the Life Sciences

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Machine Vision for
the Life Sciences

Track, Trace & Control Solutions




Presented by:

Niels Wartenberg

June 12, 2012

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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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is increasingly
adopted as an
effective means of
automating critical
processes and
increasing laboratory
throughput


Machine Vision

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More Repeatable

Faster


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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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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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The automatic extraction of
information from digital
images.

MACHINE VISION

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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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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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System
Configurations

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PROCESSING

SENSOR

LIGHTING

LENS

COMMUNICATION



PART



PART

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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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Lens


Gather light & deliver to the image sensor


Determine:


Focal Point


Field of View
(FOV)


Depth of Focus

Lens &
e
xtension
tubes

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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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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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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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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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Software

Tools

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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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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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Image Processing Tool Examples


Image arithmetic



Image warping


Binary & grayscale
morphology



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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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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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Image Analysis Tool Examples


The Blob Tool


Edge Detection


Pattern Matching


1D & 2D Symbols


OCR & OCV


Dynamic ROI Location


Measurement Resolution






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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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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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Vector Edge Detection


Edges can be
detected along
user defined
directions
(vectors)

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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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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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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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1D & 2D Symbols


Linear

(1D) Bar Code Symbols


Limited data

storage


Heigh
t provides redundancy


Require
s higher contrast


2D Symbols (
ie
, Data Matrix)


Data encoded in both height & width


Readable

360

°


Contrast as low as 20%


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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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OCV


Optical Character Verification


Typical Application


Checking correctness and
legibility of a printed label or
text



Detects printing defects

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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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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

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Nominal Measurement Resolution

Example:

Example:

Paper Clip = 1.0 "

Image
width = 1280
Pixels

Resolution =

1.0 "
/1280
Pixels

= .
0008
" /Pixel

Car = 13.5’

Image
width = 1280
Pixels

Resolution =

13.5’ / 1280
Pixels

162’’ /1280
Pixels

=
.127"
/Pixel

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Actual Measurement Resolution


Depends on:


Sensor resolution


Field
of View(FOV
)


Sub
-
pixel
capability
of vision processing tools


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Typical

Applications

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Automatic Identification Applications


Specimen ID


Reagent ID


Tube carrier ID


Microtiter plate ID


Microtiter vial ID

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Cap Color Detection

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Tube & Cap
Inspection


Application requirements


Rack location


Bar code reading


Tube/cap inspection


Solution


Camera


Custom GUI



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Absence/Presence with compact
Smart Camera

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Application requirements


Determine object location &
orientation


Calibration in real world units


Robust & accurate pattern
matching


Application examples


Robotic tube pick & place


Colony picking



Vision Guided Motion Applications




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Machine Vision


Automate critical
processes


Increase throughput


Achieve verifiable

process repeatability



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Thank You



If you have questions regarding this
webinar or topic, please an e
-
mail to
info@microscan.com
.



For further information about
machine vision, visit our website at
www.microscan.com
.