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ECCV
2010
Springer
15 years 4 months ago
MIForests: Multiple-Instance Learning with Randomized Trees
Abstract. Multiple-instance learning (MIL) allows for training classifiers from ambiguously labeled data. In computer vision, this learning paradigm has been recently used in many ...
Christian Leistner, Amir Saffari, Horst Bischof
AROBOTS
2008
77views more  AROBOTS 2008»
15 years 4 months ago
Extending obstacle avoidance methods through multiple parameter-space transformations
Obstacle avoidance methods approach the problem of mobile robot autonomous navigation by steering the robot in real-time according to the most recent sensor readings, being suitab...
Jose-Luis Blanco, Javier Gonzalez, Juan-Antonio Fe...
CVIU
2010
429views more  CVIU 2010»
15 years 4 months ago
Cascade of descriptors to detect and track objects across any network of cameras
Most multi-camera systems assume a well structured environment to detect and track objects across cameras. Cameras need to be fixed and calibrated, or only objects within a traini...
Alexandre Alahi, Pierre Vandergheynst, Michel Bier...
BMCBI
2006
100views more  BMCBI 2006»
15 years 4 months ago
Empirical array quality weights in the analysis of microarray data
Background: Assessment of array quality is an essential step in the analysis of data from microarray experiments. Once detected, less reliable arrays are typically excluded or &qu...
Matthew E. Ritchie, Dileepa S. Diyagama, Jody Neil...
CGF
2007
165views more  CGF 2007»
15 years 4 months ago
Ray-Casted BlockMaps for Large Urban Models Visualization
We introduce a GPU-friendly technique that efficiently exploits the highly structured nature of urban environments to ensure rendering quality and interactive performance of city...
Paolo Cignoni, Marco Di Benedetto, Fabio Ganovelli...
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