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» Clustering Moving Objects via Medoid Clusterings
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IROS
2006
IEEE
155views Robotics» more  IROS 2006»
14 years 1 months ago
Multiple Hypothesis Tracking of Clusters of People
— Mobile robots operating in populated environments typically can improve their service and navigation behavior when they know where people are in their vicinity and in which dir...
Manuel Mucientes, Wolfram Burgard
ICTAI
2006
IEEE
14 years 1 months ago
On the Relationships between Clustering and Spatial Co-location Pattern Mining
The goal of spatial co-location pattern mining is to find subsets of spatial features frequently located together in spatial proximity. Example co-location patterns include servi...
Yan Huang, Pusheng Zhang
IWQOS
2004
Springer
14 years 1 months ago
Robust communications for sensor networks in hostile environments
— Clustering sensor nodes increases the scalability and energy efficiency of communications among them. In hostile environments, unexpected failures or attacks on cluster heads ...
Ossama Younis, Sonia Fahmy, Paolo Santi
PVLDB
2008
82views more  PVLDB 2008»
13 years 7 months ago
TraClass: trajectory classification using hierarchical region-based and trajectory-based clustering
Trajectory classification, i.e., model construction for predicting the class labels of moving objects based on their trajectories and other features, has many important, real-worl...
Jae-Gil Lee, Jiawei Han, Xiaolei Li, Hector Gonzal...
TCSV
2010
13 years 2 months ago
Video Foreground Detection Based on Symmetric Alpha-Stable Mixture Models
Background subtraction (BS) is an efficient technique for detecting moving objects in video sequences. A simple BS process involves building a model of the background and extractin...
Harish Bhaskar, Lyudmila Mihaylova, Alin Achim