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 ID: 134917
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Slide1
Structured Forests for
Fast Edge Detection
Piotr Dollár and Larry ZitnickSlide2
what defines an edge?
Brightness
ColorTextureParallelismContinuitySymmetry …
Let the data speak.Slide3
1. Accuracy
2. SpeedSlide4
I. data driven edge detectionSlide5
edge detection as classification
Supervised Learning of Edges and Object Boundaries
CVPR 2006, Piotr Dollár, Zhuowen Tu, Serge Belongie
positives
Hard!
{ 0, 1 }Slide6
edge have structureSlide7
sketch tokens
Sketch Tokens, CVPR 2013. Joseph Lim
, C. Zitnick, and P. DollárSlide8
random forestsSlide9
upgrading the output space
{ 0, 1 }{
… }
dimensionality
2
2
256
151Slide10
II. structured edge learningSlide11
structured forests
Structured
Class-Labels in Random Forests for Semantic Image Labelling,
ICCV 2011,
P. Kontschieder
,
S. Rota
Bulò
, H.
Bischof
, M.
PelilloSlide12
tree trainingSlide13
node training
high entropy split
low entropy split
Slide14
how to train?
bad split
good split
?
?Slide15
?
?
cluster
minimize entropySlide16
III. structured edge detectionSlide17
structured forestsSlide18
sliding window detectorSlide19
sliding window detector
pixel output
structured output Slide20
multiscale detectionSlide21
½ x
2 x
1 x
multiscale detection
+
+Slide22
IV. resultsSlide23
SS=single-scale
MS=multi-scale T=# trees
6Hz
SS T=1
SS T=4
MS T=4
ODS = 0.72
ODS = 0.74
ODS = 0.73
60Hz
30Hz
gPb ODS=.73
gPb ODS=.73
FPS ≈ 1/240 HzSlide24Slide25Slide26Slide27Slide28
thanks!
source code available online