1.
Machine vision
Imaging, digitalization and process

2.
Why machine vision ?
To analyze and process automations
Based on the physical appearance of obj.

3.
Working of video camera:
Charge coupled device (CCD)

4.
Charge coupled device (CCD)

5.
Lighting for the camera
Proper lighting is always needed for a good photo
Types of light generally used for machine vision:

6.
Types of Lighting:
https://www.cognex.com/resources/interactive-tools/lighting-advisor

7.
Analog to digital conversion
Digital
Analog

8.
Process in analog to digital conversion
● Sampling:
● Quantization:
● Encoding:
Signals are sampled periodically to obtain
discrete analog signals
Each voltage levels are assigned to finite number of
defined amplitude levels and these levels are produced
as the grayscale on the system
Finally converted into digital signals

9.
Image processing and analysis
● Image data reduction
This is done to reduce the memory size of the each image produced by the camera.
There are 2 methods to perform data reduction on images:
1. Digital conversion:
It reduces the grayscale levels used for the image.
2. Windowing:
It shows only the required portion of the entire image stored in the frame buffer for image processing and analysis.

10.
What is grayscale ?
It is the black and white pixel with the
corresponding intensity of the original image
colors.

11.
● Segmentation:
Methods to implement segmentation:
1. Thresholding
2. Region growing
3. Edge detection
1.thresholding: it is the binary conversion of image pixel into grayscale (either black or
white) based on frequency histogram of the image and determining the intensity of black and
white

12.
2. Region growing:

13.
3. Edge detection:

14.
Feature extraction
A feature is the parameter obtained from the identified image data. These parameters are used to
compare the feature with the predefined data set or identify based on the application.
Examples:
● Centre of gravity
● Eccentricity
● Thickness
● Diameter
● Area
● Perimeter and etc,.

15.
Object recognition
To identify the object the image represents, we have two algorithm methods to extract the features,
Structural
technique example

16.
Robot applications
Many of the current applications of machine vision are inspection tasks that do not involve the use of an
industrial robot. There are some difficulties encountered by the machine vision system,
● The object can not be controlled in both position and appearance.
● Either position or appearance of the object can be controlled both not both.
● Neither position nor appearance of the object can be controlled.
Robotic applications:
● Inspection
● Identification
● Visual servoing and navigation

17.
Thank you