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» Markov Random Field Modeling in Computer Vision
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KDD
2005
ACM
153views Data Mining» more  KDD 2005»
14 years 8 months ago
Improving discriminative sequential learning with rare--but--important associations
Discriminative sequential learning models like Conditional Random Fields (CRFs) have achieved significant success in several areas such as natural language processing, information...
Xuan Hieu Phan, Minh Le Nguyen, Tu Bao Ho, Susumu ...
AAAI
2008
13 years 10 months ago
CIGAR: Concurrent and Interleaving Goal and Activity Recognition
In artificial intelligence and pervasive computing research, inferring users' high-level goals from activity sequences is an important task. A major challenge in goal recogni...
Derek Hao Hu, Qiang Yang
ECCV
2008
Springer
14 years 9 months ago
Segmentation and Recognition Using Structure from Motion Point Clouds
We propose an algorithm for semantic segmentation based on 3D point clouds derived from ego-motion. We motivate five simple cues designed to model specific patterns of motion and 3...
Gabriel J. Brostow, Jamie Shotton, Julien Fauqueur...
CVPR
2010
IEEE
13 years 8 months ago
Label propagation in video sequences
This paper proposes a probabilistic graphical model for the problem of propagating labels in video sequences, also termed the label propagation problem. Given a limited amount of ...
Vijay Badrinarayanan, Fabio Galasso, Roberto Cipol...
DAGM
2009
Springer
13 years 11 months ago
An Efficient Linear Method for the Estimation of Ego-Motion from Optical Flow
Abstract. Approaches to visual navigation, e.g. used in robotics, require computationally efficient, numerically stable, and robust methods for the estimation of ego-motion. One of...
Florian Raudies, Heiko Neumann