Self-organization and associative memory: 3rd edition
Self-organization and associative memory: 3rd edition
Representation of similarity in three-dimensional object discrimination
Neural Computation
Visual learning and recognition of 3-D objects from appearance
International Journal of Computer Vision
The nature of statistical learning theory
The nature of statistical learning theory
Visual space: mathematics, engineering, and science
Computer Vision and Image Understanding
Shape quantization and recognition with randomized trees
Neural Computation
Representation and recognition in vision
Representation and recognition in vision
Learning to recognize three-dimensional objects
Neural Computation
Learning innate face preferences
Neural Computation
Learning to see: genetic and environmental influences on visual development
Learning to see: genetic and environmental influences on visual development
Modeling the adaptive visual system: a survey of principled approaches
Neural Networks - Special issue: Neuroinformatics
Contour integration and segmentation with self-organized lateral connections
Biological Cybernetics
Perceptual grouping and the interactions between visual cortical areas
Neural Networks - 2004 Special issue Vision and brain
Recognition invariance obtained by extended and invariant features
Neural Networks - 2004 Special issue Vision and brain
Tilt Aftereffects in a Self-Organizing Model of the Primary Visual Cortex
Neural Computation
The emergence of visual object recognition
ICANN'05 Proceedings of the 15th international conference on Artificial Neural Networks: biological Inspirations - Volume Part I
A neural model of human object recognition development
BVAI'05 Proceedings of the First international conference on Brain, Vision, and Artificial Intelligence
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Object recognition is one of the most important functions of the human visual system, yet one of the least understood, this despite the fact that vision is certainly the most studied function of the brain. We understand relatively well how several processes in the cortical visual areas that support recognition capabilities take place, such as orientation discrimination and color constancy. This paper proposes a model of the development of object recognition capability, based on two main theoretical principles. The first is that recognition does not imply any sort of geometrical reconstruction, it is instead fully driven by the two dimensional view captured by the retina. The second assumption is that all the processing functions involved in recognition are not genetically determined or hardwired in neural circuits, but are the result of interactions between epigenetic influences and basic neural plasticity mechanisms. The model is organized in modules roughly related to the main visual biological areas, and is implemented mainly using the LISSOM architecture, a recent neural self-organizing map model that simulates the effects of intercortical lateral connections. This paper shows how recognition capabilities, similar to those found in brain ventral visual areas, can develop spontaneously by exposure to natural images in an artificial cortical model.