Modified Census Transform

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Modified Census Transform

  1. 1. Face Detection with the Modified Census Transform Bernhard Froba Andreas Ernst presented by Hyoungjin Kim
  2. 2. existing works • histogram equalization • unit variance • zero mean • local binary pattern • linear SNoW classifier
  3. 3. existing works • histogram equalization • unit variance(= unit standard deviation) - divide all pixels by the standard deviation • zero mean • local binary pattern • linear SNoW classifier
  4. 4. existing works • histogram equalization • unit variance • zero mean(subtract mean from all pixels) • local binary pattern • linear SNoW classifier
  5. 5. existing works • histogram equalization • unit variance • zero mean • local binary pattern • linear SNoW classifier
  6. 6. existing works • histogram equalization • unit variance • zero mean • local binary pattern • linear SNoW classifier
  7. 7. existing works • histogram equalization • unit variance • zero mean • local binary pattern • linear SNoW classifier
  8. 8. problem domain • Illumination variance is a big problem in object recognition which usually requires a costly compensation to classification • Ordering information is robust to outliers and invariant to monotonic intensity distortions Ramin Zabih and John Woodfill
  9. 9. census transform Ramin Zabih and John Woodfill usually transform computes some summary of local intensities.
  10. 10. census transform Ramin Zabih and John Woodfill a summary of local spatial structure
  11. 11. census transform Ramin Zabih and John Woodfill from John Woodfill and Brian Von Herzen
  12. 12. The modified census transform
  13. 13. Local structure patterns • num of kernel index 2^9-1 = 511 • used 3X3 neighborhood
  14. 14. How to generate weak classifier • The single feature weak classifier at position x with the lowest boosting error e and with regard to maximum number of feature positions allowed is chosen in boosting loop. •
  15. 15. How to generate weak classifier
  16. 16. Strong classifier
  17. 17. training of stage classifier AdaBoost Winnow update • first stage: 20 lookup-table operations have to be accumulated(Low complexity!)
  18. 18. Training the last stage background face The two sets of weight-tables {hx-face} and {hx- background} are trained using an iterative procedure param for Winnov update threshold : T promotion : A demotion : B

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