Machine Vision Introduction
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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
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Machine Vision Introduction
- 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