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Category/Object recognition

Category/Object recognition

Based on our earlier research that investigated the concept of perception as active pattern generation, we aim to combine attention and object recognition in a single interconnected network. We will demonstrate the performance on object recognition in natural scenes and provide a significant step towards the understanding of vision as a constructive process. Learning feedforward and feedback weights will result in model cells which encode with increasing hierarchy larger proportions of the visual field and more complex stimulus properties. Feedback allows to resolve ambiguities and to reveal visual details. This project is funded by the DFG.

A model of the ventral pathway and the frontal eye field for attention and object recognition. From the image, different feature maps are obtained (color and orientation) and each feature at each location is represented with its conspicuity by a population code. Learned feedforward W and feedback weights A connect the areas with each other.

Video "weights_color"

Wiltschut, J., Hamker, F.H. (2009)
Efficient Coding correlates with spatial frequency tuning in a model of V1 receptive field organization.
Visual Neuroscience. 26:21-34.
PDF-document

Funding

EU-Project EYESHOTS (Heterogeneous 3-D Perception across Visual Fragments)
German Research Foundation (DFG HA2630/4-1)

Selected Publications

Antonelli, M., Gibaldi, A., Beuth, F., Duran, A. J., Canessa, A., Chessa, M., Solari, F., del Pobil, P., Hamker, F., Chinellato, E., Sabatini, S. P. (2014)
A hierarchical system for a distributed representation of the peripersonal space of a humanoid robot
IEEE Transactions on Autonomous Mental Development, 10.1109/TAMD.2014.2332875.
PDF-document

Teichmann, M., Wiltschut, J., Hamker, F.H. (2012)
Learning invariance from natural images inspired by observations in the primary visual cortex.
Neural Computation, 24(5): 1271-1296.
PDF-document

Wiltschut, J., Hamker, F.H. (2009)
Efficient Coding correlates with spatial frequency tuning in a model of V1 receptive field organization.
Visual Neuroscience. 26:21-34.
PDF-document

Hamker, F.H., Wiltschut, J. (2007)
Hebbian learning in a model with dynamic rate-coded neurons: an alternative to the generative model approach for learning receptive fields from natural scenes
Network, Computation in Neural Systems, 18: 249-266.
PDF-document

Hamker, F. H. (2005)
The emergence of attention by population-based inference and its role in distributed processing and cognitive control of vision.
Journal for Computer Vision and Image Understanding. Special Issue on Attention and Performance in Computer Vision, 100:64-106.
PDF-document

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