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» Learning Object Representations Using Sequential Patterns
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NIPS
1997
13 years 9 months ago
Learning Generative Models with the Up-Propagation Algorithm
Up-propagation is an algorithm for inverting and learning neural network generative models. Sensory input is processed by inverting a model that generates patterns from hidden var...
Jong-Hoon Oh, H. Sebastian Seung
IJCAI
2007
13 years 9 months ago
Incremental Learning of Perceptual Categories for Open-Domain Sketch Recognition
Most existing sketch understanding systems require a closed domain to achieve recognition. This paper describes an incremental learning technique for opendomain recognition. Our s...
Andrew M. Lovett, Morteza Dehghani, Kenneth D. For...
CVIU
2010
267views more  CVIU 2010»
13 years 5 months ago
Accelerated hardware video object segmentation: From foreground detection to connected components labelling
This paper demonstrates the use of a single-chip FPGA for the segmentation of moving objects in a video sequence. The system maintains highly accurate background models, and integ...
Kofi Appiah, Andrew Hunter, Patrick Dickinson, Hon...
VISAPP
2008
13 years 9 months ago
Continuous Learning of Simple Visual Concepts Using Incremental Kernel Density Estimation
In this paper we propose a method for continuous learning of simple visual concepts. The method continuously associates words describing observed scenes with automatically extracte...
Danijel Skocaj, Matej Kristan, Ales Leonardis
PRL
2007
100views more  PRL 2007»
13 years 7 months ago
Visible models for interactive pattern recognition
The bottleneck in interactive visual classification is the exchange of information between human and machine. We introduce the concept of the visible model, which is an ion of an ...
Jie Zou, George Nagy