Abstract
Research progress in machine vision has been very significant in recent years. Robust face detection and identification algorithms are already readily available to consumers, and modern computer vision algorithms for generic object recognition are now coping with the richness and complexity of natural visual scenes. Unlike early vision models of object recognition that emphasized the role of figure-ground segmentation and spatial information between parts, recent successful approaches are based on the computation of loose collections of image features without prior segmentation or any explicit encoding of spatial relations. While these models remain simplistic models of visual processing, they suggest that, in principle, bottom-up activation of a loose collection of image features could support the rapid recognition of natural object categories and provide an initial coarse visual representation before more complex visual routines and attentional mechanisms take place. Focusing on biologically plausible computational models of (bottom-up) pre-attentive visual recognition, we review some of the key visual features that have been described in the literature. We discuss the consistency of these feature-based representations with classical theories from visual psychology and test their ability to account for human performance on a rapid object categorization task.
Highlights
Object recognition is concerned with determining the identity of an object in our visual field of view
Shape and object category information has been traditionally associated with processing in the ventral stream of the visual cortex
The princeps discovery was made by Hubel and Wiesel (1959, 1968), who first reported the existence of bar and edge detectors in the primary visual cortices of the mammalian brain. They further proposed the first cortical model of visual processing thereby suggesting that such selectivity for oriented bars could be achieved via selective pooling mechanisms from the spatial arrangements of center-surround ganglion cells in the Lateral Geniculate Nucleus (LGN; Hubel and Wiesel, 1962)
Summary
Object recognition is concerned with determining the identity of an object in our visual field of view. The princeps discovery was made by Hubel and Wiesel (1959, 1968), who first reported the existence of bar and edge detectors in the primary visual cortices of the mammalian brain They further proposed the first cortical model of visual processing thereby suggesting that such selectivity for oriented bars could be achieved via selective pooling mechanisms from the spatial arrangements of center-surround ganglion cells in the Lateral Geniculate Nucleus (LGN; Hubel and Wiesel, 1962). These ideas later formed the basis of Marr’s primal sketch in his prominent computational theory of visual processing (Marr, 1982). Our understanding of subsequent stages of processing along the visual hierarchy remains a matter of debate
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