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» Image Distance Using Hidden Markov Models
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JMLR
2012
13 years 4 months ago
Deep Learning Made Easier by Linear Transformations in Perceptrons
We transform the outputs of each hidden neuron in a multi-layer perceptron network to have zero output and zero slope on average, and use separate shortcut connections to model th...
Tapani Raiko, Harri Valpola, Yann LeCun
96
Voted
ICPR
2004
IEEE
16 years 3 months ago
Multi-Resolution Template Kernels
Domains in which shapes of objects change rapidly and significantly are a challenge for existing representation techniques: sport is a good example of this. We present a texture-b...
Chris J. Needham, Roger D. Boyle
DAGM
2008
Springer
15 years 4 months ago
MAP-Inference for Highly-Connected Graphs with DC-Programming
The design of inference algorithms for discrete-valued Markov Random Fields constitutes an ongoing research topic in computer vision. Large state-spaces, none-submodular energy-fun...
Jörg H. Kappes, Christoph Schnörr
105
Voted
CVPR
2008
IEEE
15 years 8 months ago
Modeling the structure of multivariate manifolds: Shape maps
We propose a shape population metric that reflects the interdependencies between points observed in a set of examples. It provides a notion of topology for shape and appearance m...
Georg Langs, Nikos Paragios
ICIP
2006
IEEE
16 years 3 months ago
A Variational Approach for Shapes Registration Using Vector Maps
The main focus of this paper is the shape representation and registration using vector level set functions. This powerful representation is more flexible than conventional signed ...
Hossam E. Abd El Munim, Aly A. Farag