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Project Image Morphing

Project Image Morphing. Presented By. Sharmila Gupta. Digital Image Manipulation. Simple pixel Modification. Interpolation/ Extrapolation. Compositing. Convolution. Dithering. Warping. Morphing. Simple Pixel Modification. Applying same degree of change in every pixel.

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Project Image Morphing

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  1. Project Image Morphing Presented By Sharmila Gupta

  2. Digital Image Manipulation Simple pixel Modification Interpolation/ Extrapolation Compositing Convolution Dithering Warping Morphing

  3. Simple Pixel Modification Applying same degree of change in every pixel Convert to Gray f(r,g,b) = .3r + .52g + 10b Threshold if v > threshold then f(v) = 1 else f(v) = 0 Invert f(v) = 1-v Brighten/darken f(v) = ßv for ß >= 0

  4. Image Interpolation/Extrapolation Offers a general, unifying approach to many common point and area image processing operations. Brightness, contrast, saturation, tint, and sharpness can all be controlled with one formula, separately or simultaneously. Change Contrast Original More Contrast Less Contrast Change Saturation Original More Saturated Less Saturated

  5. median filtered image Image with “impulse” noise Image Interpolation/Extrapolation Brighten/darken Original More Bright Less Bright Noise removal

  6. Compositing Compisiting images involves combining separate image layers into one Layers may be moved and arranged. Composite Original

  7. Convolution Used for different image processing, removing blur-ness, defining edge, restore special effect by filtering, masking or other technique. Edge Enhancement Original Edge Defined Blur-ness Blurred De-blurred

  8. Dithering Monitors and image files limited to 256 colors can create the illusion of more colors by Dithering the available colors in a scattered pattern, approximating the desired color. Image editors often use dithering to convert true color images to indexed color images. True-color Image Dithered Image

  9. What is Image Morphing?

  10. Image Morphing It comes from the word Metamorphosis Metamorphosis: Change shape, size, form or appearance

  11. What is Image Morphing ? Morphing can be defined as: Transition from one object to another Process of transforming one image into another. An animation technique that allows you to blend two still images, creating a sequence of in – between pictures that when played in Quick Time, metamorphoses the first image into the second.

  12. What is Image Morphing ?contd.. A process of transforming two images where it seems like the first melts, dissolves and re-arranges itself to become the second one

  13. How is Morphing done?

  14. General View It is broadly two-step process: First image is gradually distorted and is faded out The second image starts out totally distorted toward the first and is faded in.

  15. Classification of Morphing Morphing classified into three types Image-based Method Volume-based approaches Boundary representations (B-rep) -based approaches

  16. Image-based Morphing Two steps are: Warping two images to make them having the same shape Cross dissolving the resulting images

  17. Image-based Morphing WARPING ?

  18. Image-Based Morphing - Warping A warp is a 2-D geometric transformation and generates a distorted image when it is applied to an image Warping an image means apply a given deformation to it Two ways to warp an image: Forward Mapping Reverse Mapping

  19. Forward Mapping Each pixel in the source image is mapped to an appropriate pixel in the destination image. Source Image Destination Image Some pixels in the destination image may not be mapped Source Image Destination Image

  20. Reverse Mapping This method goes through each pixel in the destination image and samples an appropriate source image pixel All destination image pixels are mapped to some source image pixel. This mapping used in the Beier/Neely line morphing method.

  21. Cross Dissolving A cross-dissolve is a sequence of images which implements a gradual fade from one to the other. Very primitive No smooth transition

  22. Image-based Morphing - Example

  23. Image-based Morphing - Example Source Image Destination Image Do Morphing

  24. Image-based Morphing - Example Baby to Grandpa Morphing Groom to Bride Compositing Warping

  25. Image-Based Morphing - Other option ! Better option than Cross - Dissolving: Field/Line Morphing Mesh Mapping Multilevel Free-form Deformation (MFFD)

  26. Field/Line Morphing What pixel coordinate in the source image do we sample for each pixel in destination image ? Correspondence achieved using feature line(s) in source and destination images Source Image Destination Image

  27. Field/Line Morphing Two step process Step I : Interpolating the lines: Interpolate the coordinates of the end points of every pair of lines. Step II : Warping the Images:Each of the source images has to be deformed towards the needed frame.The deformation works pixel by pixel is based on the reverse mapping. This algorithm is called Beier-Neely Algorithm.

