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IJCV
2007
196views more  IJCV 2007»
13 years 7 months ago
Weakly Supervised Scale-Invariant Learning of Models for Visual Recognition
We investigate a method for learning object categories in a weakly supervised manner. Given a set of images known to contain the target category from a similar viewpoint, learning...
Robert Fergus, Pietro Perona, Andrew Zisserman
ICRA
2003
IEEE
169views Robotics» more  ICRA 2003»
14 years 21 days ago
Robust model-based 3D object recognition by combining feature matching with tracking
− We propose a vision based 3D object recognition and tracking system, which provides high level scene descriptions such as object identification and 3D pose information. The sys...
Sungho Kim, In-So Kweon, Incheol Kim
ICPR
2008
IEEE
14 years 8 months ago
Object recognition and segmentation using SIFT and Graph Cuts
In this paper, we propose a method of object recognition and segmentation using Scale-Invariant Feature Transform (SIFT) and Graph Cuts. SIFT feature is invariant for rotations, s...
Akira Suga, Keita Fukuda, Tetsuya Takiguchi, Yasuo...
CVPR
2005
IEEE
14 years 9 months ago
A Sparse Object Category Model for Efficient Learning and Exhaustive Recognition
We present a "parts and structure" model for object category recognition that can be learnt efficiently and in a semisupervised manner: the model is learnt from example ...
Robert Fergus, Pietro Perona, Andrew Zisserman
CVPR
2008
IEEE
14 years 9 months ago
Object recognition and segmentation by non-rigid quasi-dense matching
In this paper, we present a non-rigid quasi-dense matching method and its application to object recognition and segmentation. The matching method is based on the match propagation...
Esa Rahtu, Janne Heikkilä, Juho Kannala, Sami...