System Configurations
Smart Cameras
PC-based Systems
Software Tools
Image Processing
Image Analysis
Typical Applications
Identification
Inspection
Measurement
Robotic Guidance

7. The automatic extraction of information from digital images.
MACHINE VISION
The automatic extraction of information from digital images.

8. Examples of Useful Information
Presence/Absence of a Component
Location/ Orientation of an Object
Reading of a Human or Machine Readable Code
Non-Contact Measurement of a Dimension

9. Application Examples Reading 1D symbols on microplates
Reading 2D symbols on vials/racks
Detecting correct orientation of slides
Inspecting print quality on tubes
Inspecting drops of dispensed liquid
Guiding a lab robot to pick & place specimen tubes
Check presence/absence of consumables (e.g. pipette tips, vials or other labware)

10. System Configurations

11. COMMUNICATION
PROCESSING
SENSOR
LENS
LIGHTING
PART
PART

12. Generally, if the feature cannot be seen, it cannot be analyzed
Lighting
Proper lighting is essential to a successful machine vision application
Reveals features we want to detect/analyze
Minimizes everything else
Key choices
Type of light
Light placement with respect to the part and camera
Surface geometry & texture of part are key factors in determining lighting
Generally, if the feature cannot be seen, it cannot be analyzed

13. Lens Gather light & deliver to the image sensor Determine: Focal Point
Field of View (FOV)
Depth of Focus
Lens & extension tubes

14. Lens Configurations Fixed, interchangeable lenses Autofocus lenses
C-Mount standard
Used with standard or smart cameras
Autofocus lenses
Mechanical or liquid lens autofocus
Used in fully integrated imagers

15. Sensor is inside the camera
Image Sensors
Sensor is inside the camera
Captures light and converts it to a digital image
More pixels = more detail
Higher resolution required when:
Resolving the narrow line in a small bar code
Seeing small defect on a part
Making a precise dimensional measurement
.3MP sensor
2MP sensor

16. Machine Vision Cameras
Digital cameras
Most modern machine vision cameras
Alternative standards
Camera Link
Firewire (IEEE 1394)
USB (2.0 and 3.0)
GigE

17. GigE Vision® Standard GigE Vision standard GigE Vision advantages
Developed by the Automated Imaging Association (AIA)
Adopted by industry
Advantages over other standards
GigE Vision advantages
High bandwidth for fast transfer of large images
Uncompromised transfer up to 100 meters
Standard h/w & cables for easy, low cost integration
Standard h/w to connect multiple cameras to single/multiple computers
Highly scalable to follow Ethernet bandwidth to 10GigE & beyond

18. Software
Tools

19. Vision Processing Steps
Acquire Image
Image Processing
Image Analysis
Decision
Logic
Communicate
Results
Modify the image to make features stand out
Extract features from the image
Measure features and compare to specification
Communicate Pass/Fail decisions and other data

20. Image Processing vs. Image Analysis Tools
Original Image -> New Image
Used to make image easier to interpret or analyze
Image Analysis
Image -> Features
Typical features include an edge, line, object, etc.

21. Image Processing Tool Examples
Image arithmetic
Image warping
Binary & grayscale morphology

22. Image Warping Often used prior to OCR (Optical Character Recognition)
Rotate text viewed at an angle
Unwrap text printed on an arc or a circle

23. Create separation and then count Increase Data Matrix cell size
Morphology
Transforms the image to make certain features stand out
Use to expand, separate, merge, clean
Does not extract features
Erode black pixels:
Create separation and then count
Dilate white pixels:
Increase Data Matrix cell size

24. Image Analysis Tool Examples
The Blob Tool
Edge Detection
Pattern Matching
1D & 2D Symbols
OCR & OCV
Dynamic ROI Location
Measurement Resolution

25. Check size to ensure parts are not broken
The Blob Tool
A blob is a group of connected pixels within a size range
similar color (shade of gray)
differ from surrounding area
Typical applications
Count number of parts
Locate position of a part
Measure size of a part
Compare to a tolerance
Count: Verify that all wires are installed
Measure:
Check size to ensure parts are not broken

26. Edge Detection
Edge tools scan an image along a user-specified direction
Detect transitions between two regions of different intensity
Fit a line, circle or ellipse to the edge data
Applications
Detect or locate an object
Measure a distance
Locate a corner
Measure an angle

27. Vector Edge Detection
Edges can be detected along user defined directions (vectors)

28. Edge Tool Usage Locate: Use two Edge tools to find a corner
Measure: Measure fill level of a container or detect cap tightness
Locate: Check label placement

29. Finding Patterns in an Image
Normalized correlation based template matching
Scans template across image and identifies best match
Affected if part of what is in the template is missing from the image being analyzed
Geometric edge pattern matching
Matches patterns of edges in the image and the template
Is not affected if part of the template is missing in the image or if the polarity of the image is reversed

30. Intellifind Tool Example
Pattern Matching
Pattern matching tools learn the outline of a part of pattern
Scans across image and identifies best match
Locates pattern with sub- pixel accuracy
Intellifind Tool Example

31. 1D & 2D Symbols Linear (1D) Bar Code Symbols
Limited data storage
Height provides redundancy
Requires higher contrast
2D Symbols (ie, Data Matrix)
Data encoded in both height & width
Readable 360 °
Contrast as low as 20%

32. OCR - Optical Character Recognition
Decodes human readable text
Can handle dot matrix & dot peen printing
Noisy backgrounds
Uneven lighting
Trainable
Neural Network based
Character addition/deletion
Tolerates scale changes

33. OCV – Optical Character Verification
Typical Application
Checking correctness and legibility of a printed label or text
Detects printing defects
Confidential Information - Under NDA

34. OCR vs. OCV Terms often used incorrectly – NOT the same
OCR: Optical Character Recognition
OCV: Optical Character Verification
OCR – an automatic identification tool
Intended to decode human readable information
OCV – a print quality inspection tool
Intended to flag & reject poor quality text

35. Fundamentals of Machine Vision
Dynamic ROI Location
Relocating regions of interest (ROI) to compensate for part position and rotation
ROIs After
Part Motion
ROIs Corrected For Part Movement
ROIs Positioned

36. Nominal Measurement Resolution
Fundamentals of Machine Vision
Nominal Measurement Resolution
Example:
Paper Clip = 1.0 "
Image width = 1280 Pixels
Resolution =
1.0 " /1280 Pixels
= " /Pixel
Example:
Car = 13.5’
Image width = 1280 Pixels
Resolution =
13.5’ / 1280 Pixels
162’’ /1280 Pixels
= .127" /Pixel

37. Actual Measurement Resolution
Fundamentals of Machine Vision
Actual Measurement Resolution
Depends on:
Sensor resolution
Field of View(FOV)
Sub-pixel capability of vision processing tools

38. Typical
Applications

39. Automatic Identification Applications
Specimen ID
Reagent ID
Tube carrier ID
Microtiter plate ID
Microtiter vial ID

40. Cap Color Detection

41. Tube & Cap Inspection Application requirements Solution Rack location
Bar code reading
Tube/cap inspection
Solution
Camera
Custom GUI

42. Absence/Presence with compact Smart Camera

43. Vision Guided Motion Applications
Application requirements
Determine object location & orientation
Calibration in real world units
Robust & accurate pattern matching
Application examples
Robotic tube pick & place
Colony picking

44. Machine Vision Automate critical processes Increase throughput
Achieve verifiable process repeatability

45. Thank You
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