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» Sparse Signal Recovery Using Markov Random Fields
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CORR
2010
Springer
225views Education» more  CORR 2010»
13 years 7 months ago
Sensing Matrix Optimization for Block-Sparse Decoding
Recent work has demonstrated that using a carefully designed sensing matrix rather than a random one, can improve the performance of compressed sensing. In particular, a welldesign...
Kevin Rosenblum, Lihi Zelnik-Manor, Yonina C. Elda...
MM
2006
ACM
221views Multimedia» more  MM 2006»
14 years 1 months ago
Video object segmentation by motion-based sequential feature clustering
Segmentation of video foreground objects from background has many important applications, such as human computer interaction, video compression, multimedia content editing and man...
Mei Han, Wei Xu, Yihong Gong
ICPR
2006
IEEE
14 years 8 months ago
Robust Image Registration Based on Markov-Gibbs Appearance Model
A new approach to align an image of a textured object with a given prototype is proposed. Visual appearance of the images, after equalizing their signals, is modeled with a Markov...
Alaa E. Abdel-Hakim, Aly A. Farag, Ayman El-Baz, G...
ICA
2004
Springer
14 years 1 months ago
Postnonlinear Overcomplete Blind Source Separation Using Sparse Sources
Abstract. We present an approach for blindly decomposing an observed random vector x into f(As) where f is a diagonal function i.e. f = f1 × . . . × fm with one-dimensional funct...
Fabian J. Theis, Shun-ichi Amari
ECCV
2010
Springer
13 years 8 months ago
Semantic Segmentation of Urban Scenes Using Dense Depth Maps
In this paper we present a framework for semantic scene parsing and object recognition based on dense depth maps. Five viewindependent 3D features that vary with object class are e...
Chenxi Zhang, Liang Wang, Ruigang Yang