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Microscanwww.Microscan.com Machine Vision for the Life Sciences
Track, Trace & Control Solutions Presented by: Niels Wartenberg
June 12, 2012
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P 800.762.1149 | www.Microscan.com | Copyright 2012
Microscanwww.Microscan.com 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.
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P 800.762.1149 | www.Microscan.com | Copyright 2012
Microscanwww.Microscan.com is increasingly adopted as an effective
means of automating critical processes and increasing laboratory
throughput Machine Vision
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Microscanwww.Microscan.com More Repeatable Faster
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Microscanwww.Microscan.com 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
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P 800.762.1149 | www.Microscan.com | Copyright 2012
Microscanwww.Microscan.com Agenda Machine Vision Basics 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
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Microscanwww.Microscan.com The automatic extraction of information
from digital images. MACHINE VISION
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Microscanwww.Microscan.com 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
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Microscanwww.Microscan.com 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)
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Microscanwww.Microscan.com System Configurations
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Microscanwww.Microscan.com PROCESSING SENSOR LIGHTING LENS
COMMUNICATION PART
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Microscanwww.Microscan.com 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
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Microscanwww.Microscan.com Lens Gather light & deliver to the
image sensor Determine: Focal Point Field of View Field of View
(FOV) Depth of Focus Lens & extension tubes
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Microscanwww.Microscan.com Lens Configurations Fixed,
interchangeable lenses C-Mount standard Used with standard or smart
cameras Autofocus lenses Mechanical or liquid lens autofocus Used
in fully integrated imagers
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Microscanwww.Microscan.com Image Sensors 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 Sensor is inside the camera 2MP sensor.3MP sensor
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Microscanwww.Microscan.com Digital cameras Most modern machine
vision cameras Alternative standards Camera Link Firewire (IEEE
1394) USB (2.0 and 3.0) GigE Machine Vision Cameras
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Microscanwww.Microscan.com GigE Vision Standard GigE Vision
standard 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
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Microscanwww.Microscan.com Software Tools
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Microscanwww.Microscan.com Vision Processing Steps Modify the image
to make features stand out Acquire Image Image Processing Image
Analysis Decision Logic Communicate Results Extract features from
the image Measure features and compare to specification Communicate
Pass/Fail decisions and other data
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Microscanwww.Microscan.com Image Processing vs. Image Analysis
Tools Image Processing 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.
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Microscanwww.Microscan.com Rotate text viewed at an angle Image
Warping Often used prior to OCR (Optical Character Recognition)
Unwrap text printed on an arc or a circle
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Microscanwww.Microscan.com 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
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Microscanwww.Microscan.com 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 Measure: Check size to ensure parts are not broken Count:
Verify that all wires are installed
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Microscanwww.Microscan.com 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
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Microscanwww.Microscan.com Vector Edge Detection Edges can be
detected along user defined directions (vectors)
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Microscanwww.Microscan.com Edge Tool Usage Measure: Measure fill
level of a container or detect cap tightness Locate: Use two Edge
tools to find a corner Locate: Check label placement
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Microscanwww.Microscan.com 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
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Microscanwww.Microscan.com Pattern Matching Intellifind Tool
Example Pattern matching tools learn the outline of a part of
pattern Scans across image and identifies best match Locates
pattern with sub- pixel accuracy
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Microscanwww.Microscan.com 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%
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Microscanwww.Microscan.com 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
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Microscanwww.Microscan.com OCV Optical Character Verification
Typical Application Checking correctness and legibility of a
printed label or text Detects printing defects
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Microscanwww.Microscan.com 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
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Microscanwww.Microscan.com 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