machine vision for the life sciences - amazon web …...uses in the life sciences system...

P 800.762.1149 | www.Microscan.com | Copyright 2012 Microscan

Machine Vision for the Life Sciences

Track, Trace & Control Solutions

Presented by:

Niels Wartenberg June 12, 2012

http://www.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.





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

Machine Vision





More Repeatable Faster





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

LENS

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





Software Tools





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





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 data

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 After Part Motion

ROIs Corrected For Part Movement

ROIs Positioned





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





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 [email protected].

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




mailto:[email protected]

http://www.microscan.com/

machine vision for the life sciences - amazon web …...uses in the life sciences system...

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