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» Markov Random Field Modeling in Computer Vision
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ICIP
2008
IEEE
14 years 11 months ago
Implicit spatial inference with sparse local features
This paper introduces a novel way to leverage the implicit geometry of sparse local features (e.g. SIFT operator) for the purposes of object detection and segmentation. A two-clas...
Deirdre O'Regan, Anil C. Kokaram
CVPR
2010
IEEE
1373views Computer Vision» more  CVPR 2010»
14 years 5 months ago
Harmony Potentials for Joint Classification and Segmentation
Hierarchical conditional random fields have been successfully applied to object segmentation. One reason is their ability to incorporate contextual information at different scales....
Xavier Boix, Josep M. Gonfaus, Joost van de Weijer...
ACL
2009
13 years 7 months ago
Do Automatic Annotation Techniques Have Any Impact on Supervised Complex Question Answering?
In this paper, we analyze the impact of different automatic annotation methods on the performance of supervised approaches to the complex question answering problem (defined in th...
Yllias Chali, Sadid A. Hasan, Shafiq R. Joty
CIVR
2009
Springer
257views Image Analysis» more  CIVR 2009»
14 years 3 months ago
Trajectory-based handball video understanding
This paper presents a content-based approach for understanding handball videos. Tracked players are characterized by their 2D trajectories in the court plane. The trajectories and...
Alexandre Hervieu, Patrick Bouthemy, Jean-Pierre L...
PRL
2006
132views more  PRL 2006»
13 years 9 months ago
Modelling and accuracy estimation of a new omnidirectional depth computation sensor
Depth computation is an attractive feature in computer vision. The use of traditional perspective cameras for panoramic perception requires several images, most likely implying th...
Radu Orghidan, Joaquim Salvi, El Mustapha Mouaddib