Computer Vision

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Computer Vision Image processing

• John Hulskamp
• E-mail john_at_hulskamp.com.au
• Consultant

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Introduction

• Introducing the new subject 31049 Computer Vision
  Image Processing
• Guest Lecturer with previous experience in
  teaching research in the area

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Osteons

• Compact bone is comprised of many units called
  osteons they consist of a central canal
  surrounded by closely packed concentric layers
  called lamallae.
• This is an image of some osteons, specimen is
  from a 32yo male. Image is a 2.8x3.5mm portion
  of a contact micro-radiograph taken from a
  100micrometre thick, un-embedded, hard-ground
  section. Black areas are voids, white high
  mineral density (maybe a couple of grains of
  carborundum can be seen).
• Attribution David Thomas, Dental Sciences,
  University of Melbourne

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Near IR Imaging

• Hot strip mill temperature measurement

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Near IR Imaging (2)

• Thermal Map
• Attribution C. Lampe A Multi-Processor
  temperature profiling system for real time
  control in a hot strip rolling mill M.Eng.
  Thesis RMIT 1995

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Topics for Discussion Today

• Subject objectives
• Fundamental Issues
• Applications
• Towards image understanding
• CV IP Tools
• A Simple Beginning
• Conclusions

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Subject objectives

• adequate background knowledge about computer
  vision and image processing
• practical knowledge and skills about computer
  vision and image processing tools
• necessary knowledge to design and implement a
  prototype of a computer vision application

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Fundamental Issues

• An imaging system

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Fundamental Issues (2)

• Resolution - the smallest feature size on your
  object that the imaging system can distinguish
• Field of view - the area of inspection that the
  camera can acquire
• Working distance - the distance from the front of
  the camera lens to the object under inspection
• Sensor size - the size of a sensor's active area
• Depth of field - the maximum object depth that
  remains in focus

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Fundamental Issues (3)

• Resolution
• Resolution indicates the amount of object detail
  that the imaging system can reproduce. You can
  determine the required resolution of your imaging
  system by measuring in real-world units the size
  of the smallest feature you need to detect in the
  image.
• To make accurate measurements, a minimum of two
  pixels should represent the smallest feature you
  want to detect in the digitized image. In the
  picture , the narrowest vertical bar (w) should
  be at least two pixels wide in the image. This
  information can assist to select the appropriate
  camera and lens for the imaging application.

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Fundamental Issues (4)

• Sensor resolution is the number of columns and
  rows of CCD pixels in the camera sensor. To
  compute the sensor resolution, you need to know
  the field of view (FOV).
• The FOV is the area under inspection that the
  camera can acquire. The horizontal and vertical
  dimensions of the inspection area determine the
  FOV. Make sure the FOV encloses the object you
  want to inspect.Once you know the FOV, you can
  use the following equation to determine your
  required sensor resolutionsensor resolution
  (FOV/resolution) x 2 (FOV/size of smallest
  feature) x 2

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Fundamental Issues (5)

• Determine the focal length of your lens.A lens
  is primarily defined by its focal length. This
  picture illustrates the relationship between the
  focal length of the lens, field of view, sensor
  size, and working distance which is the distance
  from the front of the lens to the object under
  inspection.focal length sensor size x working
  distance / FOV

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Fundamental Issues (6)

• Lighting issues
• One of the most important aspects of setting up
  your imaging environment is proper illumination.
  Images acquired under proper lighting conditions
  make your image processing software development
  easier and overall processing time faster. One
  objective of lighting is to separate the feature
  or part you want to inspect from the surrounding
  background by as many gray levels as possible.
  Another goal is to control the light in the
  scene. Set up your lighting devices so that
  changes in ambient illumination-such as sunlight
  changing with the weather or time of day-do not
  compromise image analysis and processing.

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Fundamental Issues (7)

• Backlighting is another lighting technique that
  can help improve the performance of your vision
  system. If you can solve your application by
  looking at only the shape of the object, you may
  want to create a silhouette of the object by
  placing the light source behind the object you
  are imaging. By lighting the object from behind,
  you create sharp contrasts which make finding
  edges and measuring distances fast and easy. This
  picture shows a stamped metal part acquired in a
  setup using backlighting.

