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» Discrete Mixture Models for Unsupervised Image Segmentation
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MICCAI
2007
Springer
14 years 8 months ago
Detection of Spatial Activation Patterns as Unsupervised Segmentation of fMRI Data
In functional connectivity analysis, networks of interest are defined based on correlation with the mean time course of a user-selected `seed' region. In this work we propose ...
Polina Golland, Yulia Golland, Rafael Malach
EVOW
2007
Springer
14 years 1 months ago
Unsupervised Evolutionary Segmentation Algorithm Based on Texture Analysis
Abstract. This work describes an evolutionary approach to texture segmentation, a long-standing and important problem in computer vision. The difficulty of the problem can be relat...
Cynthia B. Pérez, Gustavo Olague
ICIP
2003
IEEE
14 years 9 months ago
A Bayesian framework for Gaussian mixture background modeling
Background subtraction is an essential processing component for many video applications. However, its development has largely been application driven and done in ad hoc manners. I...
Dar-Shyang Lee, Jonathan J. Hull, Berna Erol
ACCV
2009
Springer
14 years 2 months ago
Natural Image Segmentation with Adaptive Texture and Boundary Encoding
We present a novel algorithm for unsupervised segmentation of natural images that harnesses the principle of minimum description length (MDL). Our method is based on observations ...
Shankar Rao, Hossein Mobahi, Allen Y. Yang, Shanka...
ICIP
2005
IEEE
14 years 1 months ago
Robust highlight extraction using multi-stream hidden Markov models for baseball video
This paper proposes a robust statistical framework to extract highlights from a baseball broadcast video. We applied multistream Hidden Markov Models (HMMs) to control the weights...
Nguyen Huu Bach, Koichi Shinoda, Sadaoki Furui