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ECCV
1994
Springer
13 years 11 months ago
Markov Random Field Models in Computer Vision
A variety of computer vision problems can be optimally posed as Bayesian labeling in which the solution of a problem is dened as the maximum a posteriori (MAP) probability estimate...
Stan Z. Li
EMMCVPR
2005
Springer
14 years 1 months ago
Object Categorization by Compositional Graphical Models
This contribution proposes a compositionality architecture for visual object categorization, i.e., learning and recognizing multiple visual object classes in unsegmented, cluttered...
Björn Ommer, Joachim M. Buhmann
IROS
2007
IEEE
157views Robotics» more  IROS 2007»
14 years 1 months ago
View-adaptive manipulative action recognition for robot companions
— This paper puts forward an approach for a mobile robot to recognize the human’s manipulative actions from different single camera views. While most of the related work in act...
Zhe Li, Sven Wachsmuth, Jannik Fritsch, Gerhard Sa...
IJCV
2008
139views more  IJCV 2008»
13 years 7 months ago
Multilevel Image Coding with Hyperfeatures
Histograms of local appearance descriptors are a popular representation for visual recognition. They are highly discriminant with good resistance to local occlusions and to geomet...
Ankur Agarwal, Bill Triggs
ICDAR
2005
IEEE
14 years 1 months ago
Learning Diagram Parts with Hidden Random Fields
Many diagrams contain compound objects composed of parts. We propose a recognition framework that learns parts in an unsupervised way, and requires training labels only for compou...
Martin Szummer