machine vision for the life sciences

Machine Vision for the Life Sciences

Track, Trace & Control Solutions

Presented by:

Niels WartenbergJune 12, 2012

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.

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

Machine Vision

More RepeatableFaster

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

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

The automatic extraction of

information from digital images.

MACHINE VISION

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

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)

System Configurations

PROCESSING

SENSOR

LIGHTING

COMMUNICATION

PART PART

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

Lens Gather light & deliver to the image sensor Determine:

Focal Point Field of View (FOV)

Depth of Focus

Lens & extension tubes

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

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

Digital cameras Most modern machine vision cameras Alternative standards

Camera Link Firewire (IEEE 1394) USB (2.0 and 3.0) GigE

Machine Vision Cameras

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

SoftwareTools

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

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.

Image Processing Tool Examples

Image arithmetic

Image warping

Binary & grayscale morphology

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

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

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

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

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

Applications Detect or locate an object Measure a distance Locate a corner Measure an angle

Vector Edge Detection

Edges can be detected along user defined directions (vectors)

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

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

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

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%

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

OCV – Optical Character Verification

• Typical Application Checking correctness and

legibility of a printed label or text

• Detects printing defects

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

Dynamic ROI Location• Relocating regions of interest (ROI) to compensate

for part position and rotation

ROIs AfterPart Motion

ROIs Corrected ForPart Movement

ROIsPositioned

Nominal Measurement Resolution

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

Actual Measurement Resolution

• Depends on: Sensor resolution Field of View(FOV) Sub-pixel capability of vision processing tools

Typical Applications

Automatic Identification Applications

Specimen ID Reagent ID Tube carrier ID Microtiter plate ID Microtiter vial ID

Cap Color Detection

Tube & Cap Inspection Application requirements

Rack location Bar code reading Tube/cap inspection

Solution Camera Custom GUI

Absence/Presence with compact Smart Camera

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

Machine Vision Automate critical

processes Increase throughput Achieve verifiable

process repeatability

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.

machine vision for the life sciences

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