Digital Image Processing at Multiple Scales

# Digital Image Processing at Multiple Scales

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## Digital Image Processing at Multiple Scales

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##### Presentation Transcript

1. Humans Brain (Inside) Eyes Conclusion: Ideally Suited for Image Processing

2. computers May Look Ideally Suited for Image Processing… …But They’re Not

3. Filtering Images • Creates new image • Each pixel is based on the corresponding pixel and its neighbors in the old image • Filters can be used to clean images Average Filter: Each pixel in new image will be the average (mean) of a region of pixels in the old image. Median Filter: Each pixel in new image will be the median of a region of pixels in the old image. “Noisy” Picture Cleaned Picture

4. Feature Detection • What are features? • A feature is something that catches our eye in an image

5. Laplacian Filter • Laplacian filter is a filter looking like this: • The Laplacian filter detects points (or areas) that are different from their surrounding. • Us humans see the world • Through Laplacian filter

6. Feature detection in action narrow filter small features wide filter large features

7. The Problem of Scale • The computer can easily fill in small gaps in the image to clean up noise. • There are problems with larger gaps. • Solution: Work on different scales. Filter Picture With Larger Bad Piece Just Filtering is Not Effective!

8. Gaussian Pyramids • G0 = Original Image • GN, N > 0 = Reduced Image Expand Much Higher Detail Low Detail Expand G0 G1 G2 …

9. Using Filters As Pyramids • Filters can accomplish the same blurring as Gaussian pyramids. • Gaussian filters create this blurring effect by emphasizing the corresponding pixel’s neighbors more than the corresponding pixel Apply Small, Weak Gaussian Filter Apply Large, Strong Gaussian Filter Much Higher Detail Lower Detail

10. Approximations • G0s of similar images = quite different • GNs of similar images are closer than G0s Find GNs with Large N Very Slightly Similar Slightly More Similar

11. Image Completion • Method for Image Completion • Repeat with N from a large number to 0 • Obtain a filtered version of GN, enlarged to the original size (Using filters or a Gaussian pyramid) • Reintroduce the good pixels from the incomplete image Incomplete Image Complete Image Mask (Marks Valid Pixels)

12. Another Example • Can you see the Einstein in 100 random lines? Incomplete Image Complete Image Mask (Marks Valid Pixels)

13. Limitations • This method does not work as well on drawings because drawings can have more unpredictable changes in color. Incomplete Image Complete Image Mask (Marks Valid Pixels)

14. Resizing Images • Our task was making images smaller. • Why? • One reason is to transmit the image over the internet faster.

15. But how do you resize an image? • There a few methods to resize images and to reduce their number of pixels: • The simplest reduce method is to use the ‘uniform grid’

16. Adaptive Sub-sampling • To keep more pixels where details are finer • Using Feature Detection to sample (take) more pixels near features • Non-uniform grid

17. Thank you! No questions please