Title: Introduction to Machine Vision Systems
1Introduction toMachine Vision Systems
Professor Nicola Ferrier Room 3128,
ECB 265-8793 ferrier_at_engr.wisc.edu
2Machine 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
3Machine 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,
4Why use Vision?
- Dynamic Range
- Can be remotely situated
- Passive
- emits no energy (cf. Laser, sonar, IR)
- no contact required
- Flexibility
- Affordable
5Why avoid Vision?
- Computation
- must process images
- data information
- Calibration
- Sensitivity to lighting conditions
/
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
6Example ApplicationMicro-manipulation
- Micro Object handling with Micro gripper
- Postech Robotics Lab
Micro gripper
Microscope Table
7A 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).
8Image 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
9Image 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)
10Vision Image Formation
- Shape
- Lighting
- Relative Positions
- Sensor sensitivity
Same shape very different images!
11Lighting
- Structured Lighting
- Diffuse Backlighting
- Directional backlighting
- Fiber-optic/LED ring lights
12Lighting
- Polarized lighting
- Oblique lighting
- Direct front lighting
- Cross polarization
13Lighting
- Diffuse front lighting
- Dark field illumination
- Fibre optic near in-lighting
14Image Formation and Detection
Light source
15Digitization of Camera Signal
- Analog image data (voltage) is sampled and
quantized (often to 8 bits greyscale or 24 bits
of color)
16Software 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
17Image 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
18I 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.
19Find the man.
Visual tasks can be made difficult!
20Distractors
Natural systems take advantage of the fact that
visual tasks can be made difficult!
21I 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
22Feature 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
23Controlled Environment
Easy to segment image
24Measurements 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
25Goals 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