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Fundamental Issues (8)

• Perspective
• Perspective errors occur when the camera axis is
  not perpendicular to the object under inspection.
  The figures show both an ideal camera position
  (I.e. vertical) and a camera imaging an object
  from an angle.

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Fundamental Issues (9)

• Perspective and Distortion Errors
• Try to position your camera perpendicular to the
  object under inspection to reduce perspective
  errors. Integration constraints may prevent you
  from mounting the camera perpendicular to the
  scene. Under these constraints, you can still
  take precise measurements by correcting the
  perspective errors with spatial calibration
  techniques.

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Examples of Vision Applications

• Attribution National Instruments IMAQ Vision
  Product Demonstration (www.ni.com/vision)
• Examples
• Battery Clamp
• Spark Plug Gap Measurement
• Blister Pack Inspection
• PCB Inspection

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Examples of Vision Applications (2)

• Battery Clamp

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Examples of Vision Applications (3)

• Spark Plug Gap Measurement

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Examples of Vision Applications (4)

• Blister Pack Inspection Ensuring that blister
  packs contain the correct number and type of
  pills before they reach pharmacies, ensuring the
  integrity of the product and increase the yield
  of production by automating the inspection of
  blister pack contents.
• Acquire colour images of the blister packs. Use
  colour location to count the number of green
  areas in the image. With colour location, you
  create a model or template that represents the
  colours that you are searching. Then the machine
  vision application searches for the model in each
  acquired image and calculates a score for each
  match. The surface area of each pill in the pack
  must be at least 50 green to pass inspection.

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Examples of Vision Applications (5)

• Blister Pack Inspection (contd)

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Examples of Vision Applications (6)

• PCB Inspection
• To ensure that components are present and at the
  correct orientation on a PCB.

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Examples of Vision Applications (7)

• PCB Inspection (contd) Colour information
  simplifies a monochrome problem by improving
  contrast or separation of the components from the
  background. Colour pattern matching can
  distinguish objects from the background more
  efficiently than grayscale pattern matching.
• This example uses rotation-invariant pattern
  matching because it can detect the components
  regardless of their orientations. You can use the
  orientation information to determine the correct
  placement of orientation-sensitive components,
  such as capacitors or diodes.

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Towards image understanding

• Computer Vision making useful decisions about
  real physical objects and scenes based on sensed
  images
• To get to that understanding we need to process
  images hence Image Processing is a step towards
  this understanding
• Image Processing issues
• Image Statistics Histograms
• Image Enhancement
• Image Restoration
• Image Analysis Edge Detection Feature
  extraction
• Representation Description
• Pattern Recognition

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Histograms

• The cumulative histogram of a grey-level image
  f(w,h) is a function H(k) which provides the
  total number pixels (number of occurrences) that
  have grey-level less than the value k.
• Where card stands for the cardinality (i.e.
  number of pixels) of a set.
• The histogram of a grey-level image f(w,h) is a
  table h(k) which is the discrete difference of
  the cumulative histogram. It provides the total
  number pixels (number of occurrences) that have a
  specific grey-level value k.
• Attribution www.khoral.com

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Histograms (2)
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Histogram Equalisation
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CV IP Tools

• NIH Image is a public domain image processing and
  analysis program for the Macintosh. It was
  developed at the Research Services Branch (RSB)
  of the National Institute of Mental Health
  (NIMH), part of the National Institutes of Health
  (NIH). A free PC version of Image, called Scion
  Image for Windows, is available from Scion
  Corporation. Image can acquire, display, edit,
  enhance, analyse and animate images. It reads and
  writes TIFF, PICT, PICS and MacPaint files,
  providing compatibility with many other
  applications, including programs for scanning,
  processing, editing, publishing and analysing
  images. It supports many standard image
  processing functions, including contrast
  enhancement, density profiling, smoothing,
  sharpening, edge detection, median filtering, and
  spatial convolution with user defined kernels.
• Image can be used to measure area, mean,
  centroid, perimeter, etc. of user defined regions
  of interest. It also performs automated particle
  analysis and provides tools for measuring path
  lengths and angles. Spatial calibration is
  supported to provide real world area and length
  measurements. Density calibration can be done
  against radiation or optical density standards
  using user specified units. Results can be
  printed, exported to text files, or copied to the
  Clipboard.

