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Object detection

Object detection. Presented by Minh Hoai Nguyen Date: 28 March 2007. Object detection?. What we want. Miss a face!. Happy face!. Scanning window. Train a classifier on a fixed size window. Outline. Object Detection Using the Statistics of Parts

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Object detection

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  1. Object detection Presented by Minh Hoai Nguyen Date: 28 March 2007

  2. Object detection?

  3. What we want Miss a face!

  4. Happy face!

  5. Scanning window Train a classifier on a fixed size window

  6. Outline • Object Detection Using the Statistics of Parts • Schneiderman, H. & Kanade, T. CVPR00, IJCCV04 • Robust Real-time Face Detection • Viola, P. & Jones, M. CVPR01, IJCV04

  7. There are too many parameters to learn Bayes optimal classifier • Image is defined by n attrs: x1,x2,…,xn

  8. Easier to learn • Idea: • Carefully divide x1,x2,…,xn into groups: P1, P2,…, Pk • Assume P1, P2,…, Pk are independent Naïve Bayes Assumption • Assume: x1,x2,…,xn are cond. independent. • Problem: this might be a bad assumption

  9. Independent groups/parts • How to divide x1,x2,…,xn into ind. groups? • Image pixels are highly correlated. • Represent image by Wavelets instead.

  10. HL HH Wavelet transform 10 filter responses for each original pixel. LH • Wavelet transform is fully invertible. • Partially de-correlate natural imagery • More independence, easier to design parts

  11. Designing parts • Assumption: • Each wavelet coefficient only depends on few others. • Group those coefficients into parts. • Parts: • 17 types, manually defined. • Each part contains 8 coefficients.

  12. Categories of parts Intra-subband Local operator Inter-frequency Local operator “Parts” Inter-orientation Local operator Inter-frequency/ Inter-orientation Local operator Slide credit: Nicholas Chan

  13. Final form of detector How to compute these statistics? Count!

  14. Multiple poses? • Other tricks: • Not going to talk about.

  15. Reported results for faces • Kodak dataset: • Test set: 17 images, 46 faces, 36 profile views.

  16. A bigger dataset • From multiple sources 208 images, 441 faces, about 347 profiles.

  17. Robust Real-time Face Detectionby Viola,P. & Jones, M.

  18. Cascade of classifiers • Most places do not have faces!

  19. Feature evaluation can be done by few lookups Integral image Simple features Box filters Approximation of Harr-wavelets

  20. Learning the cascade • AdaBoost • Weak classifiers are box filters

  21. Learning cascade stages • Using AdaBoost to train each stage: • Adjust threshold to minimize false negatives. • Adding features until target detection and false positive rates are met (determined by CV)

  22. First classifier: • 2 features • 100% detection • 40% false detection Learned cascade • The whole cascade: • 38 stages • 6000 features in total • On dataset with 507 faces and 75 millions sub-windows, faces are detected using 10 feature evaluations on average. • On average, 10 feature evals/sub-window

  23. Reported ROC curve

  24. Comparison results

  25. The end

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