These are the slides of the RoboCV Workshop organized by roboVITics on August 11th-12th, 2012 in TT311 Smart Classroom, VIT University, Vellore. The workshop was delivered by the following people: 1. Mayank Prasad, President of roboVITics 2. Akash Kashyap, President of TEC - The Electronics Club of VIT 3. Akshat Wahi, Asst. Project Manager of roboVITics

1. Machine Vision
An Introduction
Presented to you by 1
© roboVITics | Mayank Prasad, 2012 8/26/2012

2. • Image & Image Processing
• Image Acquisition, Sampling and Quantization
• Basic Concepts
• Types of Images – Vector & Raster
• Colour Space, Pixels, Resolution, Depth, Channels
• Neighborhood, Connectivity
Outline 2
© roboVITics | Mayank Prasad, 2012 8/26/2012

3. • 2D representation of 3D real world at any instant
• Extract useful information from the image about the real
world – Image Processing
• Two types of images
• Vector Images
• Raster Images
Raster Image – Stores images
in matrix form
Image 3
© roboVITics | Mayank Prasad, 2012 8/26/2012

4. BASIC CONCEPTS
Basic Concepts related to Image Processing
4
© roboVITics | Mayank Prasad, 2012 8/26/2012

5. Digital Image – A multidimensional array of numbers
Aspect Ratio – Width:Height
Resolution – Width×Height
Pixel – Smallest Visual Element
10 10 16 28
Channel – No. of samples per point 65 70 56 43
9 6 9926703756
Single Plane – Grayscale/B&W Images 32 54 96 67 78
15 256013902296
Three Planes – Colour Images 21 54 47 42 67
32 158587853943 92
54 65 65 39
5
Concepts
32 65 87 99
© roboVITics | Mayank Prasad, 2012 8/26/2012

6. • Pixels are tiny little dots that form the image. They are
the smallest visual elements that can be seen.
• When an image is stored, the image file contains the
following information:
• Pixel Location
• Pixel Intensity
• Resolution – total number of pixels in an image
• Greater resolution  Greater detail  Greater processing
power required
Pixels & Resolution 6
© roboVITics | Mayank Prasad, 2012 8/26/2012

7. • An image that is 2048 pixels in width and 1536 pixels in
height has a total of 2048×1536 = 3,145,728 pixels or 3.1
megapixels.
• One could refer to it as 2048-by-1536 or a 3.1-megapixel
image.
A 3.1MP Image 7
© roboVITics | Mayank Prasad, 2012 8/26/2012

8. • Binary (Black & White) Image
• Only two colours – black (0) & white (1)
0 1
• Grayscale Image
• Several shades ranging in between black and white
0 1
0 255
• Colour Image
• Different Colour Spaces
Image Representation 8
© roboVITics | Mayank Prasad, 2012 8/26/2012

9. • RGB Colour Space – Red-Green-Blue
• HSV Colour Space – Hue-Saturation-Value
• Y’CrCb Colour Space
Colour Spaces 9
© roboVITics | Mayank Prasad, 2012 8/26/2012

10. Courtesy aishack.in
RGB 10
© roboVITics | Mayank Prasad, 2012 8/26/2012

11. Courtesy aishack.in
HSV 11
© roboVITics | Mayank Prasad, 2012 8/26/2012

12. RGB HSV
• Advantages • Advantages
• Intuitive • Illumination independent
• Easier to use • Easier image processing
• Widely used • Disadvantages
• Disadvantages • Not so intuitive
• Image processing is tough • Difficult to understand
RGB v/s HSV 12
© roboVITics | Mayank Prasad, 2012 8/26/2012

13. • Y = Luminescence or intensity
• Cr = RED component minus reference value
• Cb = BLUE component minus reference value
• Used in video processing
• Frame grabbers return images from a camera in this
format
Y’CrCb 13
© roboVITics | Mayank Prasad, 2012 8/26/2012

14. • Depth represents the number of shades of a particular
colour used in the formation of an image
• Applies to grayscale as well as colour images
• 1-bit : 21 = 2 shades (black & white)
• 8-bit : 28 = 256 shades
• 24-bit : 224 = 16,777,216 shades
• 64-bit : 264 = 18,446,744,073,709,551,616 shades
8-bit 16-bit
0 1 0 1
0 255 0 65535
Depth 14
© roboVITics | Mayank Prasad, 2012 8/26/2012

15. • Low level task
• Image Acquisition (sensing)
• Preprocessing (noise reduction & enhancement)
• Medium level task
• Segmentation (separating regions)
• Description (characteristic features)
• Recognition (identify regions)
• High level task
• Interpretation (assign meanings)
Image Processing 15
© roboVITics | Mayank Prasad, 2012 8/26/2012

16. • Use Webcams, Video Cameras, Digital Cameras
• Traditionally, Vidicon Camera was used
• Nowadays, CCDs – Charge-Coupled Devices and CMOS
Cameras are used
Image Acquisition 16
© roboVITics | Mayank Prasad, 2012 8/26/2012

17. 17
© roboVITics | Mayank Prasad, 2012 8/26/2012

18. Image Acquisition System
18
© roboVITics | Mayank Prasad, 2012 8/26/2012

19. Sampling & Quantization 19
© roboVITics | Mayank Prasad, 2012 8/26/2012

20. •
D 4 D 8 8 8
4 p 4 8 p 8
Neighborhood D 4 D 8 820 8
© roboVITics | Mayank Prasad, 2012 8/26/2012

21. •
V = (65,66,67,68,69)
q
62 69 69
p
64 67 68
Connectivity 65 70 7221
© roboVITics | Mayank Prasad, 2012 8/26/2012

22. q n
p m
p and q are 8-connected
m and n are m-connected
Connectivity 22
© roboVITics | Mayank Prasad, 2012 8/26/2012

23. References Image Courtesy
• Lectures on Robotics by • Digital Image Processing
Prof. B. Seth, Mech. by Gonzalez and Woods,
Engg, IIT-B (by C-DEEP) Prentice Hall
• Digital Image Processing • Learning OpenCV by
Gary Bradski and Adrian
by Gonzalez and Woods, Kaehler, O’Reilly Media,
Prentice Hall Inc.
• AI Shack – • AI Shack –
www.aishack.in www.aishack.in
Acknowledgements 23
© roboVITics | Mayank Prasad, 2012 8/26/2012

24. UP NEXT: MODULE 2
Introduction to OpenCV and MATLAB
24
© roboVITics | Mayank Prasad, 2012 8/26/2012

25. • Mayank Prasad
President, roboVITics
mayank@robovitics.in
• Akshat Wahi
Asst. Project Manager, roboVITics
+91 909 250 3053
akshat@core.robovitics.in
• Akash Kashyap
President, TEC – The Electronics Club of VIT
akash130791@gmail.com
Contacts 25
© roboVITics | Mayank Prasad, 2012 8/26/2012