Introduction to Machine Vision Systems

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Introduction toMachine Vision Systems
Professor Nicola Ferrier Room 3128,
ECB 265-8793 ferrier_at_engr.wisc.edu
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Machine Vision

• To become familiar with technologies used for
  machine vision as a sensor for robots.
• Camera and lighting technology (obtaining a
  digital representation of an image)
• Software (computational techniques to process or
  modify the image data)
• Analysis/decisions using the results of the
  processing in robot control
• Additional material in CS766, ECE 533, ME 739

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Machine Vision in Automation

• Use a camera to inspect parts to
• Guide a robot or control automated equipment
• Support statistical analysis in a
  computer-assisted-manufacturing (CAM) system
• Ensure quality in manufacturing process
• dimensions/alignment
• Determine if all components are present
• Other quality issues color, placement,

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Why use Vision?

• Dynamic Range
• Can be remotely situated
• Passive
• emits no energy (cf. Laser, sonar, IR)
• no contact required
• Flexibility
• Affordable

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Why avoid Vision?

• Computation
• must process images
• data information
• Calibration
• Sensitivity to lighting conditions

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Because the lighting is different, these 3 images
appear substantially different to a computer
to a human we easily adapt our perception for
variations in illumination and recognize that all
three images are of the same object.
Images (arrays of pixel data) must be processed
to provide information
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Example ApplicationMicro-manipulation

• Micro Object handling with Micro gripper
• Postech Robotics Lab

Micro gripper
Microscope Table
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A machine vision system often includes the
following elements

• Image Acquisition (generally from a camera placed
  above the production line),
• Image Pre-Processing (e.g. increasing the
  contrast, motion de-blur, etc),
• Feature Extraction (e.g. measuring a distance,
  checking a screw is in place etc),
• Decisions (i.e. is the part OK to a tolerance, is
  a label in the correct position), and,
• Control (e.g. give the result to a Programmable
  Logic Controller (PLC) or robot controller).

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Image Acquisition

• Transforms the visual image of a physical objects
  into a set of digitized data
• Illumination
• Image formation (including focusing)
• Image detection or sensing
• Formatting camera output signal

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Image Formation and Detection
Vision systems have an optical-electro device
that converts electromagnetic radiation from the
image of the physical object into an electric
signal used by the vision processing unit

• Image is formed by
• Illumination flux from object
• Optics (lens)
• Photosensitive detectors (photodiodes on solid
  state cameras)

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Vision Image Formation

• Shape
• Lighting
• Relative Positions
• Sensor sensitivity

Same shape very different images!
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Lighting

• Structured Lighting
• Diffuse Backlighting

• Directional backlighting
• Fiber-optic/LED ring lights

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Lighting

• Polarized lighting
• Oblique lighting

• Direct front lighting
• Cross polarization

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Lighting

• Diffuse front lighting
• Dark field illumination

• Fibre optic near in-lighting

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Image Formation and Detection
Light source
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Digitization of Camera Signal

• Analog image data (voltage) is sampled and
  quantized (often to 8 bits greyscale or 24 bits
  of color)

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Software Processing the Data

• The software allows the image to be processed,
  analyzed, and stored.
• Different types of software packages are
  available, ranging from easy-to-use packages with
  pre-defined tools, to SDKs (software development
  kits) that allow programmers to build custom
  imaging applications.
• Matlab has an image processing tool box
• Image Pre-processing
• Feature Extraction

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Image Pre-processing

• What to do with the image?
• May need to preprocess the image in order to
  analyze it
• Remove motion blur (ECE 533/738)
• Enhance contrast

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I Can See It Why cant the Computer?

• Minimize possible problems The human eye and
  brain are elaborate and versatile systems,
  capable of identifying objects in a wide variety
  of conditions. For example, we are able to
  identify familiar people even when they are
  wearing different clothes, and recognize familiar
  landmarks when driving on a foggy day. A PC-based
  imaging system is not as versatile it can only
  perform what it has been programmed to perform.
  Knowing what the system can and cannot "see" are
  important points to keep in mind to obtain the
  results you want, and reduce errors and incorrect
  measurements. Common variables include
• Changes in objects color
• Changes in surrounding lighting
• Changes in camera focus or position
• Improperly mounted camera
•   Environmental vibration
• A vibration-free environment with all extraneous
  light removed will eliminate many common
  problems.

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Find the man.
Visual tasks can be made difficult!
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Distractors
Natural systems take advantage of the fact that
visual tasks can be made difficult!
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I Can See It Why cant the Computer?

• Minimize possible problems
• Knowing what the system can and cannot "see" are
  important points to keep in mind to obtain the
  results you want, and reduce errors and incorrect
  measurements.

Engineer the environment!
Great examples include commercial motion capture
systems
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Feature Extraction/Analysis

• 2D Geometric Analysis
• Must have high contrast to separate (segment)
  part from background
• In practice back lighting is often used
• The silhouette is used to determine
• part dimensions Width, height, orientation, etc
• Part features (e.g. number of holes)
• Relationships between parts

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Controlled Environment
Easy to segment image
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Measurements from Images

• Must have relationship between the image pixels
  and the world
• 2D imaging
• the image plane and the world plane are in 1-1
  correspondence
• 3D harder

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Goals for ME 439 and ME 739

• Modeling Cameras
• Basic of pinhole
• Kinematics of Vision
• Coordinate transformations
• Processing Images
• Some simple features (sections 8.13 - 8.25)
• 2D problems

• Modeling Cameras
• Pinhole model
• Projective mapping
• Calibration Procedures
• Kinematics of Vision
• Coordinate transformations
• Motion field equations
• Processing Images
• Feature detection (lines, blobs)
• Visual Servoing (Eye-Hand Coordination) in 3D