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» Discrete optimization in computer vision
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PAMI
2010
215views more  PAMI 2010»
13 years 8 months ago
Fusion Moves for Markov Random Field Optimization
—The efficient application of graph cuts to Markov Random Fields (MRFs) with multiple discrete or continuous labels remains an open question. In this paper, we demonstrate one p...
Victor S. Lempitsky, Carsten Rother, Stefan Roth, ...
EMMCVPR
2001
Springer
14 years 2 months ago
An Experimental Comparison of Min-cut/Max-flow Algorithms for Energy Minimization in Vision
After [15, 31, 19, 8, 25, 5] minimum cut/maximum flow algorithms on graphs emerged as an increasingly useful tool for exact or approximate energy minimization in low-level vision...
Yuri Boykov, Vladimir Kolmogorov
CVPR
2011
IEEE
13 years 6 months ago
Saliency Estimation Using a Non-Parametric Low-Level Vision Model
Many successful models for predicting attention in a scene involve three main steps: convolution with a set of filters, a center-surround mechanism and spatial pooling to constru...
Naila Murray, Maria Vanrell, Xavier Otazu, C. Alej...

Publication
266views
13 years 3 months ago
NeuFlow: A Runtime Reconfigurable Dataflow Processor for Vision
In this paper we present a scalable dataflow hard- ware architecture optimized for the computation of general- purpose vision algorithms—neuFlow—and a dataflow compiler—luaFl...
C. Farabet, B. Martini, B. Corda, P. Akselrod, E. ...
ICCV
2007
IEEE
14 years 12 months ago
Capacity Scaling for Graph Cuts in Vision
Capacity scaling is a hierarchical approach to graph representation that can improve theoretical complexity and practical efficiency of max-flow/min-cut algorithms. Introduced by ...
Olivier Juan, Yuri Boykov