structured forests for fast edge detection
DESCRIPTION
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 }. - PowerPoint PPT PresentationTRANSCRIPT
Structured Forests for Fast Edge Detection
Structured Forests for Fast Edge DetectionPiotr Dollr and Larry Zitnick
1what defines an edge?BrightnessColorTextureParallelismContinuitySymmetry
Let the data speak.21. Accuracy2. Speed3I. data driven edge detection4edge detection as classificationSupervised Learning of Edges and Object BoundariesCVPR 2006, Piotr Dollr, Zhuowen Tu, Serge Belongiepositives
Hard!{ 0, 1 }
5edge have structure
6sketch tokensSketch Tokens, CVPR 2013. Joseph Lim, C. Zitnick, and P. Dollr
7random forests
8upgrading the output space{ 0, 1 }{ }
dimensionality222561519II. structured edge learning10structured forestsStructured Class-Labels in Random Forests for Semantic Image Labelling, ICCV 2011,P. Kontschieder, S. Rota Bul, H. Bischof, M. Pelillo
11tree training12node traininghigh entropy split low entropy split 13how to train?bad split
good split ??
14
??clusterminimize entropy
15III. structured edge detection16structured forests
17sliding window detector
18sliding window detectorpixel output structured output
Comparison to storing only single pixel predictionNoisy, no coherent structureWould need far more trees!This is the secret to our speed / accuracy.
19multiscale detection
20
x2 x
1 xmultiscale detection++21IV. results22
SS=single-scale MS=multi-scale T=# trees6HzSS T=1SS T=4MS T=4ODS = 0.72ODS = 0.74ODS = 0.7360Hz30HzgPb ODS=.73 gPb ODS=.73 FPS 1/240 Hz23
24
thanks!source code available online