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CV IP Tools (2)

• Khoral IncIt started out as Khoros about 10
  years ago, for UNIX-based X-systems, has become
  available for the Windows platform as well. Costs
  money today!
• Their online DIP course is very good
  http//www.khoral.com/contrib/contrib/dip2001/inde
  x.html

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CV IP Tools (3)

• UTHSCSA ImageTool
• UTHSCSA ImageTool (IT) is a free image processing
  and analysis program for Microsoft Windows 9x,
  Windows ME or Windows NT. IT can acquire,
  display, edit, analyse, process, compress, save
  and print gray scale and colour images.IT can
  read and write over 22 common file formats
  including BMP, PCX, TIF, GIF and JPEG. Image
  analysis functions include dimensional (distance,
  angle, perimeter, area) and gray scale
  measurements (point, line and area histogram with
  statistics). ImageTool supports standard image
  processing functions such as contrast
  manipulation, sharpening, smoothing, edge
  detection, median filtering and spatial
  convolutions with user-defined convolution masks.
  ImageTool was designed with an open architecture
  that provides extensibility via a variety of
  plug-ins. Support for image acquisition using
  either Adobe Photoshop plug-ins or Twain scanners
  is built-in. Custom analysis and processing
  plug-ins can be developed using the software
  development kit (SDK) provided (with source
  code). This approach makes it possible to solve
  almost any data acquisition or analysis problem
  with IT.

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CV IP Tools (4)

• Intel open source computer vision library OpenCV
• CVIPtools is a UNIX/Win32-based software package
  developed \ under the continuing direction of Dr.
  Scott E Umbaugh One of the primary purposes of
  the CVIPtools development is to allow students,
  faculty, and other researchers to explore the
  power of computer processing of digital images.
• CVIPtools is a collection of computer imaging
  tools providing services to the users at three
  layers. At the bottom level are the CVIPtools
  libraries (the application programming
  interface). Based on the CVIPtools libraries are
  the cviptcl and cvipwish shells. The cviptcl
  shell is an extension of Tcl with additional CVIP
  capabilities. With cviptcl, the user can either
  use the command line for interactive image
  processing, or write cviptcl shell scripts for
  batch processing. The cvipwish shell is the
  extension of cviptcl with the added functionality
  for building a graphical user interface (GUI)
  which allows even the casual computer users to
  experiment with many of the sophisticated tools
  available to computer imaging specialists without
  the need for any knowledge of computer
  programming.

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Topics in Course

• Image formation. Human vision. Computer vision.
• Image representation. Analog and digital images.
  Common image and video formats. Image
  compression. JPEG and MPEG.
• Image acquisition. Image acquisition systems.
  Cameras and frame grabbers.
• Image processing. Image enhancement. Convolution.
  Filtering. Edge detection. Texture analysis.
  Labelling. Contour tracing. Image morphology.
  Image segmentation.
• Image analysis. Feature extraction. Geometrical
  features. Hough transform.
• Image sequences. Motion detection. Optical flow.
  Background subtraction. Feature tracking.
• Pattern recognition. Object classification.
  Statistical, neural networks, symbolic
  classifiers.
• Computer Vision. Model-based vision. Applications
  in industrial quality inspection, video
  surveillance, robotics, medicine, multimedia .

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A Simple Beginning

• Paradigm for an image processing code
• Declarations
• File In Bring in image into Array Ir,c
• Processing on Array Ir,c to produce
• Array Or,c
• File Out Output Array Or,c as image

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Conclusions

• An overview of what is ahead for you in this
  exciting field.
• You might like to experiment with the above
  simple code to attempt to gain the histogram for
  an image, and try out histogram equalisation