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» A Framework for Multiple-Instance Learning
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ICPR
2008
IEEE
16 years 1 months ago
On-line boosted cascade for object detection
On-line boosting is a recent advancement in the field of machine learning that has opened a new spectrum of possibilities in many diverse fields. With respect to a static strong...
Ingrid Visentini, Lauro Snidaro, Gian Luca Foresti
ICRA
2008
IEEE
129views Robotics» more  ICRA 2008»
16 years 1 months ago
Communication-aware motion planning in fading environments
— In this paper we create a framework to model and characterize the impact of time-varying fading communication links on the performance of a mobile sensor network. We propose co...
Yasamin Mostofi
IROS
2008
IEEE
211views Robotics» more  IROS 2008»
16 years 1 months ago
GP-BayesFilters: Bayesian filtering using Gaussian process prediction and observation models
Abstract— Bayesian filtering is a general framework for recursively estimating the state of a dynamical system. The most common instantiations of Bayes filters are Kalman filt...
Jonathan Ko, Dieter Fox
VL
2008
IEEE
16 years 1 months ago
Mashing up visual languages and web mash-ups
Research on web mashups and visual languages share an interest in human-centered computing. Both research communities are concerned with supporting programming by everyday, techni...
M. Cameron Jones, Elizabeth F. Churchill, Michael ...
AVSS
2007
IEEE
16 years 1 months ago
Compact representation and probabilistic classification of human actions in videos
This paper addresses the problem of classifying human actions in a video sequence. A representation eigenspace approach based on the PCA algorithm is used to train the classifier...
Carlo Colombo, Dario Comanducci, Alberto Del Bimbo