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A New Method for Segmentation and Image differentiation

A New Method for Segmentation and Image differentiation. Ali Farhadi farhadi@ipm.ir Institute for Studies in Theoretical Physics and Mathematics Tehran-Iran Scientific Computing Center Vision Group. Outline of Results.

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A New Method for Segmentation and Image differentiation

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  1. A New Method for Segmentation and Image differentiation Ali Farhadi farhadi@ipm.ir Institute for Studies in Theoretical Physics and Mathematics Tehran-Iran Scientific Computing Center Vision Group

  2. Outline of Results • How to differentiate, at a crude level, between different types of images like between a texture and a facial image. • How to detect an extraneous object in a texture environment. • How to segment an image.

  3. Detecting of an Extraneous Object Demonstration

  4. Detection & Reconstruction of Colored Textures Detecting of an Extraneous Object ?

  5. Detecting of an Extraneous Object Add an extra obj

  6. Detecting an Extraneous Object Some samples

  7. Segmentation • Segmentation is achieved in two steps • 1. Determination of Candidate Windows • 2. Locating the Boundaries of Objects.

  8. SegmentationDetection of Candidate Windows

  9. SegmentationDetection of Candidate Windows

  10. SegmentationDetection of Candidate Windows

  11. The Methodology • Classification and Image Differentiation • Covariance Matrices. • Analysis of Scatter Plots of Feature Vectors . • Analysis of Dual Feature Vectors . • Application of DCT. Detection of an Extraneous Object • Application of Linear Predictive Coding. • Vector Quantization for Reconstruction. • Segmentation of still images • Detection of Candidate Windows. • Locating the Boundaries . Segmentation of movie images

  12. zoom Classification Covariance Matrices Neighborhood Configuration

  13. Classification Covariance Matrices • Linear Predictive Coding W = window index N = neighborhood configuration (o N ) X(p) = brightness of pixel p (value of the pixel)

  14. Classification Covariance Matrices Eigenvalues of Covariance Matrices of LPC Coefficients

  15. Classification Analysis of Scatter Plots • Projection of Feature Vectors Feature Vector = LPC Coefficients • 20 Dimensional Scatter Plots • (Curse of Dimensionality) • Projection Pursuit

  16. Classification Analysis of Scatter Plots • Projection Pursuit : Correlated Coefficients: 6,10,11,15 5,7,14,16 1,2,3,4,8,9,12,13,17,18,19,20

  17. Classification Analysis of Scatter Plots Lena Trunk of the tree V(Lena)=56.709 V(Tree)=1.244

  18. Classification Analysis of Scatter Plots lena tree

  19. Classification Analysis of Scatter Plots Scaled Volumes of Convex Closure of Projected LPC Coef. :

  20. Classification Analysis of Scatter Plots Out-layer points correspond to the blocks containing the BUG. Trunk of Tree Trunk of Tree with Bug

  21. Classification Analysis of Scatter Plots • Rotation of Images LPC Coefficients for original and Rotated Images. • Area of Convex Hull • Clustering • Analysis of Regression

  22. Classification Analysis of Scatter Plots

  23. Classification Analysis of Scatter Plots Scaled Areas of Convex Hulls :

  24. Classification Analysis of Dual Feature Vector • Dual Feature Vector : • FFT of Differences of LPC Coefficients of Contiguous Windows. • Projection Pursuit • Volume of Convex Closure of Projections

  25. Classification Analysis of Dual Feature Vector lena tree

  26. Classification Analysis of Dual Feature Vector Scaled Volumes of Convex closure of Dual Data :

  27. Classification Analysis of DCT • DCT of 8*8 overlapping Windows . • We keep 2nd through 10th DCT coefficients .

  28. Classification Applications of DCT • Construct curve from retained DCT coefficients as the window moves. • Calculate the distribution of areas under the curves.

  29. Classification Applications of DCT

  30. Outline • Classification • Covariance Matrices. • Analysis of Scatter Plots of Feature Vectors . • Analysis of Dual Feature Vectors . • Application of DCT. Detection of an Extraneous Object • Application of Linear Predictive Coding. • Vector Quantization for Reconstruction. • Segmentation of still images • Detection of Candidate Windows. • Locating the Boundaries . Segmentation of movie images

  31. Detecting of an Extraneous Object Preliminary Detection Variation of LPC coefficients Designated Window : T = Threshold

  32. 1 2 3 4 ? Detecting of an Extraneous Object Reconstruction • Vector Quantization • Causal Window

  33. Detecting of an Extraneous Object Demonstration

  34. Detecting an Extraneous Object Some samples

  35. Outline • Classification • Covariance Matrices. • Analysis of Scatter Plots of Feature Vectors . • Analysis of Dual Feature Vectors . • Application of DCT. Detection of an Extraneous Object • Application of Linear Predictive Coding. • Vector Quantization for Reconstruction. • Segmentation of still images • Detection of Candidate Windows. • Locating the Boundaries . Segmentation of movie images

  36. SegmentationDetection of Candidate Windows Partition Image into 64*64 non-overlapping Windows. Application of Classification Algorithms to Individual Windows .

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