Abstract
We present a neural network mechanism allowing for distance-invariant recognition of visual objects. The term distance-invariance refers to the toleration of changes in retinal image size that are due to varying view distances, as opposed to varying real-world object size. We propose a biologically plausible network model, based on the recently demonstrated spike-rate modulations of large numbers of neurons in striate and extra-striate visual cortex by viewing distance. In this context, we introduce the concept of distance complex cells. Our model demonstrates the capability of distance-invariant object recognition, and of resolving conflicts that other approaches to size-invariant recognition do not address.
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© 2002 Springer-Verlag Berlin Heidelberg
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Kupper, R., Eckhorn, R. (2002). A Neural Network Model Generating Invariance for Visual Distance. In: Dorronsoro, J.R. (eds) Artificial Neural Networks — ICANN 2002. ICANN 2002. Lecture Notes in Computer Science, vol 2415. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-46084-5_16
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DOI: https://doi.org/10.1007/3-540-46084-5_16
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