WiP

1. WiP Wildlife in a palm Kevin Cheng, Hank Chou, Huy Nguyen, Shaolei Zhang

2. Definition: • Radon transform computes projections of an image matrix along specified directions where t = xcosθ + ysinθis the line to the origin

3. Matlab calculation • In Matlab, the Radon transform Rθ{f(x,y) is the line integral of function f(x,y) parallel to the y´-axis

4. Radon perspective of images Radon of 45 degree Viewing the Radon Transform as an Image Radon of 0 degree The Radon transform for dinosaur head is computed at angles from 0° to 180°, in 1° increments

5. Why Radon? • Higher accuracy rate: up to 70 ~ 80% • Speed: 5 times faster than fft • Simplicity: 1-D projection function • Concentrate on the shape of object: take advantage of edges-detection • Invariance of rotation, translation, and scaling movement (working progress)

6. Radon transform properties • Rotation: • Translation: • Scale: →Our GOAL: Make the Radon transform invariant of rotating, translating, scaling movements of the objects.

7. Rotated 30 Original image Rotation invariance Radon transform for 60 degree Take radon transform of both images for 30 degree Radon transform for 30 degree Auto_corrlation = 0.2036 Auto_corrlation = 0.8535

8. Original image I I_trans = circshift(I, [0, 80]); Translation invariance R_sh = circshift(R1, [80, 0]) Auto_corrlation = 0.0730 Auto_corrlation = 1

9. Original image I Scaled image Scaled Radon transform Auto_corrlation = 0.7169 Auto_corrlation = 0.1024 Scale invariance

10. Original image Rotated by 30 degree

11. Original image Translation

12. Original image Scale by half

13. Input Image Gray Scale Highest % = Best Match • Noise Removal: • Median Filtering (medfilt2) • Adaptive Filtering (wiener2) Sort Result Data Base (290) Edge Detection Transform Auto-Correlation

14. Median Filtering: • Output pixel is set to an "average" of the pixel values in the neighborhood of the corresponding input pixel. • The value of an output pixel is determined by the median of the neighborhood pixels rather than the mean. The median is much less sensitive than the mean to extreme values (outliers) • Median filtering is better able to remove these outliers without reducing the sharpness of the image. • Adaptive Filtering: • The adaptive filter tailor itself to the local image variance. • Where the variance is large, the filter performs little smoothing. Where the variance is small, the filter performs more smoothing. • The adaptive filter is selective and preserves edges and other high frequency parts of an image. • There are no design tasks; the filter handles all preliminary computations, and implements the filter for an input image.

15. Noises: A Nightmare for Recognition Original Image and its Edge Detection: Noise due to lack of focus, shakiness, material of the background, etc.

16. Solution: Filter them out Original Image After Median Filter After Adaptive Filter After Edge Detection

17. Example: Less Noise, Better Result Matching % = 0.7062 Matching Images Without Noise Removal

18. Example: Less Noise, Better Result Matching % = 0.7956 Matching Images With Median Filtering

19. Example: Less Noise, Better Result Matching % = 0.7715 Matching Images With Adaptive Filtering

20. Drawbacks: Noise Removal Removing a Little Too Much Matching % = 0.8884 Matching Images Without Noise Removal

21. Drawbacks: Noise Removal Removing a Little Too Much Matching % = 0.7475 Matching Images With Median Filtering

22. Drawbacks: Noise Removal Removing a Little Too Much Matching % = 0.7392 Matching Images With Adaptive Filtering

24. END Wildlife in a palm Kevin Cheng, Hank Chou, Huy Nguyen, Shaolei Zhang