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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
2007
IEEE
16 years 8 months ago
Linear Predictors for Fast Simultaneous Modeling and Tracking
An approach for fast tracking of arbitrary image features with no prior model and no offline learning stage is presented. Fast tracking is achieved using banks of linear displacem...
Liam Ellis, Nicholas Dowson, Jiri Matas, Richard B...
ECML
2006
Springer
15 years 10 months ago
Approximate Policy Iteration for Closed-Loop Learning of Visual Tasks
Abstract. Approximate Policy Iteration (API) is a reinforcement learning paradigm that is able to solve high-dimensional, continuous control problems. We propose to exploit API for...
Sébastien Jodogne, Cyril Briquet, Justus H....
ICIP
2008
IEEE
16 years 8 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
ENGL
2007
164views more  ENGL 2007»
15 years 6 months ago
Vision Based Target-Tracking Realized with Mobile Robots using Extended Kalman Filter
— The paper presents a novel scheme for target-tracking realized with two mobile robots, where one robot is configured as tracker and the other as moving target. Fuzzy C-means cl...
Sheli Sinha Chaudhuri, Amit Konar