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Active Appearance Models : AAM

Active Appearance Models : AAM. CPE488 && CPE631 [ 2012 – KMUTT ]. Presented by Miss Chayanut Petpairote. Outline. What is AAM? How AAM work? AAM Modeling AAM Fitting Experimental Results Conclusion Demo. What is AAM? (1). Active Appearance Models

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Active Appearance Models : AAM

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  1. Active Appearance Models : AAM CPE488 && CPE631 [ 2012 – KMUTT ] Presented by Miss ChayanutPetpairote

  2. Outline • What is AAM? • How AAM work? • AAM Modeling • AAM Fitting • Experimental Results • Conclusion • Demo

  3. What is AAM? (1) • Active Appearance Models • Statistical model of object shape and grey-level appearance. • Matching to an image involves finding model parameters which minimize the difference between the image and a synthesised model.

  4. How AAM work? • AAM algorithm has 2 procedures • Modeling • Fitting

  5. AAM Modeling (1) • Two-part modeling is involved for shape and texture • Shape Model: • x bar = mean shape • Ps = data matrix to describe shape variation • bs = set of shape parameters

  6. AAM Modeling (2) • Texture Model: • g bar = mean texture based on shape-free patch • Pg = data matrix to describe texture variation • bg = set of texture parameters • Shape and Texture Model: • x bar = mean shape • g bar = mean texture based on shape-free patch • c = vector of appearance parameters controlling both the shape and the texture • Qs , Qg = matrices describing the modes of variation derived from the training set when value of c to change

  7. AAM Fitting • Fitting the model to new images, an essential step to finding the most accurate parameters of the model for face image • Seek minimum difference between a real image and one synthesised by the appearance model. • Difference vector: • Ii = vector of grey-level values in the image • Im = vector of grey-level values for the current model parameters

  8. Experimental Results (1) • Example Training Images

  9. Experimental Results (2) • Result 1:

  10. Experimental Results (3) • Result 2:

  11. Experimental Results (4) • Result 3:

  12. Conclusion • AAM is a model of shape and grey-level appearance that can generalize to almost any image. • AAM is suitable to extract compact features for various applications, such as recognition, tracking, medical image segmentation, synthesis, etc.

  13. Demo • Downloading AAM Tool from this link • http://www.isbe.man.ac.uk/~bim/software/am_tools_doc/

  14. References • Cootes T. F. Edwards G. J., and Taylor C. J., “Active Appearance Models”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 23, No. 6, Jun 2001. • Gao X., Ya S., Li X., and Tao D., “A Review of Active Apperance Models”, IEEE Transactions on Systems, Man, and Cybernetics-Part C: Applications and Reviews, Vol. 40, No. 2, Mar 2010. • www.isbe.man.ac.uk/~bim/Models/aam.html

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