Back to Algorithm Ranking.
[Grayscale] Boundary Detection Benchmark: Algorithm "Boosted Edge Learning"
Piotr Dollar, Zhuowen Tu, and Serge Belongie,
"Supervised Learning of Edges and Object Boundaries", IEEE Computer Vision and Pattern Recognition (CVPR), June, 2006.
In this paper, we propose a novel supervised learning algorithm for edge and
object boundary detection which we refer to as Boosted Edge Learning or BEL for
short. A decision of an edge point is made independently at each location in
the image; a very large aperture is used providing significant context for each
decision. In the learning stage, the algorithm selects and combines a large
number of features across different scales in order to learn a discriminative
model using an extended version of the Probabilistic Boosting Tree
classification algorithm. The learning based framework is highly adaptive and
there are no parameters to tune.
software
Click on an image for additional details.
#1 (119082) F=0.70
|
|
#2 (170057) F=0.66
|
|
#3 (58060) F=0.50
|
|
#4 (163085) F=0.49
|
|
#5 (42049) F=0.92
|
|
#6 (167062) F=0.67
|
|
#7 (157055) F=0.73
|
|
#8 (295087) F=0.71
|
|
#9 (24077) F=0.74
|
|
#10 (78004) F=0.79
|
|
#11 (220075) F=0.62
|
|
#12 (45096) F=0.76
|
|
#13 (38092) F=0.78
|
|
#14 (43074) F=0.66
|
|
#15 (16077) F=0.57
|
|
#16 (86000) F=0.62
|
|
#17 (101085) F=0.74
|
|
#18 (219090) F=0.71
|
|
#19 (89072) F=0.68
|
|
#20 (300091) F=0.57
|
|
#21 (126007) F=0.72
|
|
#22 (156065) F=0.66
|
|
#23 (76053) F=0.61
|
|
#24 (296007) F=0.66
|
|
#25 (175032) F=0.49
|
|
#26 (253027) F=0.63
|
|
#27 (304034) F=0.47
|
|
#28 (86016) F=0.39
|
|
#29 (103070) F=0.68
|
|
#30 (8023) F=0.41
|
|
#31 (260058) F=0.63
|
|
#32 (41033) F=0.62
|
|
#33 (291000) F=0.57
|
|
#34 (109053) F=0.61
|
|
#35 (130026) F=0.52
|
|
#36 (241004) F=0.85
|
|
#37 (108082) F=0.43
|
|
#38 (285079) F=0.71
|
|
#39 (147091) F=0.71
|
|
#40 (69040) F=0.50
|
|
#41 (14037) F=0.65
|
|
#42 (54082) F=0.54
|
|
#43 (189080) F=0.78
|
|
#44 (229036) F=0.67
|
|
#45 (62096) F=0.79
|
|
#46 (271035) F=0.73
|
|
#47 (167083) F=0.61
|
|
#48 (12084) F=0.48
|
|
#49 (69015) F=0.79
|
|
#50 (148089) F=0.68
|
|
#51 (160068) F=0.72
|
|
#52 (145086) F=0.71
|
|
#53 (216081) F=0.79
|
|
#54 (97033) F=0.69
|
|
#55 (182053) F=0.77
|
|
#56 (208001) F=0.66
|
|
#57 (19021) F=0.65
|
|
#58 (227092) F=0.75
|
|
#59 (134035) F=0.71
|
|
#60 (223061) F=0.58
|
|
#61 (253055) F=0.75
|
|
#62 (148026) F=0.52
|
|
#63 (210088) F=0.73
|
|
#64 (86068) F=0.59
|
|
#65 (3096) F=0.90
|
|
#66 (41069) F=0.68
|
|
#67 (21077) F=0.65
|
|
#68 (196073) F=0.72
|
|
#69 (108070) F=0.41
|
|
#70 (123074) F=0.57
|
|
#71 (376043) F=0.60
|
|
#72 (306005) F=0.70
|
|
#73 (38082) F=0.59
|
|
#74 (33039) F=0.61
|
|
#75 (108005) F=0.53
|
|
#76 (106024) F=0.72
|
|
#77 (302008) F=0.66
|
|
#78 (102061) F=0.62
|
|
#79 (197017) F=0.78
|
|
#80 (299086) F=0.71
|
|
#81 (37073) F=0.83
|
|
#82 (241048) F=0.70
|
|
#83 (65033) F=0.73
|
|
#84 (55073) F=0.52
|
|
#85 (66053) F=0.74
|
|
#86 (143090) F=0.69
|
|
#87 (85048) F=0.76
|
|
#88 (42012) F=0.60
|
|
#89 (351093) F=0.71
|
|
#90 (361010) F=0.73
|
|
#91 (175043) F=0.32
|
|
#92 (87046) F=0.60
|
|
#93 (105025) F=0.56
|
|
#94 (236037) F=0.51
|
|
#95 (101087) F=0.77
|
|
#96 (304074) F=0.62
|
|
#97 (296059) F=0.85
|
|
#98 (159008) F=0.59
|
|
#99 (385039) F=0.81
|
|
#100 (69020) F=0.65
|
|
Page generated on 20-Feb-2013 11:08:18.