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ICCV
2009
IEEE
17 years 15 days ago
Quantifying Contextual Information for Object Detection
Context is critical for minimising ambiguity in object de- tection. In this work, a novel context modelling framework is proposed without the need of any prior scene segmen- tat...
Wei-Shi Zheng, Shaogang Gong and Tao Xiang
ICCV
2009
IEEE
2030views Computer Vision» more  ICCV 2009»
17 years 15 days ago
Robust Tracking-by-Detection using a Detector Confidence Particle Filter
We propose a novel approach for multi-person trackingby- detection in a particle filtering framework. In addition to final high-confidence detections, our algorithm uses the con...
Michael D. Breitenstein, Fabian Reichlin, Bastian ...
ICCV
2009
IEEE
17 years 15 days ago
Evaluating Information Contributions of Bottom-up and Top-down Processes
This paper presents a method to quantitatively evaluate information contributions of individual bottom-up and topdown computing processes in object recognition. Our objective is...
Xiong Yang, Tianfu Wu, Song-Chun Zhu
ICCV
2009
IEEE
17 years 15 days ago
Deformable Model Fitting with a Mixture of Local Experts
Local experts have been used to great effect for fitting deformable models to images. Typically, the best location in an image for the deformable model’s landmarks are found t...
Jason M. Saragih, Simon Lucey, Jeffrey F. Cohn
ICCV
2009
IEEE
1318views Computer Vision» more  ICCV 2009»
17 years 15 days ago
Non-Local Sparse Models for Image Restoration
We propose in this paper to unify two different ap- proaches to image restoration: On the one hand, learning a basis set (dictionary) adapted to sparse signal descriptions has p...
Julien Mairal, Francis Bach, Jean Ponce, Guillermo...
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