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Structured Forests for Fast Edge Detection. Piotr Dollár and Larry Zitnick. what defines an edge?. Brightness Color Texture Parallelism Continuity Symmetry …. Let the data speak. 1. Accuracy. 2. Speed. I. data driven edge detection. edge detection as classification. { 0, 1 }.

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Structured Forests for Fast Edge Detection


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    Presentation Transcript
    1. Structured Forests for Fast Edge Detection Piotr Dollár and Larry Zitnick

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

    3. 1. Accuracy 2. Speed

    4. I. data driven edge detection

    5. edge detection as classification { 0, 1 } Hard! positives Supervised Learning of Edges and Object Boundaries CVPR 2006, Piotr Dollár, ZhuowenTu, Serge Belongie

    6. edge have structure

    7. sketch tokens Sketch Tokens, CVPR 2013. Joseph Lim, C. Zitnick, and P. Dollár

    8. random forests

    9. upgrading the output space dimensionality 2 { 0, 1 } { … } 151 2256

    10. II. structured edge learning

    11. structured forests Structured Class-Labels in Random Forests for Semantic Image Labelling, ICCV 2011, P. Kontschieder, S. Rota Bulò, H. Bischof, M. Pelillo

    12. tree training

    13. node training low entropy split  high entropy split 

    14. how to train? ? ? good split  bad split 

    15. minimize entropy cluster ? ?

    16. III. structured edge detection

    17. structured forests

    18. sliding window detector

    19. sliding window detector pixel output  structured output 

    20. multiscale detection

    21. multiscale detection + + ½ x 1 x 2 x

    22. IV. results

    23. 60Hz ODS = 0.72 SS T=1 30Hz 6Hz ODS = 0.74 ODS = 0.73 SS T=4 MS T=4 gPb ODS=.73 FPS ≈ 1/240 Hz gPb ODS=.73 SS=single-scale MS=multi-scale T=# trees

    24. thanks! source code available online