  28. BEIER-NEELY ALGORITHM Divide the two image to be morphed into lines

  29. BEIER-NEELY ALGORITHM (contd.) Create Intermediate Morph for both images Intermediate Morph ? Use corresponding lines in both the images to redefine a new position for each pixel according to a parameter t If t = 0 for First image and t = 1 for Second Image then Intermediate Morph: 0<t<1

  30. BEIER-NEELY ALGORITHM (contd.) Compute line endpoints in the intermediate morph A = endpoint of a line in First image B = endpoint of a line in Second image AB = endpoint of the line in Intermediate image = (1-t)*A+t*B Use Field Morphing to warp First Image according to intermediate line endpoints Use Field Morphing to warp Second Image according to intermediate line endpoints Cross-Dissolve the warped images using t to define weighted pixel average

  31. BEIER-NEELY ALGORITHM (contd.) Cross Dissolve the intermediate warped images Warped First Image Warped Second Image

  32. BEIER-NEELY ALGORITHM - PIXEL POSITION Compute position of pixel X in destination image relative to the line drawn in destination image.(x,y)  (u,v) Q’ Q X v u P’ P Source Image Destination Image

  33. BEIER-NEELY ALGORITHM - PIXEL POSITION Compute coordinates of pixel in source image whose position relative to the line drawn in source image is (u,v). (u,v) (x’,y’) Q’ Q X v v u u P’ P Source Image Destination Image

  34. BEIER-NEELY ALGORITHM - PIXEL POSITION Computation of pixel X with respect to Line (x,y) (u,v) Q (qx,qy) (x-px)(qx-px)+(y-py)(qy-py) X (x,y) v (X-P).(Q-P) (qx-px)+(qy-py) ||(Q-P)||2 u (X-P).Perpendicular(Q-P) ||(Q-P)|| P (px,py) Destination Image (qy-py, -qx+px)

  35. BEIER-NEELY ALGORITHM-WEIGHTED AVG. Computation of weighted pixel average Q (qx,qy) b lengthp X (x,y) Weight = a + dist v length = (qx-px)2 + (qy-py)2 dist = abs(v) if o<u<1 or dist = distance of (x,y) from P if u < 0 or dist = distance of (x,y) from Q if u > 1 u P (px,py) (a,b) controls influence of line for points near it

  36. BEIER-NEELY ALGORITHM For each pixel X=(x,y) in the destination image DSUM=(0,0) , weightsum=0 for each line(Pi, Qi) calculate(ui,vi) based on Pi, Qi calculate (xi’, yi’) based on u,v and Pi, Qi calculate displacement Di = Xi’ – X for this line compute weight for line(Pi,Qi) DSUM+=Di*weight weightsum+=weight (x’y’) = (x,y)+DSUM/weightsum color at destination pixel(x,y) = color at source pixel(x’y’)

  37. Mesh Morphing • Mesh is a rough presentation of an object • How it is done ? Meshed Image • Source and Destination images are meshed • Meshes for both images are interpolated • Intermediate images are cross-dissolved

  38. Mesh Morphing-Algorithm • for each frame f do • Linearly interpolate mesh M, between Ms and Mt • warp Images to I1, using meshes Ms and M • warp Imaget to I2, using meshes Mt and M • Linearly interpolate image I1 and I2 • end

  39. Mesh Morphing

  40. Mesh Morphing - Pros and Cons • Pros All control points affects the warping equally. • Cons Hard to fit the mesh in the image Not enough control on certain points when needed. A Mesh A Rendered Image

  41. Multilevel Free Form Deformation (MFFD) Morphing two sequences of live action, rather than just two still images Mark all features in key frames Interpolate the features between key frames Do image metamorphosis on set of image pairs

  42. Multilevel Free Form Deformation (MFFD)

  43. Volume Based Approaches • Technique: The objects are expressed as level sets of distance functions Two steps: Warp: deform the 3D space in order to make the two objects to be morphed coincide as much as possible Interpolation: linear interpolate distance fields deformed by the warp • Interaction: The user interface allows to select feature (or anchor) points in each voxelized object space and map the anchor points of the source object to the anchor points of the target object

  44. Volume Based Approaches - Example

  45. Volume Based Approaches - Example

  46. Volume Based Approaches D. Cohen-Or, D. Levin, A. Solomovoci. Three-dimensional distance field metamorphosis. ACM Trans. Graphics 17:116-141, 1998 http://www.math.tau.ac.il/~levin/

  47. B-Rep Based Approaches Polyhedral Morphing using Feature based surface decomposition The users only need to specify a few corresponding pairs of features on the two polyhedra. They can then specify the trajectories along which these features travel during the morph using Bezier curves, as shown in the next page. The algorithm not only provides the user with high-level control in terms of specifying the features and trajectories, it also allows for local refinement

  48. B-Rep Based Approaches Destination Image Source Image

  49. B-Rep Based Approaches

  50. B-Rep Based Approaches A. Gregory, A. State, M. C. Lin, D. Manocha, and M. A. Livingston. Interactive surface decomposition for polyhedral morphing. The Visual Computer (1999) 15:453-470 http://www.cs.unc.edu/~geom/3Dmorphing/

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