This document discusses machine vision systems and their components and applications. It describes the basic process of image acquisition, digitization, processing, analysis and interpretation. It outlines the main types of vision systems and cameras used. It also discusses different lighting techniques and image processing methods like segmentation, feature extraction and pattern recognition. Finally, it notes that machine vision is widely used for industrial inspection to automate tasks and improve efficiency.

1. Acquisition of image data, followed by the
processing and interpretation of these data by
computer for some useful application like
inspection, counting etc.

2.  2D system
◦ Most commonly using system.
◦ For measuring dimensions of parts.
 Verifying presence of components.
 Checking features of Flat or semi flat surfaces.
 3D system
◦ Only for special purpose
 Application include 3D analysis of scenes.

3.  Image acquisition and digitization
 Image processing and analysis
 Interpretation

4.  What the hell is this?
◦ It is nothing but capture the images or video using
a video camera (image acquisition is over now) then
digitize the image using an ADC( Analog to digital
converter) and store the image data for subsequent
analysis.
Take ok….
Camera ready….
Action….

5.  Of course there is a camera for capturing
video
 Light sources for providing light
 Analog to digital converter (ADC)

6. There are mainly two types of vision system
they are:-
 Binary System
 Gray scale system

7.  Vidicon Cameras
 Solid-State Cameras

8. The scene captured by the vision camera
must be well illuminated and the illum
ination must be constant over time
 There are mainly five categories of lighting
systems.
◦ Front lighting
◦ Back lighting
◦ Side lighting
◦ Structured lighting
◦ Strobe lighting.

9.  Front lighting.
◦ Light source is located at the same side of the
camera.
◦ Produces a reflected light from the object that allow
inspection of surface features.

10.  Back lighting.
◦ Light source is placed between behind the object
being viewed by the camera.
◦ This create dark silhouette of the object that
contrasts sharply with the light background.
◦ This type is used for inspect parts dimension and
distinguish between part outlines.
Silhouette
Back Lighting

11.  Side lighting
◦ Light source is placed at the side of the surface to
be illuminated.
◦ For finding out surface irregularities, flaws, defects
on the surface.

12.  Structured lighting
◦ Projection of special light pattern onto the object.
◦ Usually planer sheet of highly focused light are
used.
The above elevation differences are calculated by
trigonometric relation

13.  Strobe Lighting.
◦ The scene is illuminated by short pulse of high
intensity light which causes moving object appear
to be stationary.
◦ This is dangerous causing migraine, fizz to the
operator… 

14. Different techniques for image processing and
analysis the image data in machine vision
system.
 Segmentation( consist of two different
technique)
◦ Thresholding
◦ Edge detection
 Feature extraction

15.  Segmentation:- Indented to define separate
region of interest within the image.
◦ The two common segmentation techniques.
 Thresholding
 Conversion of each pixel intensity level into a binary value,
representing black or white.
 There is a threshold value of intensity
 If the value of the pixel of the image is less than the
threshold value then the pixel value is Zero(Black)
otherwise One( White).
Monalisa after thresholding

16. Edge detection
 Determines the location of boundaries between an
object and its surroundings in an image.
 This is accomplished by identifying the contrast in
light intensity that exists between adjecent pixels at
the border of the objects.
Monolisa after edge detection

17.  Feature extraction.
◦ Used for extracting features like area, length, width,
diameter, perimeter from the image.
The area of the leaf can be calculated by counting the
number of squares in it. 

18.  Pattern recognition.
 Two common pattern recognition technique
are:-
◦ Template matching
◦ Feature weighting.

19.  Pattern recognition
◦ Recognizing the object
◦ Comparing the image with predefined models or
standard values.
◦ Template matching:-
 Compare one or more feature of an image with the
corresponding feature of model or template stored in
computer memory.
 Image is compared pixel by pixel.
 Disadvantage : very difficult to aligning the part in the
same position and orientation in front of the camera,
to allow the comparison to be made with out
complication in the image processing.

20. ◦ Feature Weighting.
 Several features like area, length and perimeter are
combined into a single measure by assigning a weight
to each feature according to the relative importance in
the identifying the object.
 The score of the object in the image is compared with
the score of the image in the computer memory to
achieve proper identification.

21.  Inspection
 Identification
 Visual guidance and control

22.  Machine vision in inspection
◦ 80% of inspection works in industries are done by
machine vision
◦ Save lot’s of time
 Dimensional measurement
 Dimensional gaging.
 Verification of the presence of components.
 Verification of hole location and number of holes.
 Detection of surface flaws and defects.
 Detection of flaws in a printed label.

23.  Automation, Production system and computer
integrated manufacturing by Mikell P Groover.