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- W2000440123 abstract "The problem of how we recognize objects independently of viewpoint, size and conditions of illumination is a puzzle that has been much studied in visual neuroscience. However, until recently, computational approaches to this problem have been comparatively sparse and have operated on the basis of training a network on a limited set of relatively simple inputs (e.g. 1 Poggio T. Edelman S. A network that learns to recognize 3D objects. Nature. 1990; 343: 263-266 Crossref PubMed Scopus (651) Google Scholar ). Such models are ‘view-based’ in the sense that they perform object recognition on the basis of a comparison of a new input with previously stored views of objects that most closely match the novel input. Now, Riesenhuber and Poggio 2 Riesenhuber M. Poggio T. Hierarchical models of object recognition in cortex. Nat. Neurosci. 1999; 2: 1019-1025 Crossref PubMed Scopus (2263) Google Scholar have developed a model that operates on the basis of a non-linear maximum operation (or ‘MAX’), which more closely reflects what the brain must do when faced with multiple stimuli in the visual input. The idea of MAX is that it pools features by giving greater weight to the most active (or best matching) input, rather than performing a linear summation, with all inputs given equal weight. Such a mechanism is consistent with neurophysiological data from the temporal cortical visual pathway, which is thought to have a key role in object recognition. Running simulations with the model, the authors found that it did indeed achieve recognition of complex multi-element inputs, displaying the fundamental requirements of feature selectivity and invariance to viewpoint. Significantly, the model is also hierarchical in that it incorporates a hierarchy of increasingly complex features, and at each successive stage, the invariance among features increases. This is a natural extension of the structural model of the visual processing, from the simple cells of striate cortex to the object-selective properties of inferotemporal cortex. This model may represent the most complete and predictive implementation of visual object recognition, and promises further advances in the quest to solve the task of object constancy in perception." @default.
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- W2000440123 date "2000-01-01" @default.
- W2000440123 modified "2023-09-25" @default.
- W2000440123 title "A novel view of recognition" @default.
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- W2000440123 doi "https://doi.org/10.1016/s1364-6613(99)01432-1" @default.
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