1 / 42

Reduksi Dimensi Image dengan Principal Components Analysis (PCA)

Reduksi Dimensi Image dengan Principal Components Analysis (PCA) . Sumber: Trucco & Verri chap. 10 Standford Vision & Modeling. Contoh: problem Pattern Recognition. Rotate coordinate system:. Problem Dimensi tinggi ??. PCA (Principal Component Analysis).

devin
Download Presentation

Reduksi Dimensi Image dengan Principal Components Analysis (PCA)

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Reduksi Dimensi Image dengan Principal Components Analysis (PCA) Sumber: Trucco & Verri chap. 10 Standford Vision & Modeling

  2. Contoh: problem Pattern Recognition

  3. Rotate coordinate system:

  4. Problem Dimensi tinggi ??

  5. PCA (Principal Component Analysis) • Untuk reduksi dimensi data (Dimensional Reduction) !!! • Ekstraksi struktur data dari dataset high dimenson. • Mencari basis signal berdasarkan data statistik objek.

  6. PCA

  7. PCA

  8. Demo dengan Matlab: • Mencari basis signal citra wajah. • Image recognition, face recognition.

  9. PCA

  10. Reduksi dimensi linear: High-dimensional Input Space

  11. Linear Subspace: = + = + 1.7

  12. Linear Subspace:

  13. Principal Components Analysis: m

  14. Contoh: Data: Kirby, Weisser, Dangelmayer 1993

  15. Contoh: Data: New Basis Vectors PCA

  16. Contoh: Data: EigenLips PCA

  17. Contoh: Face Recognition dengan Eigenfaces (Turk+Pentland, ):

  18. Contoh: Face Recognition System (Moghaddam+Pentland):

  19. Contoh: Visual Cortex Hubel

  20. Contoh: Visual Cortex Hubel

  21. Contoh: Receptive Fields Hubel

  22. Contoh: Receptive Fields Hancock et al: The principal components of natural images

  23. Contoh: Receptive Fields Hancock et al: The principal components of natural images

  24. Contoh: Active Appearance Models (AAM): (Cootes et al)

  25. Contoh: Active Appearance Models (AAM): (Cootes et al)

  26. Contoh: Active Appearance Models (AAM): (Cootes et al)

  27. Contoh: 3D Morphable Models (Blanz+Vetter)

  28. Ulasan Constrain - V V E(V) Analytically derived: Affine, Twist/Exponential Map Learned: Linear/non-linear Sub-Spaces

  29. Non-Rigid Constrained Spaces E(S) Constrain S = (p ,…,p ) 1 n

  30. Mixture Models Non-Rigid Constrained Spaces • Linear Subspaces: • Small Basis Set • Principal Components • Analysis Nonlinear Manifolds:

  31. Manifold Learning EM Mixture of Patches Training Data

  32. Mixture of Projections

  33. Contoh: Eigen Tracking (Black and Jepson)

  34. Contoh: Shape Models for tracking:

  35. Feature/Shape Models secara umum: Visual Motion Contours: Blake, Isard, Reynard

  36. Feature/Shape Models secara umum: Visual Motion Contours: Blake, Isard, Reynard

  37. Linear Discriminant Analysis:

  38. Fisher’s linear discriminant:

  39. Contoh: Eigenfaces vs Fisherfaces Glasses or not Glasses ?

  40. Contoh: Eigenfaces vs Fisherfaces Input New Axis Belhumeur, Hespanha, Kriegman 1997

  41. Basis Shape Algorithms lainnya: • ICA (Independent Components Analysis, Bell+Sejnowski) • Maximize Entropy (or spread of output distribution):

  42. Basis Shape Algorithms lainnya: • NMF (non-negative matrix factorization, Lee+Seung) • LNMF (local NMF, Li et al)

More Related