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17 years 5 months ago
Markov Random Field Modeling in Computer Vision
Markov random field (MRF) theory provides a basis for modeling contextual constraints in visual processing and interpretation. It enables us to develop optimal vision algorithms sy...
Stan Z. Li
CVPR
2009
IEEE
17 years 1 months ago
Learning Invariant Features Through Topographic Filter Maps
Several recently-proposed architectures for highperformance object recognition are composed of two main stages: a feature extraction stage that extracts locallyinvariant feature...
Koray Kavukcuoglu, Marc'Aurelio Ranzato, Rob Fergu...
CVPR
2009
IEEE
1390views Computer Vision» more  CVPR 2009»
17 years 1 months ago
Stacks of Convolutional Restricted Boltzmann Machines for Shift-Invariant Feature Learning
In this paper we present a method for learning classspecific features for recognition. Recently a greedy layerwise procedure was proposed to initialize weights of deep belief ne...
Mohammad Norouzi (Simon Fraser University), Mani R...
ICIP
2006
IEEE
16 years 8 months ago
Local Discriminant Embedding with Tensor Representation
We present a subspace learning method, called Local Discriminant Embedding with Tensor representation (LDET), that addresses simultaneously the generalization and data representat...
Jian Xia, Dit-Yan Yeung, Guang Dai
ICIP
2006
IEEE
16 years 8 months ago
Hardware Computation of Moment Functions in a Silicon Retina using Binary Patterns
We present in this paper a method for implementing moment functions in a CMOS retina for shape recognition applications. The method is based on the use of binary patterns and it a...
Olivier Aubreton, Lew Fock Chong Lew Yan Voon, Guy...