machine vision for the life sciences
DESCRIPTION
Machine Vision for the Life Sciences. Presented by: Niels Wartenberg June 12, 2012. Track, Trace & Control Solutions. Niels Wartenberg Microscan Sr. Applications Engineer, Clinical - PowerPoint PPT PresentationTRANSCRIPT
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Machine Vision for the Life Sciences
Track, Trace & Control Solutions
Presented by:
Niels WartenbergJune 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 technologycourses, 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 RepeatableFaster
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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 & extension 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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SoftwareTools
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Vision Processing Steps
Modify the image to make features stand out
AcquireImage
ImageProcessing
ImageAnalysis
DecisionLogic
CommunicateResults
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 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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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 AfterPart Motion
ROIs Corrected ForPart Movement
ROIsPositioned
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Nominal Measurement Resolution
Example:
Example:
Paper Clip = 1.0 "Image width = 1280 PixelsResolution =
1.0 " /1280 Pixels= .0008 " /Pixel
Car = 13.5’Image width = 1280 PixelsResolution =
13.5’ / 1280 Pixels162’’ /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 [email protected].
For further information about machine vision, visit our website at www.microscan.com.