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From Edges to Objects: Upgrading Output Space with Pixel Prediction Sharpening

Explore the evolution of edge prediction to object recognition, enhancing output quality. Key factors include brightness, color continuity, symmetry, and texture. Discover the impact of structured forest predictions and sharpened pixel outputs on image quality and object detection rates. Dive into the advancements in segmentation techniques for hard object proposals, from Selective Search to SLIC and SEEDS. Access the source code online for further study.

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From Edges to Objects: Upgrading Output Space with Pixel Prediction Sharpening

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  1. From Edges to Objects Piotr Dollár and Larry Zitnick

  2. outline structured edge prediction [Dollár & Zitnick, ICCV13, arXiv14] from edges to objects[Zitnick & Dollár, ECCV14]

  3. what defines an edge? Brightness Color Texture Parallelism Continuity Symmetry … Let the data speak.

  4. upgrading the output space year bits { 0, 1 } 1 CVPR06 { … } 8 CVPR13 256 ICCV13

  5. random forests

  6. structured forests

  7. structured prediction

  8. pixel prediction

  9. sharpening

  10. sharpened prediction

  11. original image pixel  structured  sharpened 

  12. 30Hz ODS = 0.73 12.5Hz 2.5Hz ODS = 0.75 ODS = 0.74 SH SH+MS gPb ODS=.73 FPS ≈ 1/240 Hz gPb ODS=.73 SH = sharpen MS = multiscale

  13. outline structured edge prediction [Dollár & Zitnick, ICCV13, arXiv14] from edges to objects[Zitnick & Dollár, ECCV14]

  14. sliding window detection restrictive!

  15. segmentation for detection hard!

  16. object proposals Selective Search - 2011 Ojectness - 2010 CPMC - 2010 Random. Prim’s - 2013

  17. object proposals from ? sliding windows? segments? superpixels? SLIC - 2012 SEEDS - 2012

  18. from edges to objects

  19. fast 

  20. thanks! source code available online

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