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Outline

Outline. How Can We Use Digital Images? A Digital Image is a Matrix Manipulating Images Improving Images for Robot Eyes. 4.1 How Can We Use Digital Images?. The Power of Digital Images. It is easier and easier to acquire or make digital images Digital cameras and scanners

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Outline

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  1. Outline • How Can We Use Digital Images? • A Digital Image is a Matrix • Manipulating Images • Improving Images for Robot Eyes

  2. 4.1 How Can We Use Digital Images?

  3. The Power of Digital Images • It is easier and easier to acquire or make digital images • Digital cameras and scanners • Chief advantage over analog (e.g. film) images: Easy to change • How might we change images?

  4. Image Softening and Blurring

  5. Image Sharpening

  6. Finding Edges in Images • Edges in images usually define solid objects in our 3D world • Finding edges is a first step to navigation and manipulation

  7. Subtracting Images • Image subtraction is a simple way to find differencesbetween two images • Useful for security, recognition, and navigation applications

  8. Image Segmentation • Segmentation: Dividing images into regions • Applications • Identifying foreground vs. background • Finding objects using only one image

  9. 4.2 A Digital Image Is a Matrix

  10. 14 12 5 7 0 1 6 5 4 3 11 10 Digital Images are Matrices • Matrix: An array of numbers A = 3 rows and 4 columns • Elements: A(i,j) , i and j are integers Examples: A(2,3) = 6, A(1,4) = 7, A(3,4) = 10

  11. Classes of Image Manipulation • Mapping: Pixel-by-pixel operations • Arithmetic Combination of Two Images: e.g. addition, multiplication • Filtering: Operations on groups of pixels in an image (neighborhood operation) Fact: Almost all applications are made up of simple combinations of these manipulations

  12. Image Mapping • All of the form B(i,j) = {function of A(i,j)} where B(i,j) is the output image • Many familiar effects: • Brightness, Contrast, Negative, Thresholding, Gamma Correction

  13. k = 50 k = -50 Brightness Mapping B(i,j) = A(i,j) + k k = constant (expressed as a table)

  14. Example: Brightness Mapping Each color increased by 60 Original 24-bit image Each color decreased by 60 Green and blue +60, red -60

  15. Contrast Mapping • Formula: B(i,j) = s • A(i,j) • If s > 1, contrast is increased • If s < 1, contrast is decreased • Need to quantize after multiplying, as s • A(i,j) may not be an integer • Aside: This setting is found on most TV sets

  16. s > 1 s < 1 s > 1 and k < 0 s < 1 and k > 0 Contrast Mapping (cont.)

  17. Example: Contrast and Brightness Higher contrast with lower brightness Higher contrast • Formula: B(i,j) = s • A(i,j) + k • This is easy to do!

  18. 14 12 5 7 0 1 6 5 4 3 11 10 Example: Contrast and Brightness • Given the 4-bit grayscale image A = • Compute the image with a contrast of 1.5 and a brightness of 2

  19. 15 15 10 13 2 4 11 10 8 7 15 15 Solution: Contrast and Brightness • Answer: B(i,j) = 1.5•A(i,j) + 2 B = Quantization causes values such as 9.5 and 3.5 to be rounded up Image is clipped at 15 (Why?)

  20. Infinity Project Experiment – 4.1

  21. Brightness and Contrast Grayscale

  22. Brightness and Contrast Color Image

  23. Brightness and Contrast Color Camera

  24. Brightness and Contrast Cascade

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