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ICIP
2006
IEEE
14 years 9 months ago
Light and Fast Statistical Motion Detection Method Based on Ergodic Model
In this paper, we propose a light and fast pixel-based statistical motion detection method based on a background subtraction procedure. The statistical representation of the backg...
Pierre-Marc Jodoin, Max Mignotte, Janusz Konrad
JMM2
2007
148views more  JMM2 2007»
13 years 7 months ago
Object Segmentation Using Background Modelling and Cascaded Change Detection
— The automatic extraction and analysis of visual information is becoming generalised. The first step in this processing chain is usually separating or segmenting the captured v...
Luís Filipe Teixeira, Jaime S. Cardoso, Lu&...

Publication
1851views
15 years 8 months ago
Cerebrovascular Segmentation from TOF Using Stochastic Models
In this paper, we present an automatic statistical approach for extracting 3D blood vessels from time-of-flight (TOF) magnetic resonance angiography (MRA) data. The voxels of the d...
M. Sabry Hassouna, Aly A. Farag, Stephen Hushek, T...
JAIR
1998
198views more  JAIR 1998»
13 years 7 months ago
Probabilistic Inference from Arbitrary Uncertainty using Mixtures of Factorized Generalized Gaussians
This paper presents a general and efficient framework for probabilistic inference and learning from arbitrary uncertain information. It exploits the calculation properties of fini...
Alberto Ruiz, Pedro E. López-de-Teruel, M. ...
ECCV
2004
Springer
14 years 9 months ago
A Unified Algebraic Approach to 2-D and 3-D Motion Segmentation
We present an analytic solution to the problem of estimating multiple 2-D and 3-D motion models from two-view correspondences or optical flow. The key to our approach is to view th...
René Vidal, Yi Ma