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IROS
2008
IEEE
144views Robotics» more  IROS 2008»
16 years 29 days ago
Frame rate object extraction from video sequences with self organizing networks and statistical background detection
— In many computer vision related applications it is necessary to distinguish between the background of an image and the objects that are contained in it. This is a difficult pr...
Thiago C. Bellardi, Dizan Vasquez, Christian Laugi...
AAAI
1996
15 years 7 months ago
A Hybrid Learning Approach for Better Recognition of Visual Objects
Real world images often contain similar objects but with different rotations, noise, or other visual alterations. Vision systems should be able to recognize objects regardless of ...
Ibrahim F. Imam, Srinivas Gutta
ICCV
2011
IEEE
14 years 6 months ago
Scene Recognition and Weakly Supervised Object Localization with Deformable Part-Based Models
Weakly supervised discovery of common visual structure in highly variable, cluttered images is a key problem in recognition. We address this problem using deformable part-based mo...
Megha Pandey, Svetlana Lazebnik
NIPS
2004
15 years 8 months ago
Contextual Models for Object Detection Using Boosted Random Fields
We seek to both detect and segment objects in images. To exploit both local image data as well as contextual information, we introduce Boosted Random Fields (BRFs), which use boos...
Antonio Torralba, Kevin P. Murphy, William T. Free...
ICCV
2009
IEEE
16 years 11 months ago
Active Skeleton for Non-rigid Object Detection
We present a shape-based algorithm for detecting and recognizing non-rigid objects from natural images. The existing literature in this domain often cannot model the objects ver...
Xiang Bai, Xinggang Wang, Longin Jan Latecki, Weny...