Models of object recognition
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Cited by (16)
Recognition of rotated objects and cognitive offloading in dogs
2022, iScienceCitation Excerpt :Several alternative models have been proposed to explain how the human visual system recognizes whether two objects seen from different points of view are the same or not. For example, some researchers proposed that object recognition might be based on more than just one processing mechanism (the “multiple routes” hypothesis, Vanrie et al. (2001)) or that it might be based on interpolation between the limited views of an object stored in memory (Edelman and Poggio, 1991; Riesenhuber and Poggio, 2000). The strategies used by non-human species to recognize rotated objects are debated too, as discussed below.
Mechanosensation and Adaptive Motor Control in Insects
2016, Current BiologyCitation Excerpt :One way to identify compelling scientific problems is to ask which features of biological systems are most difficult to replicate in artificial systems. Thirty years ago, a reasonable answer might have been the problem of visual object recognition, which motivated many neurophysiological and computational studies of the primate visual cortex [195]. However, recent advances in artificial neural networks have made it possible for a computer to automatically classify natural images with accuracies that match, and sometimes surpass, human performance [196].
View-invariant object category learning, recognition, and search: How spatial and object attention are coordinated using surface-based attentional shrouds
2009, Cognitive PsychologyCitation Excerpt :One problem with such theories is that one can easily find objects that do not seem to conform to any pre-determined set of parts. According to “view-based” object recognition theories, objects are represented as collections of view-specific representations, leading to recognition performance that is a function of previously seen object views (Bulthoff & Edelman, 1992; Edelman & Poggio, 1991; Tarr & Bulthoff, 1995, 1998; Tarr, Williams, Hayward, & Gauthier, 1998). As noted above, ARTSCAN employs a view-based object learning and recognition strategy.
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1993, Current Opinion in NeurobiologyActive vision: On the relevance of a bio-inspired approach for object detection
2020, Bioinspiration and Biomimetics