1 / 23

Automatic Image Rescaling Preserving Design Intention

Automatic Image Rescaling Preserving Design Intention. Research Update Prasad Gabbur. Scaling that preserves the design intention of the objects:. Simple scaling. Goal. Original:. Approach. Split input image into background (bg) and foreground (fg) layers

kato-blanchard
Download Presentation

Automatic Image Rescaling Preserving Design Intention

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. Automatic Image Rescaling Preserving Design Intention Research Update Prasad Gabbur

  2. Scaling that preserves the design intention of the objects: Simple scaling Goal Original:

  3. Approach • Split input image into background (bg) and foreground (fg) layers • Scale the bg and fg layers separately Background Foreground

  4. Background layer scaling • Scale and shift elements to fit new page • Ignore aspect ratio • Classification of background elements Area Horizontal (Top, Bottom) Vertical (Left, right)

  5. Scale Scale + Shift Scale +Shift Background layer scaling • Scale and shift of background elements Area To fit new page Horizontal To fit new page width Vertical To fit new page height Sx > Sy Sy > Sx

  6. Foreground layer scaling • Scale and shift elements to fit new page • Preserve aspect ratio • Classification of foreground elements Corner (TL, TR, BL, BR) Horizontal (Top, Bottom) Vertical (Left, Right)

  7. Scale + Shift Scale + Shift Scale + Shift Foreground layer scaling Corner Horizontal Vertical Sy > Sx Sx > Sy

  8. Foreground layer scaling • Scaling preserves aspect ratio (Scale factor = min (Sx, Sy)) • Shifting preserves distance ratio (dL/dR = const, dT/dB = const) Shift of horizontal elements Shift of vertical elements dT_old dL_new dR_new dT_new dL_old dR_old Horizontal Vertical dB_old dB_new Sy > Sx (dT_new / dB_new) = (dT_old / dB_old) Original Sx > Sy (dL_new / dR_new) = (dL_old / dR_old)

  9. Element extraction • Elements are connected regions in the foreground or background layer Background layer(s) Foreground layer(s) Multilayer image Extract alpha channel Label connected components

  10. Connected component labeling • Group together spatially connected pixels as a single component • Each component is assigned a unique integer label 4-connectivity 8-connectivity

  11. Xu Xl Xc Connected component labeling • Region coloring algorithm (4-connected) [Ballard & Brown, 1982] • Each pixel (Xc) in the image is scanned with the following mask: new_label = 1 If (XuЄ background & XlЄ foreground), then label (Xc) = label (Xl) Else if (XuЄ foreground & XlЄ background), then label (Xc) = label (Xu) Else if (XuЄ foreground & XlЄ foreground), then label (Xc) = min ( label (Xl), label (Xu) ) Else label (Xc) = new_label new_label = new_label + 1

  12. Connected component labeling • Basic region coloring algorithm is slow • Requires multiple passes through the image • A faster version is realized with the help of a custom data structure • An array of the above data type can store information about all connected components • Only one pass through the image is necessary

  13. … … … … … … … … Connected component labeling • A single raster scan of the image gives rise to following structure Image with two connected components Data structure at the end of a single image scan

  14. Connected component labeling • Links in the array can be visualized as a tree structure • Nodes in the tree are equivalent labels of a connected component A fictitious connected component

  15. Connected component labeling • Trees with different configurations are possible depending on region complexity • All branches merge at the bottom One branch Two branches Four branches

  16. 1 1 6 1 1 2 7 1 1 3 1 8 Resolve 4 1 9 1 5 1 10 1 1 11 1 12 Connected component labeling • Resolving label equivalences • All the equivalent labels are assigned the least value among them by stepping through the tree

  17. Connected component labeling • Resolving label equivalences Resolve

  18. Elements • Each connected component in the background or foreground layer is an element • Geometric properties (bounding box center and limits) are computed as part of the labeling process • Elements are classified based on the geometric properties Background layer Foreground layer

  19. Scaling issues* • Scale up • Sparse distribution of pixels in the output image • Bilinear interpolation to fill in pixel values • Scale down • Aliasing due to sub-sampling • Low pass filtering before sub-sampling * Thanks to Jian Fan, HP Labs.

  20. Results • Background layer Scaled (Sx >S y) Original Labeled Scaled (Sy >S x)

  21. Results • Foreground layer Scaled (Sx >S y) Original Labeled Scaled (Sy >S x)

  22. Next • Stitch together the foreground and background layers • Work on an XML design for input

  23. Thank you!

More Related