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CVPR
2009
IEEE
17 years 1 months ago
Understanding Videos, Constructing Plots - Learning a Visually Grounded Storyline Model from Annotated Videos
Analyzing videos of human activities involves not only recognizing actions (typically based on their appearances), but also determining the story/plot of the video. The storyline...
Abhinav Gupta (University of Maryland), Praveen Sr...
CVPR
2009
IEEE
16 years 1 months ago
Understanding videos, constructing plots learning a visually grounded storyline model from annotated videos
Analyzing videos of human activities involves not only recognizing actions (typically based on their appearances), but also determining the story/plot of the video. The storyline ...
Abhinav Gupta, Praveen Srinivasan, Jianbo Shi, Lar...
CVPR
1999
IEEE
1104views Computer Vision» more  CVPR 1999»
16 years 8 months ago
Geodesic Active Contours for Supervised Texture Segmentation
This paper presents a variational method for supervised texture segmentation, which is based on ideas coming from the curve propagation theory. We assume that a preferable texture...
Nikos Paragios, Rachid Deriche
HUC
2010
Springer
15 years 6 months ago
Bayesian recognition of motion related activities with inertial sensors
This work presents the design and evaluation of an activity recognition system for seven important motion related activities. The only sensor used is an Inertial Measurement Unit ...
Korbinian Frank, Maria Josefa Vera Nadales, Patric...
BMCBI
2006
119views more  BMCBI 2006»
15 years 6 months ago
Hidden Markov Model Variants and their Application
Markov statistical methods may make it possible to develop an unsupervised learning process that can automatically identify genomic structure in prokaryotes in a comprehensive way...
Stephen Winters-Hilt