Neural routing circuits for forming invariant representations of visual objects
Neural routing circuits for forming invariant representations of visual objects
Face Recognition by Elastic Bunch Graph Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
The FERET Evaluation Methodology for Face-Recognition Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Map-Seeking Circuits in Visual Cognition: A Computational Mechanism for Biological and Machine Vision
Distortion Invariant Object Recognition in the Dynamic Link Architecture
IEEE Transactions on Computers
Slow feature analysis: unsupervised learning of invariances
Neural Computation
Multilinear Analysis of Image Ensembles: TensorFaces
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Maplets for correspondence-based object recognition
Neural Networks - 2004 Special issue: New developments in self-organizing systems
Bilinear Sparse Coding for Invariant Vision
Neural Computation
Separating Style and Content with Bilinear Models
Neural Computation
Analysis of Constrained Optimization Variants of the Map-Seeking Circuit Algorithm
Journal of Mathematical Imaging and Vision
Convergence of Map Seeking Circuits
Journal of Mathematical Imaging and Vision
Rapid convergence to feature layer correspondences
Neural Computation
State-dependent computation using coupled recurrent networks
Neural Computation
A parallel computation that assigns canonical object-based frames of reference
IJCAI'81 Proceedings of the 7th international joint conference on Artificial intelligence - Volume 2
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Self-organization of steerable topographic mappings as basis for translation invariance
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part II
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Perceptual systems often have to disentangle different factors from mixed observations. If each factor is represented by a set of variables, each standing for a discrete value of the factor, the factor values underlying an observation can be extracted by a winner-take-all (WTA) mechanism over the direct product of the factors. Search in the product space, however, is expensive. It is computationally attractive to work on the marginal factors. In this letter we study the dynamics of a multifactor system modeled by a number of interacting WTA dynamics, one for each factor. We give theoretical results on the stable fixed points of this system and show experimental results on invariant object recognition.