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Multiple View Object Cosegmentation using Appearance and Stereo Cues

Published on Nov 12, 20124890 Views

Adarsh Kowdle

We present an automatic approach to segment an object in calibrated images acquired from multiple viewpoints. Our system starts with a new piecewise planar layer-based stereo algorithm that estimates

ECCV 2012 - Firenze

Related categories

Object Recognition

Chapter list

Multiple View Object Cosegmentation using Appearance and Stereo Cues00:00
Image 100:04
Final result using our approach00:19
Previous Work (1)00:23
Previous Work (2)00:59
Contributions01:40
Overview (1)01:56
Overview (2)02:07
Piecewise planer stereo (1)02:25
Piecewise planer stereo (2)02:31
Piecewise planer stereo (3)02:46
Piecewise planer stereo (4)03:00
Piecewise planer stereo (5)03:09
Piecewise planer stereo (6)03:18
Piecewise planer stereo (7)03:42
Piecewise planer stereo (8)04:03
Piecewise planer stereo (9)04:07
Piecewise planer stereo (10)04:26
Piecewise planer stereo (11)04:45
Piecewise planer stereo (12)05:04
Piecewise planer stereo (13)05:09
Overview (3)05:25
Region-level FG/BG labeling (1)05:30
Region-level FG/BG labeling (2)05:36
Region-level FG/BG labeling (3)05:45
Region-level FG/BG labeling (4)06:24
Region-level FG/BG labeling (5)06:32
Region-level FG/BG labeling (6)06:39
Region-level FG/BG labeling (7)06:49
Region-level FG/BG labeling (9)07:01
Region-level FG/BG labeling (10)07:06
Region-level FG/BG labeling (11)07:17
Region-level FG/BG labeling (12)07:30
Region-level FG/BG labeling (13)07:40
Overview (4)07:53
Multiview FG/BG labeling (1)07:58
Multiview FG/BG labeling (2)08:06
Multiview FG/BG labeling (3)08:32
Multiview FG/BG labeling (4)08:40
Multiview FG/BG labeling (5)08:52
Multiview FG/BG labeling (6)08:57
Overview (5)09:09
Datasets09:14
Quantitative results (1)09:29
Quantitative results (2)09:35
Quantitative results (3)09:44
Quantitative results (4)09:47
Quantitative results (5)09:57
Comparisons (1)10:00
Comparisons (2)10:35
Quantitative results (6)10:53
Quantitative results (7)11:06
Quantitative results (8)11:17
Conclusions11:30
Thank you11:50
Additional Results11:54