How Does Our Visual System Achieve Shift and Size Invariance

By Laurenz Wiskott. 2004

60 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Neuroscience, Cognitive Science & Psychology

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Citations in the corpus

Learning Generative Models with Visual Attention (2013, background): “Dynamic routing has been hypothesized as providing a way for achieving shift and size invariance in the visual cortex [14, 15].”
Neural networks and neuroscience-inspired computer vision. (2014, background): “The analysis of time-varying signals for object perception has long been the subject of convergent interest across computational neuroscience and computer vision [76–86], but we argue that there is a good deal of room tomake these connections stronger going forward.”
Bilinear Sparse Coding for Invariant Vision (2005, background): “As discussed by Wiskott (2004), the approaches can be divided into two rough classes: (1) those that explicitly deal with different scales and locations by means of normalizing a perceived image to a canonical view and (2) those that find simple localized features that become transformation invarian”
The Packet Switching Brain (2011, unknown): “Dynamic routing is a central feature of one prominent model of early visual processing (see Wiskott, 2006, for an overview).”
Deep Learning of Invariant Spatio-Temporal Features from Video (2010, background): “1(V1), visual area 2 (V2), visual area 4 (V4), and inferotemporal area (IT) [52]1.”
Deep Hierarchies in the Primate Visual Cortex: What Can We Learn for Computer Vision? (2013, background): “Early on does the visual system try to make responses invariant to frequently occurring but irrelevant variations [162].”