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» On the Power of Local Orientations
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106
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COMPUTING
2007
97views more  COMPUTING 2007»
15 years 1 months ago
Multiresolution morphing for planar curves
We present a multiresolution morphing algorithm using “as-rigid-as-possible” shape interpolation combinedwithanangle-lengthbasedmultiresolutiondecompositionofsimple2Dpiecewise...
Stefanie Hahmann, Georges-Pierre Bonneau, Baptiste...
94
Voted
EG
2007
92views more  EG 2007»
15 years 1 months ago
Service innovation for e-government: a broadband-based example
: This paper investigates the relationship between broadband and service innovation in an e-government context. We ask, what characterizes successful broadband based service innova...
Bendik Bygstad, Gjermund Lanestedt, Jyoti Choudrie
ML
2006
ACM
142views Machine Learning» more  ML 2006»
15 years 1 months ago
The max-min hill-climbing Bayesian network structure learning algorithm
We present a new algorithm for Bayesian network structure learning, called Max-Min Hill-Climbing (MMHC). The algorithm combines ideas from local learning, constraint-based, and sea...
Ioannis Tsamardinos, Laura E. Brown, Constantin F....
PAMI
2006
125views more  PAMI 2006»
15 years 1 months ago
Channel Smoothing: Efficient Robust Smoothing of Low-Level Signal Features
In this paper, we present a new and efficient method to implement robust smoothing of low-level signal features: B-spline channel smoothing. This method consists of three steps: en...
Michael Felsberg, Per-Erik Forssén, Hanno S...
84
Voted
IJON
2007
117views more  IJON 2007»
15 years 29 days ago
Learning sensory representations with intrinsic plasticity
Intrinsic plasticity (IP) refers to a neuron’s ability to regulate its firing activity by adapting its intrinsic excitability. Previously, we showed that model neurons combinin...
Nicholas Butko, Jochen Triesch