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» Object correspondence as a machine learning problem
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ICML
2004
IEEE
16 years 2 months ago
Training conditional random fields via gradient tree boosting
Conditional Random Fields (CRFs; Lafferty, McCallum, & Pereira, 2001) provide a flexible and powerful model for learning to assign labels to elements of sequences in such appl...
Thomas G. Dietterich, Adam Ashenfelter, Yaroslav B...
UAI
2004
15 years 3 months ago
Recovering Articulated Object Models from 3D Range Data
We address the problem of unsupervised learning of complex articulated object models from 3D range data. We describe an algorithm whose input is a set of meshes corresponding to d...
Dragomir Anguelov, Daphne Koller, Hoi-Cheung Pang,...
RSS
2007
145views Robotics» more  RSS 2007»
15 years 3 months ago
Semantic Modeling of Places using Objects
— While robot mapping has seen massive strides , higher level abstractions in map representation are still not widespread. Maps containing semantic concepts such as objects and l...
Ananth Ranganathan, Frank Dellaert
ECCV
2010
Springer
15 years 5 months ago
Recursive Coarse-to-Fine Localization for fast Object Detection
Cascading techniques are commonly used to speed-up the scan of an image for object detection. However, cascades of detectors are slow to train due to the high number of detectors a...
Marco Pedersoli, Jordi Gonzàlez, Andrew D. Bagdan...
ALS
2003
Springer
15 years 7 months ago
Not Everything We Know We Learned
This is foremost a methodological contribution. It focuses on the foundation of anticipation and the pertinent implications that anticipation has on learning (theory and experiment...
Mihai Nadin