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Filters

Filters. Plan of presentation. Review of available filters Filter application in various parts of automatic face recognition system Further research. Filter grouping. One pixel operations Pixel area operations Image histogram operations Image rotation & scaling Complex techniques.

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Filters

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  1. Filters Face Recognition and Biometric Systems 2005/2006

  2. Plan of presentation • Review of available filters • Filter application in various parts of automatic face recognition system • Further research Face Recognition and Biometric Systems 2005/2006

  3. Filter grouping • One pixel operations • Pixel area operations • Image histogram operations • Image rotation & scaling • Complex techniques Face Recognition and Biometric Systems 2005/2006

  4. One pixel operations • Linear function • Power function • Logarithmic function • Application • Contrast improvement • Image sharpness enhancement Face Recognition and Biometric Systems 2005/2006

  5. Linear function • Scaling • Dynamic range scaling in a chosen sections Face Recognition and Biometric Systems 2005/2006

  6. Power function • Gamma correction • Image after translation still looks naturally Face Recognition and Biometric Systems 2005/2006

  7. Logarithmic function • Gray level compression • Natural image look • Partial lost of image information Face Recognition and Biometric Systems 2005/2006

  8. Input image Logarithm Scaling Gamma One pixel filters - example Face Recognition and Biometric Systems 2005/2006

  9. Input image Logarithm Scaling Gamma One pixel filters - example Face Recognition and Biometric Systems 2005/2006

  10. Input image Logarithm Scaling Gamma One pixel filters - example Face Recognition and Biometric Systems 2005/2006

  11. One pixel filters • Advantages: • Improvement of image contrast • Better sharpness • Disadvantages: • Too bright pixels • Difficulties with optimal parameters selection Face Recognition and Biometric Systems 2005/2006

  12. Area filters • Lowpass filters • Mean filter • Gauss • Median • Highpass filters • Roberts • Prewitt • Sobel • Laplacian Face Recognition and Biometric Systems 2005/2006

  13. Lowpass filters • Noise reduction • Image smoothing • Contour blurring Face Recognition and Biometric Systems 2005/2006

  14. Mean filter • Linear filter • Light image smoothing Face Recognition and Biometric Systems 2005/2006

  15. Gauss filter • Filter uses power function • Stronger image smoothing in a shorter time Face Recognition and Biometric Systems 2005/2006

  16. Median filter • Nonlinear filter • Good for noise removal from image without important information elimination Face Recognition and Biometric Systems 2005/2006

  17. Input image Gauss Mean Median Lowpass filters - example Face Recognition and Biometric Systems 2005/2006

  18. Highpass filters • Image sharpness enhancement • Contour detection • In case of noisy images the errors will multiply Face Recognition and Biometric Systems 2005/2006

  19. Roberts filter • Gradient method Face Recognition and Biometric Systems 2005/2006

  20. Prewitt filter • Gradient method Face Recognition and Biometric Systems 2005/2006

  21. Sobel filter • Gradient method Face Recognition and Biometric Systems 2005/2006

  22. Laplacian filter • Method uses second derivative properties Face Recognition and Biometric Systems 2005/2006

  23. Input image Prewitt Roberts Sobel Highpass filters - example Face Recognition and Biometric Systems 2005/2006

  24. Histogram operations • Stretching • Fitting • Equalization Face Recognition and Biometric Systems 2005/2006

  25. Histogram stretching • Image dynamic range enlargement for image contrast & sharpness enhancement • Does not work on images with characteristic histogram Face Recognition and Biometric Systems 2005/2006

  26. Histogram equalization • Equal distribution of gray scale levels in input image • Contrast enhancement Face Recognition and Biometric Systems 2005/2006

  27. Histogram equalization • Algorithm: Face Recognition and Biometric Systems 2005/2006

  28. Histogram fitting • Its aim is a transformation of an input histogram so it looks like the given one • Image lighting unification Face Recognition and Biometric Systems 2005/2006

  29. Histogram fitting • Algorithm: • Input & output image histogram calculation (hIn ,hOut ) • Histogram normalization • Increment function calculation Face Recognition and Biometric Systems 2005/2006

  30. Histogram fitting • Algorithm: Face Recognition and Biometric Systems 2005/2006

  31. Input image Equalization Stretching Fitting Histogram - example Face Recognition and Biometric Systems 2005/2006

  32. Histogram • Minimization of lighting differences in images from different sources • Image sharpness and contrast enhancement Face Recognition and Biometric Systems 2005/2006

  33. Image Rotation / Scaling Face Recognition and Biometric Systems 2005/2006

  34. Complex filters - techniques • Kuwahara • Canny • Unsharp Masking • LogAbout • GammaAbout Face Recognition and Biometric Systems 2005/2006

  35. Kuwahra filter • Nonlinear filters • Good image smoothing • Low contours blurring • Algorithm: • For each region: • Result: Face Recognition and Biometric Systems 2005/2006

  36. Canny filter • Optimal contour detection • Algorithm: • Gauss filter • Sobel filter • Borders direction described as • Direction definition • Pixel tracking in the direction of borders and removal of unnecessary pixels • Thresholding Face Recognition and Biometric Systems 2005/2006

  37. Unsharp Masking • Image sharpening • Minor noise elimination • Algorithm: • I(x,y) = Gauss(Iin(x,y)) • Ihp(x,y) = Iin(x,y) – I(x,y) • Ihp(x,y) = 0 dla Ihp(x,y) < threshold • Iout(x,y) = Iin(x,y) + a*Ihp(x,y) Face Recognition and Biometric Systems 2005/2006

  38. LogAbout method • Contour detection improvement Highpass filter Logarithmic filter Face Recognition and Biometric Systems 2005/2006

  39. Histogram stretching Gauss LogAbout HistAbout method • Contour detection enhancement Face Recognition and Biometric Systems 2005/2006

  40. Gamma Gauss LogAbout GammaAbout method • Contour detection improvement Face Recognition and Biometric Systems 2005/2006

  41. Where use filers? • Input image • Detection • Normalization Face Recognition and Biometric Systems 2005/2006

  42. Input image • Problems: • Noises • Solution: • Gauss filter • Median filter Face Recognition and Biometric Systems 2005/2006

  43. Input image/Detection • Problem: • Dark image • Solution: • Histogram stretching • Gamma correction • GammaAbout Face Recognition and Biometric Systems 2005/2006

  44. Detection • Problem: • Contour detection • Solution: • Roberts filter • Prewitt filter • Sobel filter • Canny’s method Face Recognition and Biometric Systems 2005/2006

  45. Shape normalization • Problem: • Lack of size unification • Solution: • Scaling • Problem: • Non frontal face • Solution: • Rotation Face Recognition and Biometric Systems 2005/2006

  46. Lighting normalization • Problem: • Irregular face lightning • Solution: • Histogram operations Face Recognition and Biometric Systems 2005/2006

  47. Filter usage • Image quality enhancement • Object detection method efficiency improvement • Image normalization • Lighting normalization Face Recognition and Biometric Systems 2005/2006

  48. What further?? • Lighting normalization is still an area for research • Dark image brightening Face Recognition and Biometric Systems 2005/2006

  49. Thank You Face Recognition and Biometric Systems 2005/2006

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