Machine Vision Tools for Solving Auto ID Applications

Track, Trace & Control Solutions© 2010 Microscan Systems, Inc.

Machine Vision Tools for Solving Auto ID Applications

Part 3 of a 3-part webinar series: Introduction to Machine Vision

© 2010 Microscan Systems, Inc. © 2010 Microscan Systems, Inc.

About your Instructors

Dr. Jonathan LudlowMachine Vision Product Manager

Juan WorleTechnical Training Coordinator


Today’s Objectives

By the end of this Webinar, you will know:

Most common Machine Vision tools How you can use these tools in the most common applications


Today’s Topics

Today we will discuss:

Machine Vision tools

Helper Tools

Combining Machine Vision tools


Machine Vision Tools

The job of a Machine Vision tool is to extract useful information from an image.

We will focus on the tools that Measure, Decode, Count and Locate: 1D and 2D decoding Optical Character Recognition (OCR) Blob Edge Intellifind


1D and 2D Decoding

Decode tools for Data Matrix and linear barcodes: Decode tools are the same as you would find in a 2D imager Validates or reports the data within a code

You must have at least 4 sensor pixels

on each element

Quiet zone should be 10% of Data Matrix size

You must have at least 1 sensor pixel on the narrow bar

Quiet zone should be 5x the narrow bar size

ROI: Machine Vision tool use Regions of

Interest to search for features


Optical Character Recognition (OCR)

OCR tools can be used to read ‘Human Readable’ text: Default fonts are industry standards for OCR Teach your own font for more flexibility Validate the OCR data

Four examples of industry standard OCR fonts

Trainable OCR tools let you learn a non-standard font


Optical Character Recognition (OCR)

Make your OCR application reliable: OCR characters should be 25 pixels wide Each character should have a quiet zone Avoid large gaps in the character

The sample on the left does not have enough spacing. The sample on the right has good spacing,

even background, and good character size.We will deal with the space between the dots later.


Optical Character Recognition (OCR)OCR characters on a noisy background, poorly marked characters, and handwriting are examples of applications to carefully consider. Be careful of fonts with similar characters like O 0 and D Suggest improvements to make it reliable

OCR algorithms for handwriting are specialized and time-consuming

Poor print quality and text through the background will cause

reliability problems.


Blob Tool: Connectivity ToolA blob is any group of pixels within a size range that are of a similar color (shade of grey) and are different than the surrounding area. Count the number of blobs Measure the size of a blob Locate the position of a blob Compare to a tolerance

Count: Verify 3 wires are installedMeasure: Ensure pills are

not broken by comparing to a size tolerance


Blob Tool: Connectivity Tool

When using the Blob tool, consider: Blobs are threshold-based (converted to black and white) Blobs must stand out from the background There must not be other blobs in the area that can be mistaken as

the object

Count: Check ball count by counting the number of

blobs in 30-50 pixel range

Count: Look for debris by counting the number of blobs

greater than 50 pixels


Edge Tools

Edge tools scan an image for an edge: Locate an object Locate a corner Measure the distance between edges Measure the angle of an edge Compare to tolerances

Close-up: Edge tools look for transitions in grayscale and fit an

edge to the transitions


Edge Tools

When searching for an edge, consider the following: Edge tools scan in one direction that is user-defined Edge tools fit a line to the transition in grayscale Search from the cleanest side to the noisy side

Measure: Two Edge tools are used to measure the fill level

inside a container

Locate: Use two Edge tools to find the corner

Two Edge tools check that a label is in the right place


IntellifindMicroscan’s powerful pattern matching tool searches for a pattern within the Field of View: Locate shapes and outlines Identify parts

Learns the outline or shape to locate parts Allows for incomplete parts Angle and scale can change Locate if background changes Locate if the polarity changes


Intellifind

When selecting a pattern to match: The pattern should be unique Use simple patterns that are easy to identify

Intellifind is used to verify the type of product cartons currently being run

A similar application is sorting based on product

Intellifind is used to locate the position of a part and report the information to a robot for precise positioning


Helper Tools

Tools that prepare an image for other tools are called helper tools. These tools will produce a new image that other tools will use.

Two tool types that we will discuss today:

Morphology

Dynamic Location


Morphology

Transforms the image to make features stand out: Use to expand, separate, merge, clean Does not extract features

Erode black pixels: Create separation, then count using Blob tool Dilate white pixels: Increase

Data Matrix cell size to correct for poor cell fill


Morphology

When using Morphology to alter an image: The separation between features must be larger than the gaps

being closed. Be careful that small features do not disappear when eroding

Erode white: Decrease the size of white area to link up the dots, produces a more reliable OCR read


Dynamic Location

Use a Locate tool like Intellifind to position measurement or ID tools: Allows loose fixturing or random presentation Precise tool positioning

Locate and Measure: Intellifind locates the spark plug and the Blob tool is used to measure the terminal


Combining Machine Vision Tools

Machine Vision tools can be combined together to build a complete inspection.

When combining tools:

Keep the application simple

Remember that the image must be consistent


Combining tools

Read Data Matrix and OCR then validate the data. Measure the part and check for o-ring assembly.

Two camera system compares lid to container using Intellifind. When they do not match, the line shuts down and warns an operator.

Automotive Fuel Injector Food Packaging


Machine Vision Tools for Auto ID Applications

Conclusion The more common Machine Vision tools can be categorized into the

top applications: Measure, Decode, Read, Count and Locate These tools include: 1D/2D barcodes and OCR decoding, Blob, Edge,

and Intellifind Each tool has unique characteristics that can be used to make the

application reliable Morphology and Dynamic Location tools will modify an image to

enhance features or make them more reliable


Thank you!For More informationWebsite: www.microscan.com

– Online courses– Spec sheets– Technology Brochures– Support Self-help and support request form

Instructors:Juan Worle, Technical Training Coordinator Email: [email protected]. Jonathan Ludlow, Machine Vision Product Manager Email: [email protected]

Feedback on this webinar: www.microscan.com/feedbackAdditional contacts:

Additional product information: [email protected]: [email protected]: [email protected]

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Machine Vision Tools for Solving Auto ID Applications

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